Update: Google has finally released the lists. I guess the “transparency” continues.

In “Google Algorithm Changes For June: What’s On The List?” I pointed out that Google, by the end of July, had still not provided its monthly list of algorithm changes for June. It’s now August 9, and they still have not provided the list for June or July.

Are you interested in seeing these lists or do you find them irrelevant? Share your thoughts here.

Since Google started sharing these lists, the company has been posting them to its Inside Search blog. The last post to that blog, as of the time of this writing, is still “Make your mark on Google with Handwrite for Mobile and Tablet Search” from July 26. I was starting to wonder if they were simply no longer using this blog.

Google announced at the end of last week that it would be shutting down a number of its blogs. Google has over 150 of them, and has decided that it would be better to consolidate the information into fewer blogs, while shutting down the ones that are either updated infrequently or are redundant with other blogs. Google does do a lot of cross-posting.

That does not, however, appear to be the case with the Inside Search blog. Today, Google announced on its Google Finance blog that it is shutting down that blog, and that updates that would have otherwise appeared there will start appearing on the Inside Search blog. The Inside Search blog lives on.

But where are those lists?

Last November, Google started the tradition with this post, looking at some changes it had recently implemented. In December, Google announced that it would just make this a monthly series.

“We know people care about how search works, so we always want to push the envelope when it comes to transparency,” Google engineering director Scott Huffman wrote at the time.

“We’ve been wracking our brains trying to think about how to make search even more transparent,” he wrote. “The good news is that we make roughly 500 improvements in a given year, so there’s always more to share. With this blog series, we’ll be highlighting many of the subtler algorithmic and visible feature changes we make.”

That’s a lot of changes Google makes each year, and I doubt there haven’t been any made over the last two months that Google hasn’t publicized. So what happened to the transparency?

Huffman noted that the changes that appear in these lists aren’t necessarily big enough to warrant blog posts of their own. In other words, the changes are typically not as Earth shattering as the Panda update or the Penguin update. The lists have, however, given us insight into the kinds of improvements Google is looking to make on an ongoing basis. For example, month to month, we can usually see Google tweaking how it deals with freshness of content.

Here’s a list of all of the things that have appeared on these lists since Google been releasing them (in order, since November). It’s a huge list, so feel free to skip past it, but that’s kind of the point. If Google stop releasing these lists, that seems like a whole lot less transparency.

• Cross-language information retrieval updates: For queries in languages where limited web content is available (Afrikaans, Malay, Slovak, Swahili, Hindi, Norwegian, Serbian, Catalan, Maltese, Macedonian, Albanian, Slovenian, Welsh, Icelandic), we will now translate relevant English web pages and display the translated titles directly below the English titles in the search results. This feature was available previously in Korean, but only at the bottom of the page. Clicking on the translated titles will take you to pages translated from English into the query language.
• Snippets with more page content and less header/menu content: This change helps us choose more relevant text to use in snippets. As we improve our understanding of web page structure, we are now more likely to pick text from the actual page content, and less likely to use text that is part of a header or menu.
• Better page titles in search results by de-duplicating boilerplate anchors: We look at a number of signals when generating a page’s title. One signal is the anchor text in links pointing to the page. We found that boilerplate links with duplicated anchor text are not as relevant, so we are putting less emphasis on these. The result is more relevant titles that are specific to the page’s content.
• Length-based autocomplete predictions in Russian: This improvement reduces the number of long, sometimes arbitrary query predictions in Russian. We will not make predictions that are very long in comparison either to the partial query or to the other predictions for that partial query. This is already our practice in English.
• Extending application rich snippets: We recently announced rich snippets for applications. This enables people who are searching for software applications to see details, like cost and user reviews, within their search results. This change extends the coverage of application rich snippets, so they will be available more often.
• Retiring a signal in Image search: As the web evolves, we often revisit signals that we launched in the past that no longer appear to have a significant impact. In this case, we decided to retire a signal in Image Search related to images that had references from multiple documents on the web.
• Fresher, more recent results: As we announced just over a week ago, we’ve made a significant improvement to how we rank fresh content. This change impacts roughly 35 percent of total searches (around 6-10% of search results to a noticeable degree) and better determines the appropriate level of freshness for a given query.
• Refining official page detection: We try hard to give our users the most relevant and authoritative results. With this change, we adjusted how we attempt to determine which pages are official. This will tend to rank official websites even higher in our ranking.
• Improvements to date-restricted queries: We changed how we handle result freshness for queries where a user has chosen a specific date range. This helps ensure that users get the results that are most relevant for the date range that they specify.
• Prediction fix for IME queries: This change improves how Autocomplete handles IME queries (queries which contain non-Latin characters). Autocomplete was previously storing the intermediate keystrokes needed to type each character, which would sometimes result in gibberish predictions for Hebrew, Russian and Arabic.

• Image Search landing page quality signals. [launch codename “simple”] This is an improvement that analyzes various landing page signals for Image Search. We want to make sure that not only are we showing you the most relevant images, but we are also linking to the highest quality source pages.
• More relevant sitelinks. [launch codename “concepts”, project codename “Megasitelinks”] We improved our algorithm for picking sitelinks. The result is more relevant sitelinks; for example, we may show sitelinks specific to your metropolitan region, which you can control with your location setting.
• Soft 404 Detection. Web servers generally return the 404 status code when someone requests a page that doesn’t exist. However, some sites are configured to return other status codes, even though the page content might explain that the page was not found. We call these soft 404s (or “crypto” 404s) and they can be problematic for search engines because we aren’t sure if we should ignore the pages. This change is an improvement to how we detect soft 404s, especially in Russian, German and Spanish. For all you webmasters out there, the best practice is still to always use the correct response code.
• More accurate country-restricted searches. [launch codename “greencr”] On domains other than .com, users have the option to see only results from their particular country. This is a new algorithm that uses several signals to better determine where web documents are from, improving the accuracy of this feature.
• More rich snippets. We improved our process for detecting sites that qualify for shopping, recipe and review rich snippets. As a result, you should start seeing more sites with rich snippets in search results.
• Better infrastructure for autocomplete. This is an infrastructure change to improve how our autocomplete algorithm handles spelling corrections for query prefixes (the beginning part of a search).
• Better spam detection in Image Search. [launch codename “leaf”] This change improves our spam detection in Image Search by extending algorithms we already use for our main search results.
• Google Instant enhancements for Japanese. For languages that use non-Latin characters, many users use a special IME (Input Method Editor) to enter queries. This change works with browsers that are IME-aware to better handle Japanese queries in Google Instant.
• More accurate byline dates. [launch codename “foby”] We made a few improvements to how we determine what date to associate with a document. As a result, you’ll see more accurate dates annotating search results.
• Live results for NFL and college football. [project codename “Live Results”] We’ve added new live results for NFL.com and ESPN’s NCAA Football results. These results now provide the latest scores, schedules and standings for your favorite football teams.
• Improved dataset for related queries. We are now using an improved dataset on term relationships to find related queries. We sometimes include results for queries that are related to your original search, and this improvement leads to results from more relevant related queries.
• Related query improvements. [launch codename “lyndsy”] Sometimes we fetch results for queries that are related to the original query but have fewer words. We made several changes to our algorithms to make them more conservative and less likely to introduce results without query words.
• Better lyrics results. [launch codename “baschi”, project codename “Contra”] This change improves our result quality for lyrics searches.
• Tweak to +1 button on results page. As part of our continued effort to deliver a beautifully simple user experience across Google products, we’ve made a subtle tweak to how the +1 button appears on the results page. Now the +1 button will only appear when you hover over a result or when the result has already been +1’d.
• Better spell correction in Vietnamese. [project codename “Pho Viet”] We launched a new Vietnamese spelling model. This will help give more accurate spelling predictions for Vietnamese queries.
• Upcoming events at venues. We’ve improved the recently released places panel for event venues. For major venues, we now show up to three upcoming events on the right of the page. Try it for [staples center los angeles] or [paradise rock club boston].
• Improvements to image size signal. [launch codename “matter”] This is an improvement to how we use the size of images as a ranking signal in Image Search. With this change, you’ll tend to see images with larger full-size versions.
• Improved Hebrew synonyms. [launch codename “SweatNovember”, project codename “Synonyms”] This update refines how we handle Hebrew synonyms across multiple languages. Context matters a lot for translation, so this change prevents us from using translated synonyms that are not actually relevant to the query context.
• Safer searching. [launch codename “Hoengg”, project codename “SafeSearch”] We updated our SafeSearch tool to provide better filtering for certain queries when strict SafeSearch is enabled.
• Encrypted search available on new regional domains. Google now offers encrypted search by default on google.com for signed-in users, but it’s not the default on our other regional domains (eg: google.fr for France). Now users in the UK, Germany and France can opt in to encrypted search by navigating directly to an SSL version of Google Search on their respective regional domains: https://www.google.co.uk, https://www.google.de andhttps://www.google.fr.
• Faster mobile browsing. [launch codename “old possum”, project codename “Skip Redirect”] Many websites redirect smartphone users to another page that is optimized for smartphone browsers. This change uses the final smartphone destination url in our mobile search results, so you can bypass all the redirects and load the target page faster.

• Fresher results. [launch codename “nftc”] We made several adjustments to the freshness algorithm that we released in November. These are minor updates to make sure we continue to give you the freshest, most relevant results.
• Faster autocomplete. [launch codename “Snappy Suggest”, project codename “Suggest”] We made improvements to our autocomplete system to deliver your predicted queries much faster.
• Autocomplete spelling corrections. [launch codename “Trivial”, project codename “Suggest”] This is an improvement to the spelling corrections used in autocomplete, making those corrections more consistent with the spelling corrections used in search. This launch targets corrections where the spelling change is very small.
• Better spelling full-page replacement. [launch codenames “Oooni”, “sgap”, project codename “Full-Page Replacement”] When we’re confident in a spelling correction we automatically show results for the corrected query and let you know we’re “Showing results for [cheetah]” (rather than, say, “cheettah”). We made a couple of changes to improve the accuracy of this feature.
• Better spelling corrections for rare queries. This change improves one of the models that we use to make spelling corrections. The result is more accurate spell corrections for a number of rare queries.
• Improve detection of recurrent event pages. [launch codename “neseda”] We made several improvements to how we determine the date of a document. As a result, you’ll see fresher, more timely results, particularly for pages discussing recurring events.
• High-quality sites algorithm improvements. [launch codenames “PPtl” and “Stitch”, project codename “Panda”] In 2011, we launched the Panda algorithm change, targeted at finding more high-quality sites. We improved how Panda interacts with our indexing and ranking systems, making it more integrated into our pipelines. We also released a minor update to refresh the data for Panda.
• Cross-language refinements. [launch codename Xiangfan] Previously, we only generated related searches based on the display language. With this change, we also attempt to auto-detect the language of the original query to generate related search queries. Now, a user typing a query in French might see French query refinements, even if her language is set to English.
• English on Google Saudi Arabia. Users in Saudi Arabia can now more easily choose an English interface to search on google.com.sa.
• Improved scrolling for Image Search. Previously when you scrolled in Image Search, only the image results would move while the top and side menus were pinned in place. We changed the scrolling behavior to make it consistent with our main search results and the other search modes, where scrolling moves the entire page.
• Improved image search quality. [launch codename “endearo”, project codename “Image Search”] This is a small improvement to our image search ranking algorithm. In particular, this change helps images with high-quality landing pages rank higher in our image search results.
• More relevant related searches. Sometimes at the bottom of the screen you’ll see a section called “Searches related to” with other queries you may want to try. With this change, we’ve updated the model for generating related searches, resulting in more useful query refinements.
• Blending of news results. [launch codename “final-destination”, project codename “Universal Search”] We improved our algorithm that decides which queries should show news results, making it more responsive to realtime trends. We also made an adjustment to how we blend news results in Universal Search. Both of these changes help news articles appear in your search results when they are relevant.
• Automatically disable Google Instant based on computer speed. [project codename “Psychic Search”] Google Instant has long had the ability to automatically turn itself off if you’re on a slow internet connection. Now Instant can also turn itself off if your computer is slow. If Instant gets automatically disabled, we continue to check your computer speed and will re-enable Instant if your performance improves. We’ve also tweaked search preferencesso you can always have Instant on or off, or have it change automatically.

• More coverage for related searches. [launch codename “Fuzhou”] This launch brings in a new data source to help generate the “Searches related to” section, increasing coverage significantly so the feature will appear for more queries. This section contains search queries that can help you refine what you’re searching for.
• Tweak to categorizer for expanded sitelinks. [launch codename “Snippy”, project codename “Megasitelinks”] This improvement adjusts a signal we use to try and identify duplicate snippets. We were applying a categorizer that wasn’t performing well for our expanded sitelinks, so we’ve stopped applying the categorizer in those cases. The result is more relevant sitelinks.
• Less duplication in expanded sitelinks. [launch codename “thanksgiving”, project codename “Megasitelinks”] We’ve adjusted signals to reduce duplication in the snippets forexpanded sitelinks. Now we generate relevant snippets based more on the page content and less on the query.
• More consistent thumbnail sizes on results page. We’ve adjusted the thumbnail size for most image content appearing on the results page, providing a more consistent experience across result types, and also across mobile and tablet. The new sizes apply to rich snippet results for recipes and applications, movie posters, shopping results, book results, news results and more.
• More locally relevant predictions in YouTube. [project codename “Suggest”] We’ve improved the ranking for predictions in YouTube to provide more locally relevant queries. For example, for the query [lady gaga in ] performed on the US version of YouTube, we might predict [lady gaga in times square], but for the same search performed on the Indian version of YouTube, we might predict [lady gaga in India].
• More accurate detection of official pages. [launch codename “WRE”] We’ve made an adjustment to how we detect official pages to make more accurate identifications. The result is that many pages that were previously misidentified as official will no longer be.
• Refreshed per-URL country information. [Launch codename “longdew”, project codename “country-id data refresh”] We updated the country associations for URLs to use more recent data.
• Expand the size of our images index in Universal Search. [launch codename “terra”, project codename “Images Universal”] We launched a change to expand the corpus of results for which we show images in Universal Search. This is especially helpful to give more relevant images on a larger set of searches.
• Minor tuning of autocomplete policy algorithms. [project codename “Suggest”] We have a narrow set of policies for autocomplete for offensive and inappropriate terms. This improvement continues to refine the algorithms we use to implement these policies.
• “Site:” query update [launch codename “Semicolon”, project codename “Dice”] This change improves the ranking for queries using the “site:” operator by increasing the diversity of results.
• Improved detection for SafeSearch in Image Search. [launch codename “Michandro”, project codename “SafeSearch”] This change improves our signals for detecting adult content in Image Search, aligning the signals more closely with the signals we use for our other search results.
• Interval based history tracking for indexing. [project codename “Intervals”] This improvement changes the signals we use in document tracking algorithms.
• Improvements to foreign language synonyms. [launch codename “floating context synonyms”, project codename “Synonyms”] This change applies an improvement we previously launched for English to all other languages. The net impact is that you’ll more often find relevant pages that include synonyms for your query terms.
• Disabling two old fresh query classifiers. [launch codename “Mango”, project codename “Freshness”] As search evolves and new signals and classifiers are applied to rank search results, sometimes old algorithms get outdated. This improvement disables two old classifiers related to query freshness.
• More organized search results for Google Korea. [launch codename “smoothieking”, project codename “Sokoban4”] This significant improvement to search in Korea better organizes the search results into sections for news, blogs and homepages.
• Fresher images. [launch codename “tumeric”] We’ve adjusted our signals for surfacing fresh images. Now we can more often surface fresh images when they appear on the web.
• Update to the Google bar. [project codename “Kennedy”] We continue to iterate in our efforts to deliver a beautifully simple experience across Google products, and as part of that this month we made further adjustments to the Google bar. The biggest change is that we’ve replaced the drop-down Google menu in the November redesign with a consistent and expanded set of links running across the top of the page.
• Adding three new languages to classifier related to error pages. [launch codename “PNI”, project codename “Soft404”] We have signals designed to detect crypto 404 pages (also known as “soft 404s”), pages that return valid text to a browser but the text only contain error messages, such as “Page not found.” It’s rare that a user will be looking for such a page, so it’s important we be able to detect them. This change extends a particular classifier to Portuguese, Dutch and Italian.
• Improvements to travel-related searches. [launch codename “nesehorn”] We’ve made improvements to triggering for a variety of flight-related search queries. These changes improve the user experience for our Flight Search feature with users getting more accurate flight results.
• Data refresh for related searches signal. [launch codename “Chicago”, project codename “Related Search”] One of the many signals we look at to generate the “Searches related to” section is the queries users type in succession. If users very often search for [apple] right after [banana], that’s a sign the two might be related. This update refreshes the model we use to generate these refinements, leading to more relevant queries to try.
• International launch of shopping rich snippets. [project codename “rich snippets”]Shopping rich snippets help you more quickly identify which sites are likely to have the most relevant product for your needs, highlighting product prices, availability, ratings and review counts. This month we expanded shopping rich snippets globally (they were previously only available in the US, Japan and Germany).
• Improvements to Korean spelling. This launch improves spelling corrections when the user performs a Korean query in the wrong keyboard mode (also known as an “IME”, or input method editor). Specifically, this change helps users who mistakenly enter Hangul queries in Latin mode or vice-versa.
• Improvements to freshness. [launch codename “iotfreshweb”, project codename “Freshness”] We’ve applied new signals which help us surface fresh content in our results even more quickly than before.
• Web History in 20 new countries. With Web History, you can browse and search over your search history and webpages you’ve visited. You will also get personalized search results that are more relevant to you, based on what you’ve searched for and which sites you’ve visited in the past. In order to deliver more relevant and personalized search results, we’ve launched Web History in Malaysia, Pakistan, Philippines, Morocco, Belarus, Kazakhstan, Estonia, Kuwait, Iraq, Sri Lanka, Tunisia, Nigeria, Lebanon, Luxembourg, Bosnia and Herzegowina, Azerbaijan, Jamaica, Trinidad and Tobago, Republic of Moldova, and Ghana. Web History is turned on only for people who have a Google Account and previously enabled Web History.
• Improved snippets for video channels. Some search results are links to channels with many different videos, whether on mtv.com, Hulu or YouTube. We’ve had a feature for a while now that displays snippets for these results including direct links to the videos in the channel, and this improvement increases quality and expands coverage of these rich “decorated” snippets. We’ve also made some improvements to our backends used to generate the snippets.
• Improvements to ranking for local search results. [launch codename “Venice”] This improvement improves the triggering of Local Universal results by relying more on the ranking of our main search results as a signal.
• Improvements to English spell correction. [launch codename “Kamehameha”] This change improves spelling correction quality in English, especially for rare queries, by making one of our scoring functions more accurate.
• Improvements to coverage of News Universal. [launch codename “final destination”] We’ve fixed a bug that caused News Universal results not to appear in cases when our testing indicates they’d be very useful.
• Consolidation of signals for spiking topics. [launch codename “news deserving score”, project codename “Freshness”] We use a number of signals to detect when a new topic is spiking in popularity. This change consolidates some of the signals so we can rely on signals we can compute in realtime, rather than signals that need to be processed offline. This eliminates redundancy in our systems and helps to ensure we can continue to detect spiking topics as quickly as possible.
• Better triggering for Turkish weather search feature. [launch codename “hava”] We’ve tuned the signals we use to decide when to present Turkish users with the weather search feature. The result is that we’re able to provide our users with the weather forecast right on the results page with more frequency and accuracy.
• Visual refresh to account settings page. We completed a visual refresh of the account settings page, making the page more consistent with the rest of our constantly evolving design.
• Panda update. This launch refreshes data in the Panda system, making it more accurate and more sensitive to recent changes on the web.
• Link evaluation. We often use characteristics of links to help us figure out the topic of a linked page. We have changed the way in which we evaluate links; in particular, we are turning off a method of link analysis that we used for several years. We often rearchitect or turn off parts of our scoring in order to keep our system maintainable, clean and understandable.
• SafeSearch update. We have updated how we deal with adult content, making it more accurate and robust. Now, irrelevant adult content is less likely to show up for many queries.
• Spam update. In the process of investigating some potential spam, we found and fixed some weaknesses in our spam protections.
• Improved local results. We launched a new system to find results from a user’s city more reliably. Now we’re better able to detect when both queries and documents are local to the user.

• Autocomplete with math symbols. [launch codename “Blackboard”, project codename “Suggest”] When we process queries to return predictions in autocomplete, we generally normalize them to match more relevant predictions in our database. This change incorporates several characters that were previously normalized: “+”, “-”, “*”, “/”, “^”, “(“, “)”, and “=”. This should make it easier to search for popular equations, for example [e = mc2] or [y = mx+b].
• Improvements to handling of symbols for indexing. [launch codename “Deep Maroon”] We generally ignore punctuation symbols in queries. Based on analysis of our query stream, we’ve now started to index the following heavily used symbols: “%”, “$”, “\”, “.”, “@”, “#”, and “+”. We’ll continue to index more symbols as usage warrants.
• Better scoring of news groupings. [launch codename “avenger_2”] News results on Google are organized into groups that are about the same story. We have scoring systems to determine the ordering of these groups for a given query. This subtle change slightly improves our scoring system, leading to better ranking of news clusters.
• Sitelinks data refresh. [launch codename “Saralee-76”] Sitelinks (the links that appear beneath some search results and link deeper into the respective site) are generated in part by an offline process that analyzes site structure and other data to determine the most relevant links to show users. We’ve recently updated the data through our offline process. These updates happen frequently (on the order of weeks).
• Improvements to autocomplete backends, coverage. [launch codename “sovereign”, project codename “Suggest”] We’ve consolidated systems and reduced the number of backend calls required to prepare autocomplete predictions for your query. The result is more efficient CPU usage and more comprehensive predictions.
• Better handling of password changes. Our general approach is that when you change passwords, you’ll be signed out from your account on all machines. This change ensures that changing your password more consistently signs your account out of Search, everywhere.
• Better indexing of profile pages. [launch codename “Prof-2”] This change improves the comprehensiveness of public profile pages in our index from more than two-hundred social sites.
• UI refresh for News Universal. [launch codename “Cosmos Newsy”, project codename “Cosmos”] We’ve refreshed the design of News Universal results by providing more results from the top cluster, unifying the UI treatment of clusters of different sizes, adding a larger font for the top article, adding larger images (from licensed sources), and adding author information.
• Improvements to results for navigational queries. [launch codename “IceMan5”] A “navigational query” is a search where it looks like the user is looking to navigate to a particular website, such as [New York Times] or [wikipedia.org]. While these searches may seem straightforward, there are still challenges to serving the best results. For example, what if the user doesn’t actually know the right URL? What if the URL they’re searching for seems to be a parked domain (with no content)? This change improves results for this kind of search.
• High-quality sites algorithm data update and freshness improvements. [launch codename “mm”, project codename “Panda”] Like many of the changes we make, aspects of our high-quality sites algorithm depend on processing that’s done offline and pushed on a periodic cycle. In the past month, we’ve pushed updated data for “Panda,” as we mentioned in a recent tweet. We’ve also made improvements to keep our database fresher overall.
• Live results for UEFA Champions League and KHL. We’ve added live-updating snippets in our search results for the KHL (Russian Hockey League) and UEFA Champions League, including scores and schedules. Now you can find live results from a variety of sports leagues, including the NFL, NBA, NHL and others.
• Tennis search feature. [launch codename “DoubleFault”] We’ve introduced a new search feature to provide realtime tennis scores at the top of the search results page. Try [maria sharapova] or [sony ericsson open].
• More relevant image search results. [launch codename “Lice”] This change tunes signals we use related to landing page quality for images. This makes it more likely that you’ll find highly relevant images, even if those images are on pages that are lower quality.
• Fresher image predictions in all languages. [launch codename “imagine2”, project codename “Suggest”] We recently rolled out a change to surface more relevant image search predictions in autocomplete in English. This improvement extends the update to all languages.
• SafeSearch algorithm tuning. [launch codenames “Fiorentini”, “SuperDyn”; project codename “SafeSearch”] This month we rolled out a couple of changes to our SafeSearch algorithm. We’ve updated our classifier to make it smarter and more precise, and we’ve found new ways to make adult content less likely to appear when a user isn’t looking for it
• Tweaks to handling of anchor text. [launch codename “PC”] This month we turned off a classifier related to anchor text (the visible text appearing in links). Our experimental data suggested that other methods of anchor processing had greater success, so turning off this component made our scoring cleaner and more robust.
• Simplification to Images Universal codebase. [launch codename “Galactic Center”] We’ve made some improvements to simplify our codebase for Images Universal and to better utilize improvements in our general web ranking to also provide better image results.
• Better application ranking and UI on mobile. When you search for apps on your phone, you’ll now see richer results with app icons, star ratings, prices, and download buttons arranged to fit well on smaller screens. You’ll also see more relevant ranking of mobile applications based on your device platform, for example Android or iOS.
• Improvements to freshness in Video Universal. [launch codename “graphite”, project codename “Freshness”] We’ve improved the freshness of video results to better detect stale videos and return fresh content.
• Fewer undesired synonyms. [project codename “Synonyms”] When you search on Google, we often identify other search terms that might have the same meaning as what you entered in the box (synonyms) and surface results for those terms as well when it might be helpful. This month we tweaked a classifier to prevent unhelpful synonyms from being introduced as content in the results set.
• Better handling of queries with both navigational and local intent. [launch codename “ShieldsUp”] Some queries have both local intent and are very navigational (directed towards a particular website). This change improves the balance of results we show, and helps ensure you’ll find highly relevant navigational results or local results towards the top of the page as appropriate for your query.
• Improvements to freshness. [launch codename “Abacus”, project codename “Freshness”] We launched an improvement to freshness late last year that was very helpful, but it cost significant machine resources. At the time we decided to roll out the change only for news-related traffic. This month we rolled it out for all queries.
• Improvements to processing for detection of site quality. [launch codename “Curlup”] We’ve made some improvements to a longstanding system we have to detect site quality. This improvement allows us to get greater confidence in our classifications.
• Better interpretation and use of anchor text. We’ve improved systems we use to interpret and use anchor text, and determine how relevant a given anchor might be for a given query and website.
• Better local results and sources in Google News. [launch codename “barefoot”, project codename “news search”] We’re deprecating a signal we had to help people find content from their local country, and we’re building similar logic into other signals we use. The result is more locally relevant Google News results and higher quality sources.
• Deprecating signal related to ranking in a news cluster. [launch codename “decaffeination”, project codename “news search”] We’re deprecating a signal that’s no longer improving relevance in Google News. The signal was originally developed to help people find higher quality articles on Google News. (Note: Despite the launch codename, this project has nothing to do with Caffeine, our update to indexing in 2010).
• Fewer “sibling” synonyms. [launch codename “Gemini”, project codename “Synonyms”] One of the main signals we look at to identify synonyms is context. For example, if the word “cat” often appears next to the term “pet” and “furry,” and so does the word “kitten”, our algorithms may guess that “cat” and “kitten” have similar meanings. The problem is that sometimes this method will introduce “synonyms” that actually are different entities in the same category. To continue the example, dogs are also “furry pets” — so sometimes “dog” may be incorrectly introduced as a synonym for “cat”. We’ve been working for some time to appropriately ferret out these “sibling” synonyms, and our latest system is more maintainable, updatable, debuggable, and extensible to other systems.
• Better synonym accuracy and performance. [project codename “Synonyms”] We’ve made further improvements to our synonyms system by eliminating duplicate logic. We’ve also found ways to more accurately identify appropriate synonyms in cases where there are multiple synonym candidates with different contexts.
• Retrieval system tuning. [launch codename “emonga”, project codename “Optionalization”] We’ve improved systems that identify terms in a query which are not necessarily required to retrieve relevant documents. This will make results more faithful to the original query.
• Less aggressive synonyms. [launch codename “zilong”, project codename “Synonyms”] We’ve heard feedback from users that sometimes our algorithms are too aggressive at incorporating search results for other terms. The underlying cause is often our synonym system, which will include results for other terms in many cases. This change makes our synonym system less aggressive in the way it incorporates results for other query terms, putting greater weight on the original user query.
• Update to systems relying on geographic data. [launch codename “Maestro, Maitre”] We have a number of signals that rely on geographic data (similar to the data we surface in Google Earth and Maps). This change updates some of the geographic data we’re using.
• Improvements to name detection. [launch codename “edge”, project codename “NameDetector”] We’ve improved a system for detecting names, particularly for celebrity names.
• Updates to personalization signals. [project codename “PSearch”] This change updates signals used to personalize search results.
• Improvements to Image Search relevance. [launch codename “sib”] We’ve updated signals to better promote reasonably sized images on high-quality landing pages.
• Remove deprecated signal from site relevance signals. [launch codename “Freedom”] We’ve removed a deprecated product-focused signal from a site-understanding algorithm.
• More precise detection of old pages. [launch codename “oldn23″, project codename “Freshness”] This change improves detection of stale pages in our index by relying on more relevant signals. As a result, fewer stale pages are shown to users.
• Tweaks to language detection in autocomplete. [launch codename “Dejavu”, project codename “Suggest”] In general, autocomplete relies on the display language to determine what language predictions to show. For most languages, we also try to detect the user query language by analyzing the script, and this change extends that behavior to Chinese (Simplified and Traditional), Japanese and Korean. The net effect is that when users forget to turn off their IMEs, they’ll still get English predictions if they start typing English terms.
• Improvements in date detection for blog/forum pages. [launch codename “fibyen”, project codename “Dates”] This change improves the algorithm that determines dates for blog and forum pages.
• More predictions in autocomplete by live rewriting of query prefixes. [launch codename “Lombart”, project codename “Suggest”] In this change we’re rewriting partial queries on the fly to retrieve more potential matching predictions for the user query. We use synonyms and other features to get the best overall match. Rewritten prefixes can include term re-orderings, term additions, term removals and more.
• Expanded sitelinks on mobile. We’ve launched our expanded sitelinks feature for mobile browsers, providing better organization and presentation of sitelinks in search results.
• More accurate short answers. [project codename “Porky Pig”] We’ve updated the sources behind our short answers feature to rely on data from Freebase. This improves accuracy and makes it easier to fix bugs.
• Migration of video advanced search backends. We’ve migrated some backends used in video advanced search to our main search infrastructure.
• +1 button in search for more countries and domains. This month we’ve internationalized the +1 button on the search results page to additional languages and domains. The +1 button in search makes it easy to share recommendations with the world right from your search results. As we said in our initial blog post, the beauty of +1’s is their relevance—you get the right recommendations (because they come from people who matter to you), at the right time (when you are actually looking for information about that topic) and in the right format (your search results).
• Local result UI refresh on tablet. We’ve updated the user interface of local results on tablets to make them more compact and easier to scan.

• Categorize paginated documents. [launch codename “Xirtam3”, project codename “CategorizePaginatedDocuments”] Sometimes, search results can be dominated bydocuments from a paginated series. This change helps surface more diverse results in such cases.
• More language-relevant navigational results. [launch codename “Raquel”] For navigational searches when the user types in a web address, such as [bol.com], we generally try to rank that web address at the top. However, this isn’t always the best answer. For example, bol.com is a Dutch page, but many users are actually searching in Portuguese and are looking for the Brazilian email service, http://www.bol.uol.com.br/. This change takes into account language to help return the most relevant navigational results.
• Country identification for webpages. [launch codename “sudoku”] Location is an important signal we use to surface content more relevant to a particular country. For a while we’ve had systems designed to detect when a website, subdomain, or directory is relevant to a set of countries. This change extends the granularity of those systems to the page level for sites that host user generated content, meaning that some pages on a particular site can be considered relevant to France, while others might be considered relevant to Spain.
• Anchors bug fix. [launch codename “Organochloride”, project codename “Anchors”] This change fixed a bug related to our handling of anchors.
• More domain diversity. [launch codename “Horde”, project codename “Domain Crowding”] Sometimes search returns too many results from the same domain. This change helps surface content from a more diverse set of domains.
• More local sites from organizations. [project codename “ImpOrgMap2”] This change makes it more likely you’ll find an organization website from your country (e.g. mexico.cnn.com for Mexico rather than cnn.com).
• Improvements to local navigational searches. [launch codename “onebar-l”] For searches that include location terms, e.g. [dunston mint seattle] or [Vaso Azzurro Restaurant 94043], we are more likely to rank the local navigational homepages in the top position, even in cases where the navigational page does not mention the location.
• Improvements to how search terms are scored in ranking. [launch codename “Bi02sw41”] One of the most fundamental signals used in search is whether and how your search terms appear on the pages you’re searching. This change improves the way those terms are scored.
• Disable salience in snippets. [launch codename “DSS”, project codename “Snippets”] This change updates our system for generating snippets to keep it consistent with other infrastructure improvements. It also simplifies and increases consistency in the snippet generation process.
• More text from the beginning of the page in snippets. [launch codename “solar”, project codename “Snippets”] This change makes it more likely we’ll show text from the beginning of a page in snippets when that text is particularly relevant.
• Smoother ranking changes for fresh results. [launch codename “sep”, project codename “Freshness”] We want to help you find the freshest results, particularly for searches with important new web content, such as breaking news topics. We try to promote content that appears to be fresh. This change applies a more granular classifier, leading to more nuanced changes in ranking based on freshness.
• Improvement in a freshness signal. [launch codename “citron”, project codename “Freshness”] This change is a minor improvement to one of the freshness signals which helps to better identify fresh documents.
• No freshness boost for low-quality content. [launch codename “NoRot”, project codename “Freshness”] We have modified a classifier we use to promote fresh content to exclude fresh content identified as particularly low-quality.
• Tweak to trigger behavior for Instant Previews. This change narrows the trigger area forInstant Previews so that you won’t see a preview until you hover and pause over the icon to the right of each search result. In the past the feature would trigger if you moused into a larger button area.
• Sunrise and sunset search feature internationalization. [project codename “sunrise-i18n”] We’ve internationalized the sunrise and sunset search feature to 33 new languages, so now you can more easily plan an evening jog before dusk or set your alarm clock to watch the sunrise with a friend.
• Improvements to currency conversion search feature in Turkish. [launch codename “kur”, project codename “kur”] We launched improvements to the currency conversion search feature in Turkish. Try searching for [dolar kuru], [euro ne kadar], or [avro kaç para].
• Improvements to news clustering for Serbian. [launch codename “serbian-5”] For news results, we generally try to cluster articles about the same story into groups. This change improves clustering in Serbian by better grouping articles written in Cyrillic and Latin. We also improved our use of “stemming” — a technique that relies on the “stem” or root of a word.
• Better query interpretation. This launch helps us better interpret the likely intention of your search query as suggested by your last few searches.
• News universal results serving improvements. [launch codename “inhale”] This change streamlines the serving of news results on Google by shifting to a more unified system architecture.
• UI improvements for breaking news topics. [launch codename “Smoothie”, project codename “Smoothie”] We’ve improved the user interface for news results when you’re searching for a breaking news topic. You’ll often see a large image thumbnail alongside two fresh news results.
• More comprehensive predictions for local queries. [project codename “Autocomplete”] This change improves the comprehensiveness of autocomplete predictions by expanding coverage for long-tail U.S. local search queries such as addresses or small businesses.
• Improvements to triggering of public data search feature. [launch codename “Plunge_Local”, project codename “DIVE”] This launch improves triggering for the public data search feature, broadening the range of queries that will return helpful population and unemployment data.
• Adding Japanese and Korean to error page classifier. [launch codename “maniac4jars”, project codename “Soft404”] We have signals designed to detect crypto 404 pages (also known as “soft 404s”), pages that return valid text to a browser, but the text only contains error messages, such as “Page not found.” It’s rare that a user will be looking for such a page, so it’s important we be able to detect them. This change extends a particular classifier to Japanese and Korean.
• More efficient generation of alternative titles. [launch codename “HalfMarathon”] We use a variety of signals to generate titles in search results. This change makes the process more efficient, saving tremendous CPU resources without degrading quality.
• More concise and/or informative titles. [launch codename “kebmo”] We look at a number of factors when deciding what to show for the title of a search result. This change means you’ll find more informative titles and/or more concise titles with the same information.
• Fewer bad spell corrections internationally. [launch codename “Potage”, project codename “Spelling”] When you search for [mango tea], we don’t want to show spelling predictions like “Did you mean ‘mint tea’?” We have algorithms designed to prevent these “bad spell corrections” and this change internationalizes one of those algorithms.
• More spelling corrections globally and in more languages. [launch codename “pita”, project codename “Autocomplete”] Sometimes autocomplete will correct your spelling before you’ve finished typing. We’ve been offering advanced spelling corrections in English, and recently we extended the comprehensiveness of this feature to cover more than 60 languages.
• More spell corrections for long queries. [launch codename “caterpillar_new”, project codename “Spelling”] We rolled out a change making it more likely that your query will get a spell correction even if it’s longer than ten terms. You can watch uncut footage of when we decided to launch this from our past blog post.
• More comprehensive triggering of “showing results for” goes international. [launch codename “ifprdym”, project codename “Spelling”] In some cases when you’ve misspelled a search, say [pnumatic], the results you find will actually be results for the corrected query, “pneumatic.” In the past, we haven’t always provided the explicit user interface to say, “Showing results for pneumatic” and the option to “Search instead for pnumatic.” We recently started showing the explicit “Showing results for” interface more often in these cases in English, and now we’re expanding that to new languages.
• “Did you mean” suppression goes international. [launch codename “idymsup”, project codename “Spelling”] Sometimes the “Did you mean?” spelling feature predicts spelling corrections that are accurate, but wouldn’t actually be helpful if clicked. For example, the results for the predicted correction of your search may be nearly identical to the results for your original search. In these cases, inviting you to refine your search isn’t helpful. This change first checks a spell prediction to see if it’s useful before presenting it to the user. This algorithm was already rolled out in English, but now we’ve expanded to new languages.
• Spelling model refresh and quality improvements. We’ve refreshed spelling models and launched quality improvements in 27 languages.
• Fewer autocomplete predictions leading to low-quality results. [launch codename “Queens5”, project codename “Autocomplete”] We’ve rolled out a change designed to show fewer autocomplete predictions leading to low-quality results.
• Improvements to SafeSearch for videos and images. [project codename “SafeSearch”] We’ve made improvements to our SafeSearch signals in videos and images mode, making it less likely you’ll see adult content when you aren’t looking for it.
• Improved SafeSearch models. [launch codename “Squeezie”, project codename “SafeSearch”] This change improves our classifier used to categorize pages for SafeSearch in 40+ languages.
• Improvements to SafeSearch signals in Russian. [project codename “SafeSearch”] This change makes it less likely that you’ll see adult content in Russian when you aren’t looking for it.
• Increase base index size by 15%. [project codename “Indexing”] The base search index is our main index for serving search results and every query that comes into Google is matched against this index. This change increases the number of documents served by that index by 15%. *Note: We’re constantly tuning the size of our different indexes and changes may not always appear in these blog posts.
• New index tier. [launch codename “cantina”, project codename “Indexing”] We keep our index in “tiers” where different documents are indexed at different rates depending on how relevant they are likely to be to users. This month we introduced an additional indexing tier to support continued comprehensiveness in search results.
• Backend improvements in serving. [launch codename “Hedges”, project codename “Benson”] We’ve rolled out some improvements to our serving systems making them less computationally expensive and massively simplifying code.
• “Sub-sitelinks” in expanded sitelinks. [launch codename “thanksgiving”] This improvementdigs deeper into megasitelinks by showing sub-sitelinks instead of the normal snippet.
• Better ranking of expanded sitelinks. [project codename “Megasitelinks”] This change improves the ranking of megasitelinks by providing a minimum score for the sitelink based on a score for the same URL used in general ranking.
• Sitelinks data refresh. [launch codename “Saralee-76”] Sitelinks (the links that appear beneath some search results and link deeper into the site) are generated in part by an offline process that analyzes site structure and other data to determine the most relevant links to show users. We’ve recently updated the data through our offline process. These updates happen frequently (on the order of weeks).
• Less snippet duplication in expanded sitelinks. [project codename “Megasitelinks”] We’ve adopted a new technique to reduce duplication in the snippets of expanded sitelinks.
• Movie showtimes search feature for mobile in China, Korea and Japan. We’ve expanded our movie showtimes feature for mobile to China, Korea and Japan.
• MLB search feature. [launch codename “BallFour”, project codename “Live Results”] As the MLB season began, we rolled out a new MLB search feature. Try searching for [sf giants score] or [mlb scores].
• Spanish football (La Liga) search feature. This feature provides scores and information about teams playing in La Liga. Try searching for [barcelona fc] or [la liga].
• Formula 1 racing search feature. [launch codename “CheckeredFlag”] This month we introduced a new search feature to help you find Formula 1 leaderboards and results. Try searching [formula 1] or [mark webber].
• Tweaks to NHL search feature. We’ve improved the NHL search feature so it’s more likely to appear when relevant. Try searching for [nhl scores] or [capitals score].
• Keyword stuffing classifier improvement. [project codename “Spam”] We have classifiers designed to detect when a website is keyword stuffing. This change made the keyword stuffing classifier better.
• More authoritative results. We’ve tweaked a signal we use to surface more authoritative content.
• Better HTML5 resource caching for mobile. We’ve improved caching of different components of the search results page, dramatically reducing latency in a number of cases.

• Deeper detection of hacked pages. [launch codename “GPGB”, project codename “Page Quality”] For some time now Google has been detecting defaced content on hacked pages and presenting a notice on search results reading, “This site may be compromised.” In the past, this algorithm has focused exclusively on homepages, but now we’ve noticed hacking incidents are growing more common on deeper pages on particular sites, so we’re expanding to these deeper pages.
• Autocomplete predictions used as refinements. [launch codename “Alaska”, project codename “Refinements”] When a user types a search she’ll see a number of predictions beneath the search box. After she hits “Enter”, the results page may also include related searches or “refinements”. With this change, we’re beginning to include some especially useful predictions as “Related searches” on the results page.
• More predictions for Japanese users. [project codename “Autocomplete”] Our usability testing suggests that Japanese users prefer more autocomplete predictions than users in other locales. Because of this, we’ve expanded the number or predictions shown in Japan to as many as eight (when Instant is on).
• Improvements to autocomplete on Mobile. [launch codename “Lookahead”, project codename “Mobile”] We made an improvement to make predictions work faster on mobile networks through more aggressive caching.
• Fewer arbitrary predictions. [launch codename “Axis5”, project codename “Autocomplete”] This launch makes it less likely you’ll see low-quality predictions in autocomplete.
• Improved IME in autocomplete. [launch codename “ime9”, project codename “Translation and Internationalization”] This change improves handling of input method editors (IMEs) in autocomplete, including support for caps lock and better handling of inputs based on user language.
• New segmenters for Asian languages. [launch codename “BeautifulMind”] Speech segmentation is about finding the boundaries between words or parts of words. We updated the segmenters for three asian languages: Chinese, Japanese, and Korean, to better understand the meaning of text in these languages. We’ll continue to update and improve our algorithm for segmentation.
• Scoring and infrastructure improvements for Google Books pages in Universal Search.[launch codename “Utgo”, project codename “Indexing”] This launch transitions the billions of pages of scanned books to a unified serving and scoring infrastructure with web search. This is an efficiency, comprehensiveness and quality change that provides significant savings in CPU usage while improving the quality of search results.
• Unified Soccer feature. [project codename “Answers”] This change unifies the soccer search feature experience across leagues in Spain, England, Germany and Italy, providing scores and scheduling information right on the search result page.
• Improvements to NBA search feature. [project codename “Answers”] This launch makes it so we’ll more often return relevant NBA scores and information right at the top of your search results. Try searching for [nba playoffs] or [heat games].
• New Golf search feature. [project codename “Answers”] This change introduces a new search feature for the Professional Golf Association (PGA) and PGA Tour, including information about tour matches and golfers. Try searching for [tiger woods] or [2012 pga schedule].
• Improvements to ranking for news results. [project codename “News”] This change improves signals we use to rank news content in our main search results. In particular, this change helps you discover news content more quickly than before.
• Better application of inorganic backlinks signals. [launch codename “improv-fix”, project codename “Page Quality”] We have algorithms in place designed to detect a variety of link schemes, a common spam technique. This change ensures we’re using those signals appropriately in the rest of our ranking.
• Improvements to Penguin. [launch codename “twref2”, project codename “Page Quality”] This month we rolled out a couple minor tweaks to improve signals and refresh the data used by the penguin algorithm.
• Trigger alt title when HTML title is truncated. [launch codename “tomwaits”, project codename “Snippets”] We have algorithms designed to present the best possible result titles. This change will show a more succinct title for results where the current title is so long that it gets truncated. We’ll only do this when the new, shorter title is just as accurate as the old one.
• Efficiency improvements in alternative title generation. [launch codename “TopOfTheRock”, project codename “Snippets”] With this change we’ve improved the efficiency of title generation systems, leading to significant savings in cpu usage and a more focused set of titles actually shown in search results.
• Better demotion of boilerplate anchors in alternate title generation. [launch codename “otisredding”, project codename “Snippets”] When presenting titles in search results, we want to avoid boilerplate copy that doesn’t describe the page accurately, such as “Go Back.” This change helps improve titles by avoiding these less useful bits of text.
• Internationalizing music rich snippets. [launch codename “the kids are disco dancing”, project codename “Snippets”] Music rich snippets enable webmasters to mark up their pages so users can more easily discover pages in the search results where you can listen to or preview songs. The feature launched originally on google.com, but this month we enabled music rich snippets for the rest of the world.
• Music rich snippets on mobile. [project codename “Snippets”] With this change we’ve turned on music rich snippets for mobile devices, making it easier for users to find songs and albums when they’re on the go.
• Improvement to SafeSearch goes international. [launch codename “GentleWorld”, project codename “SafeSearch”] This change internationalizes an algorithm designed to handle results on the borderline between adult and general content.
• Simplification of term-scoring algorithms. [launch codename “ROLL”, project codename “Query Understanding”] This change simplifies some of our code at a minimal cost in quality. This is part of a larger effort to improve code readability.
• Fading results to white for Google Instant. [project codename “Google Instant”] We made a minor user experience improvement to Google Instant. With this change, we introduced a subtle fade animation when going from a page with results to a page without.
• Better detection of major new events. [project codename “Freshness”] This change helps ensure that Google can return fresh web results in realtime seconds after a major event occurs.
• Smoother ranking functions for freshness. [launch codename “flsp”, project codename “Freshness”] This change replaces a number of thresholds used for identifying fresh documents with more continuous functions.
• Better detection of searches looking for fresh content. [launch codename “Pineapples”, project codename “Freshness”] This change introduces a brand new classifier to help detect searches that are likely looking for fresh content.
• Freshness algorithm simplifications. [launch codename “febofu”, project codename “Freshness”] This month we rolled out a simplification to our freshness algorithms, which will make it easier to understand bugs and tune signals.
• Updates to +Pages in right-hand panel. [project codename “Social Search”] We improved our signals for identifying relevant +Pages to show in the right-hand panel.
• Performance optimizations in our ranking algorithm. [launch codename “DropSmallCFeature”] This launch significantly improves the efficiency of our scoring infrastructure with minimal impact on the quality of our results.
• Simpler logic for serving results from diverse domains. [launch codename “hc1”, project codename “Other Ranking Components”] We have algorithms to help return a diverse set of domains when relevant to the user query. This change simplifies the logic behind those algorithms.
• Precise location option on tablet. [project codename “Mobile”] For a while you’ve had the option to choose to get personalized search results relevant to your more precise location on mobile. This month we expanded that choice to tablet. You’ll see the link at the bottom of the homepage and a button above local search results.
• Improvements to local search on tablet. [project codename “Mobile”] Similar to thechanges we released on mobile this month, we also improved local search on tablet as well. Now you can more easily expand a local result to see more details about the place. After tapping the reviews link in local results, you’ll find details such as a map, reviews, menu links, reservation links, open hours and more.
• Internationalization of “recent” search feature on mobile. [project codename “Mobile”] This month we expanded the “recent” search feature on mobile to new languages and regions.

This is the kind of stuff we’re missing out on.

Publishers, webmasters and search industry enthusiasts would no doubt like to see the lists continue. Will they? That remains to be seen.

We’ve reached out to Google more than one about the lists, trying to find out if we should still expect them or not, but the company has not responded.

Update: Since this was originally written, Google did make another post to the Inside Search blog about updates to quick answer results on mobile. More on that here. Still no update lists.

Update 2: Google has posted to the Inside Search blog a second time since this piece was originally written, and this time it is actually about an algorithmic update, though it’s just one new update – they’ll be taking into account copyright removal notices – and no sign of the lists.