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Archive for the ‘Search/SEO’ Category

Bing Redesign Is On The Way

May 11th, 2012 Comments off

According to a story in MediaPost.com today, Bing.com has been redesigned and new ad models are on the way

“Microsoft introduced a new version of the search engine with a three-column design combining traditional Web search and social. The product will soon roll out in the U.S.”

“The combination of search and social signals will produce a clear and clean vision of user intent. Derrick Connell, corporate VP of search program management, said the right rail paid-search ads will remain in place and should perform slightly better. ”

“In the future, Microsoft will “start to experiment with new ad formats and models.”

Search pages must evolve or become obsolete”

“While the left rail will continue to serve up search results, the middle column will give users a snapshot or relevant information and services related to the search, including maps, restaurant reservations and reviews. The aim is to provide the information before the searcher asks.”

“For now, Bing will tap into as much publicly available data as possible from Facebook — and soon Twitter and other networks.”

“The redesign should not have an influence on Yahoo search results.

The redesign of Bing  “will not have a material impact on the algorithmic search results we provide to Yahoo Search.”

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Google’s Penguin Update Has WebMasterWorld’s Forum On Fire

May 6th, 2012 Comments off

Google’s Penguin update to its algorithm, has a lot of people on the WebmasterWorld.com Forum chatting about its effect

Most comments are detecting changes to their own sites and pages.

As usually it was one comment that started off the conversation that is currently over 30 pages and here it is:

“”"After over 10 years of consistently ranking between position #1 and #3 on page one for a single four letter word search term, the month of March (starting on the 10th) has resulted in falling to below the fold, then to page 2, then page 3 and today I have been completely removed for that term.

In place is nothing but garbage. Branding obviously has lost traction. I’m thinking whatever they have done is meant to stick. I checked to see if I had been over optimized for the term, but with a mere 4 occurrences on my page and my need to use that term to describe my product, I really don’t know what they want from us anymore…

The effect of lost traffic has set off all the “Big Traffic change for URL” warnings and the anayltics charts have nose dived. Anyone else seeing this type of situation on their long established sites?

Lost income from March updates is just about $1000 / week.

I’d almost guess that Google is just removing older authority sites in favor of nothing but news articles and blogs.”"

Here are some other comments I found interesting off the forum:

1.


Matt Cutts and his team are geniuses and have finally figured out a way to combat SPAM on the internet. This update had a ton of collateral damage and he will be fixing it in the coming weeks and/or months.

or

Matt Cutts and his team have over-engineered the Google algorithm and this cluster-f@*$ of an update will solve nothing. Thousands of honest webmasters who were only trying to follow the rules will get dinged……and yes, some spammers will, however the spammers will come right back with a new set of tricks, once they figure out how to take advantage of the new loopholes created by the algorithm.

Meanwhile, thousands of honest webmasters will still pay the price and never get their rankings back…..thereby creating a void in the search results that will be filled with even more spammy looking sites.

I think Cutts and Co. unfairly targeted sites with affiliate links and made “catch-all” filters for sites that do have affiliate links, regardless of whether they were spam or not.…

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If Only 20% Of The US Population Has Even Heard Of SEO Why Are We Surprised About There Lack Of Knowledge Of Domains?

March 30th, 2012 Comments off

According to Matt Cutts of Google only 20% of the US population has ever “heard” of the term SEO.

The question wasn’t whether people understand the term SEO, its significance; its importance in the internet community.

The question was  just whether they have heard of it, and the answer of 20% is pretty shocking:

“”In my world, everyone I talk to has heard of search engine optimization (SEO).”

“But I’ve always wondered: do regular people in the U.S. know what SEO is? ”

“With Google’s new Consumer Surveys product, I can actually find out. I asked 1,576 people “Have you heard of ‘search engine optimization’?”

“It turns out only 1 in 5 people (20.4%) in the U.S. have heard of SEO! ”

“The survey also turned up an interesting gender difference: almost 25% of men have heard of SEO, but only about 16% of women have.”

So if only 20% of the US population have heard of SEO, how can we expect them to have heard and understand the value of domain names by in large?

 

 …

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Cnet: Google Plans to Penalize ‘Overly Optimized’ Sites

March 18th, 2012 Comments off

According to Cnet.com, Google is planning to penalize sites that overuse search-engine-optimization techniques.

Google engineer Matt Cutts discussed the plan at South by Southwest (SXSW) and Search Engine Land posted an audio clip from a panel discussion.

“Google wants to “level the playing field” regarding “all those people doing, for lack of a better word, over optimization or overly SEO–versus those making great content and great sites”.

“We are trying to make GoogleBot smarter, make our relevance better, and we are also looking for those who abuse it, like too many keywords on a page, or exchange way too many links or go well beyond what you normally expect.”

“The changes will begin affecting search results “in the upcoming month or next few weeks”

This should make exact match domain even more valuable as Google continues to lessen the ability to game the system.…

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If Gus is Right Domain Names Maybe In Big Trouble As Google Moves To Location Based Search Results

March 12th, 2012 Comments off

In the course of the week we at Mostwanteddomains.com, receive 50-100 offers on and occasionally run into some interesting people.

Last week I had email conversation with a guy named Gus.

So according to Gus, who claims to have some inside knowledge about Google’s algorithm and plans for how it will rank sites in the future, domains are in big trouble.

According to Gus, Google will start ranking sites based on the location of the searcher, so that direct match domains will no longer have preference when it comes to search results.

“Domains are not dead, its just that Google will no longer rank them based on searches. “

Says Gus

“Right now if you search “Bellevue trial attorney” you will see that bellevuetrialattorney.com is #1, if you search “Bellevue trial attorneys”, you will see that bellevuetrialattorneys.com is #1. “

“If you own the .com that matches the key words, your always #1.”

“Well that ends soon when “location” starts.”

“It’s Google’s new algorithm code. Its based on where you are, and it produces results within feet of where you actually are.”

“There is maybe 22 people in the world that know this.”

Well I guess there are more than 22 now.

Of course Google has been giving more and more location based search results for a while.

Search for “Best Buy” and a map will come up showing you where all the Best Buy stores are located closest to you.

Now whether Google will alter it’s search results to actually provide the top results  based off of location, rather than other factors including direct match domains is interesting.

Using Gus’s example, if you type in “Bellevue trial attorney” and live in in close to Bellevue,  it’s certainly not a stretch to think you will see search results based on your location with the top links showing up be attorney’s who are closest to you.

Let’s say you didn’t live in Bellevue.   Lets say you lived in Seattle or even Florida but had a matter that needed handling in Bellevue would you still get search results based on the attorney’s closest to you or would the search default to current search results?

Let’s assume  Gus is right and Google moves completely to a location based search results.

There are many other losers and caveats other than direct match domains if Google moved to an all location based search.

SEO

How many SEO guys are there out in the world that are going to lose a lot of work if Google simply starts ranking sites by the links closest to the user.…

Google Blogs About Out 40 Changes It Made In February

February 27th, 2012 Comments off

Google just published on its blog,  40 changes to search quality

“This month we have many improvements to celebrate. With 40 changes reported, that marks a new record for our monthly series on search quality. Most of the updates rolled out earlier this month, and a handful are actually rolling out today and tomorrow. We continue to improve many of our systems, including related searches, sitelinks, autocomplete, UI elements, indexing, synonyms, SafeSearch and more. Each individual change is subtle and important, and over time they add up to a radically improved search engine.

Here’s the list for February:

  • 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 for expanded 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.

Matt Cutts: Google Changes Algorithm to Penalize Sites That “Don’t Have Much Content “above-the-fold”

January 20th, 2012 Comments off

In a blog post today Matt Cutts of Google delivers what appears to be bad news for mini-sites or other sites top-heavy with ads.

According to the post, Google is once again changing their algorithm to penalize sites that  “don’t have much content “above-the-fold”.

Here is the full post:

“”In our ongoing effort to help you find more high-quality websites in search results, today we’re launching an algorithmic change that looks at the layout of a webpage and the amount of content you see on the page once you click on a result.”"

As we’ve mentioned previously, we’ve heard complaints from users that if they click on a result and it’s difficult to find the actual content, they aren’t happy with the experience.

“”Rather than scrolling down the page past a slew of ads, users want to see content right away.”

“”So sites that don’t have much content “above-the-fold” can be affected by this change. If you click on a website and the part of the website you see first either doesn’t have a lot of visible content above-the-fold or dedicates a large fraction of the site’s initial screen real estate to ads, that’s not a very good user experience. Such sites may not rank as highly going forward.”"

“”We understand that placing ads above-the-fold is quite common for many websites; these ads often perform well and help publishers monetize online content. This algorithmic change does not affect sites who place ads above-the-fold to a normal degree, but affects sites that go much further to load the top of the page with ads to an excessive degree or that make it hard to find the actual original content on the page. This new algorithmic improvement tends to impact sites where there is only a small amount of visible content above-the-fold or relevant content is persistently pushed down by large blocks of ads.”"

“”This algorithmic change noticeably affects less than 1% of searches globally. That means that in less than one in 100 searches, a typical user might notice a reordering of results on the search page. If you believe that your website has been affected by the page layout algorithm change, consider how your web pages use the area above-the-fold and whether the content on the page is obscured or otherwise hard for users to discern quickly.”"

“”You can use our Browser Size tool, among many others, to see how your website would look under different screen resolutions.”"

“”If you decide to update your page layout, the page layout algorithm will automatically reflect the changes as we re-crawl and process enough pages from your site to assess the changes.…

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Microsoft’s New Study Showing That Domain Names Matter

December 13th, 2011 Comments off

Microsoft has just released a paper (.pdf) on the value of good domains calling it “Domain Bias” which they say proves that a good domain matters to users.

Domain Bias according to the author is defined as “a user’s propensity to believe that a page is more relevant just because it comes from a particular domain.”

“We provide evidence of the existence of domain bias in click activity as well as in human judgments via a comprehensive collection of experiments. ”

“We begin by studying the difference between domains that a search engine surfaces and that users click. Surprisingly, we find that despite changes in the overall distribution of surfaced domains, there has not been a comparable shift in the distribution of clicked domains.”

“”Users seem to have learned the landscape of the internet and their click behavior has thus become more predictable over time.”"

“We find that domains can actually flip a user’s preference about 25% of the time.”"

“The existence of domain bias has numerous consequences including, for example, the importance of discounting click activity from reputable domains.”

“”Our goal is to provide incontrovertible proof of the existence of domain bias.”
We do so via a series of carefully designed experiments.
We ask if a search engine drastically changes the surfaced domains, do domain clicks also change accordingly? Amazingly, the answer turns out to be no. In- stead, we find that users click on the same domains despite changes in surfaced content. In a similar vein, if we take two search engines of wildly different relevance, we ask if domain clicks also swing wildly. Again, to our surprise, the answer is no. We observe that the top domains garner a larger and larger fraction of the clicks and it is not because search engines are surfacing a smaller number of domains. On the contrary, search engines are changing the domains they show. It is users who have decided to visit a smaller number of domains.

It should not be surprising that users have learned to trust some domains over others. Indeed, past work such as TrustRank measures user trust at a domain level [10]. A recent eye-tracking study also confirms that users pay at- tention to the displayed URL1. One could argue that search

1“Eye-tracking studies: More than meets the eye”,

engines already know this and exploit it by using the PageR- ank of a domain in their scoring functions so as to boost documents from domains of high reputation.

What is surprising is that users click on results from rep- utable domains even when more relevant search results are available. Our experiments are geared towards proving that domains can so drastically influence perceived relevance that users will favor some domains, regardless of content. View- ing content on the Internet as products, domains have emerged as brands. And users have developed such fierce brand loy- alty that their clicks are tainted by domains.

We establish the existence of domain bias via a Pepsi/Coke style blind taste test. In our experiment, we request rele- vance feedback from different users where each is shown a query and two search results in three scenarios: with the snippet only (i.e., absent domain), with the snippet and true URL, and with the snippet and swapped URL. We find that in 25% of the cases, the behavior of users resembles a blind following to domains. For example, for the query {one of the most common types of heart disease}, there are two snippets and two domains, one from webmd.com and another from genetichealth.com. Absent domain, users prefer the snippet from genetichealth. When domains are revealed, users prefer the snippet of webmd. More interest- ingly, when we paired the genetichealth snippet with the webmd URL, users flip their preference and go back to prefer- ring the snippet from genetichealth (now paired with the domain webmd). The experiment demonstrates that users have become reliant on domains in assessing the relevance of search results, and may in some cases blindly trust content from reputable domains.

Next, we design an experiment to demonstrate a system- atic bias towards certain domains that spans across search queries. Designing an experiment to tease out the existence of domain trust is a non-trivial task. One confounding fac- tor is relevance—perhaps the reason why certain domains attract the majority of clicks is that content from the do- main appears to be more relevant to the user. Another con- founding factor is position bias—perhaps the search engine tends to rank some domains higher than others and that is what leads to the observed domain preference. We design an experiment that removes the relevance factor by focusing on query, URL1, URL2 combinations that are labeled equally relevant by a strong majority of a panel of human judges. Further, the experiment removes position bias by only draw- ing inferences about domain A being preferred to domain B when A is ranked below B and yet still A is clicked more often. By accumulating these preferences, we find that we can construct an ordering of domains that agrees well with user preferences. Such an ordering with strong agreement would have been impossible in the absence of domain trust, thus confirming its presence.

The existence of domain trust has important consequences for several areas of web search research. For example, it in- fluences the design of user click models [3, 6, 7, 9], which have focused on relevance and position of the search results as the principal factors that influence user clicks. Domain bias introduces a new factor that needs to be considered. It also influences the large body of literature of learning relevance from clicks. While many studies have considered ways to remove position bias [2, 7, 11], we must now consider

published at http://googleblog.blogspot.com/2009/02/ eye-tracking-studies-more-than-meets.html, 2009.

how to remove domain bias. Domain bias also affects how queries are categorized as navigational vs. informational. As user visits concentrate on fewer domains, former informa- tional queries may now appear navigational, and semantic approaches may be needed to distinguish between the two types.

The goal of this paper is to provide indisputable proof of the existence of domain bias. We believe this is an important phenomenon and we take careful steps in establishing that it exists beyond reasonable doubt. We also take first steps in quantifying the amount of bias as it can help with the aforementioned applications. Nonetheless, our approach is limited in scale due to the reliance of human labels. The quantification of domain bias at web scale remains a deep challenge and we leave it as a great question for future work.

2. RELATED WORK

The bias of user clicks on search engines has been studied before. Joachims et. al. found user clicks to be good signals for implicit relevance judgments but observed via an eye- tracking study that there is considerable position bias [12]. Later, Craswell et. al. carried out ranking perturbation ex- periments and proposed a cascade model: users scan results from top to bottom and make click decisions based on rele- vance [6]. Similar to our study, Craswell et. al. found that users did not blindly trust search engines. Unlike the study by Craswell et. al., however, our findings are at the aggre- gate level of page domains and explain clicks beyond pure relevance. In [23], the authors show that users are biased towards “attractively” formatted snippets. Our experiments are geared towards establishing a different bias, by pairing snippets with swapped URLs.

User browsing models for search engine results, both or- ganic and sponsored, have attracted considerable attention in recent years [1, 3, 6, 9, 21, 22, 23]. These models aim to estimate the click-through rate (CTR) of a result (i.e., the probability that a result is clicked), given the result’s position and previous clicks in the user session. The CTR is commonly modeled as the product of the examination probability and the perceived relevance of the result (proba- bility of a click given examination). The models vary in the examination probability and perceived relevance functions, but all agree that these functions depend only on the cur- rent state of the results (i.e., pages) and the current user’s session clicks. On the other hand, our work shows that CTR is not only influenced by relevance and examination but also by domain preference.

It is well known that page quality is correlated with its hosting domain. There is related work on domain trust in the context of spam. For example, Gyo ̈ngyi et. al. proposed TrustRank – PageRank like ranking of domains based on a seed set of trusted reputable sites [10]. It is common prac- tice for search engines to use domain as a feature in ranking. For example, PageRank [17] can be applied to the hyperlink structure on domains to obtain domain rank scores. Alter- natively, domains that garner many clicks may be boosted higher in the ranking. Our work shows that if clicks are used to boost pages in the ranking, that domain bias must first be discounted.

A related line of research is on the bias of search engines on page popularity. Cho and Roy observed that search engines penalized newly created pages by giving higher ranking to the current popular pages [4]. A number of solutions were

proposed including using the change in popularity as a signal of page quality [5] and partial randomization of ranking [19]. Although this line of work is related to ours in that we look at the influence of search engines on users, our focus is dif- ferent: we aim to understand and model user’s long-term preference for specific domains.

There are a number of macro-level user behavior stud- ies that we will present in Section 3. For example, [20, 14, 15] analyze user traffic from search engines to individual sites and characterize search and browsing behavior. Unlike previous studies that characterize search behavior at a par- ticular time point, our work emphasizes longitudinal search behavior. Mei and Church [15] conducted a related study where they showed that the visited web at a particular point in time has low entropy. Our work is different in that we look at the visited web over time. We similarly confirm that user visits are predictable, but we also point out that user visits are slow to change. Users are consistent about the domains they visit and are less influenced by changes in the displayed results.

3. SEARCH ENGINE INFLUENCE

We set out to study user domain bias by examining user behavior from search engines at the aggregate level. Our goal is to check whether users simply follow search engines and click on the top returned results without giving them much scrutiny. So we start by comparing the changes in the top displayed results to the changes in the clicked results. Intuitively, if users have little preference for domains, we expect the changes in the displayed results to trigger equiv- alent changes in the clicked results. Surprisingly, however, in spite of the changes in the displayed results we find that clicks tend to be rather stable with respect to domains.

Our experiments also reveal that search results concen- trate over time on fewer domains with increasingly larger share of results pointing to the top domains. This trend is accompanied by an increase in click-through rates (even after factoring out query distribution changes) and is in con- trast to the growing size of the web content and the number of registered domains.

Although the evidence we present in this section alone does not definitively prove the existence of domain bias (we provide more rigorous experiments in the subsequent sec- tions), the results are likely to be potential consequences of the phenomenon. By pointing out the potential conse- quences up front, we motivate careful examination of domain bias in web search.

Figure 1: Methodology.

Why Domains? We study user visit patterns in terms of the aggregate distribution of page views over their hosting domains. Although technically we aggregate at the level of hosts, we use the term “domains” throughout the paper.

Consider Figure 1. This is a sample of URLs clicked by search engine users with the total number of clicks each URL received, irrespective of queries. We aggregate clicks on pages from the same host to obtain a distribution of clicks over the host names. We look at hosts and not the individual pages because studying visits with respect to pages over a long time period becomes impractical: after one year nearly 60% of the pages are replaced by new ones [16]. More im- portantly, aggregating visits over hosts makes sense because hosts roughly correspond to individual publishers on the web, e.g., each sub-domain of wordpress.com corresponds to an individual blog. We also performed experiments on top level domains and obtained similar results to the ones presented here.

Data. Our findings are based on data derived from search logs. We study user visit patterns over a seven-day period at two different time points: July 2009 and July 2010. Looking at the same period in 2009 and 2010 minimizes the temporal bias. To remove variance due to geographic and linguistic differences in search behavior, we only consider queries is- sued in the English speaking United States locale.

Method. We use Shannon entropy to measure the display and visit distribution of domains. Compared to other mea- sures such as power-law exponent, Shannon entropy has an intuitive meaning: it is the average number of bits required to encode the destination domain of each visit. Increasing entropy is a sign of user visits becoming more diverse, while decreasing entropy is a sign of user visits becoming more pre- dictable and suggests the formation of domain preferences.

We use KL(Kullback-Leibler) divergence to measure the

difference between two distributions. Recall that KL diver-

gence is defined as DKL(p||q) = P p(d)log p(d) for two d∈D q(d)

distributions p = {p(d)} and q = {q(d)}. KL divergence measures the average number of extra bits required to en- code the distribution of p using the optimal encoding of q. Together with Shannon entropy, it provides an intuition of the magnitude of distribution changes. One problem with the use of KL divergence is that it is undefined when there is a domain d such that p(d) > 0 and q(d) = 0. To address this issue, we employ the standard add-one smoothing: be- fore computing the distribution, we add one to the count of each domain.

3.1 Displayed vs. Clicked Results

We compare the search displays and user visits from the same search engine in two time periods: July 2009 and July 2010. We refer to these data sets as 2009 and 2010 data, respectively. We only consider the top 5 results returned by the search engine for each query. By focusing on the top results, we aim to reduce the influence of examination bias: users scan results from the top to bottom, so the top results are more likely to be examined [12]. We also analyzed the top 1 result and top 10 result distributions and found similar insights to the ones we present here.

Table 1 shows the ten most frequently displayed domains in 2009. We show the display and visit shares2 point-wise for each domain in 2009 and 2010. Observe the drastic change in the display shares in contrast to the more modest change in the visit shares. The entropy, as well as the KL diver-

2The display (visit) share of a domain is the number of times the domain is displayed (visited) over the total number of displays (visits).

URLs

Host

2009

2010

en.wikipedia.org/bp_oil_spill

200

en.wikipedia.org

300

300

en.wikipedia.org/eigenvector

100

facebook.com/jenn-ibe

20

facebook.com

100

400

facebook.com/privacy

30

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Microsoft’s New Study Showing That Domain Names Are VERY Important

December 13th, 2011 Comments off

Microsoft has just released a paper (.pdf) on the value of good domains calling it “Domain Bias” which looks like one of the best papers ever written on the subject of domains that prove out the value of a good domain.

This paper may become a domainers best friend in helping to sell domains to end users.

The authors call this as Domain Bias according to the author is defined as “a user’s propensity to believe that a page is more relevant just because it comes from a particular domain.”"We provide evidence of the existence of domain bias in click activity as well as in human judgments via a comprehensive collection of experiments. ”

“We begin by studying the difference between domains that a search engine surfaces and that users click. Surprisingly, we find that despite changes in the overall distribution of surfaced domains, there has not been a comparable shift in the distribution of clicked domains.”

“”Users seem to have learned the landscape of the internet and their click behavior has thus become more predictable over time.”"

“We find that domains can actually flip a user’s preference about 25% of the time.”"

“The existence of domain bias has numerous consequences including, for example, the importance of discounting click activity from reputable domains.”

“”Our goal is to provide incontrovertible proof of the existence of domain bias.”

“We do so via a series of carefully designed experiments.”

“We ask if a search engine drastically changes the surfaced domains, do domain clicks also change accordingly? Amazingly, the answer turns out to be no.”

“Instead, we find that users click on the same domains despite changes in surfaced content. In a similar vein, if we take two search engines of wildly different relevance, we ask if domain clicks also swing wildly. Again, to our surprise, the answer is no.”

“We observe that the top domains garner a larger and larger fraction of the clicks and it is not because search engines are surfacing a smaller number of domains. On the contrary, search engines are changing the domains they show. It is users who have decided to visit a smaller number of domains.”

“It should not be surprising that users have learned to trust some domains over others. ”

“”What is surprising is that users click on results from rep-utable domains even when more relevant search results are available.”

“Our experiments are geared towards proving that domains can so drastically influence perceived relevance that users will favor some domains, regardless of content. Viewing content on the Internet as products, domains have emerged as brands. And users have developed such fierce brand loyalty that their clicks are tainted by domains.”

“Our experiments also reveal that search results concentrate over time on fewer domains with increasingly larger share of results pointing to the top domains.”

“This trend is accompanied by an increase in click-through rates (even after factoring out query distribution changes) and is in contrast to the growing size of the web content and the number of registered domains.”

You should check out the full 10 page study.

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Google Changes Its Search Algorithm & Actually Talks About It

November 14th, 2011 Comments off

Google published a post today on its blog “to share the methodology and process behind our search ranking, evaluation and algorithmic changes. ”

“Here’s a list of ten improvements from the past couple weeks:

  • 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.

If you’re a site owner, before you go wild tuning your anchor text or thinking about your web presence for Icelandic users, please remember that this is only a sampling of the hundreds of changes we make to our search algorithms in a given year, and even these changes may not work precisely as you’d imagine. We’ve decided to publish these descriptions in part because these specific changes are less susceptible to gaming.”"”

For those of us working in search every day, we think this stuff is incredibly exciting — but then again, we’re big search geeks. Let us know what you think and we’ll consider publishing more posts like this in the future.

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