Personalized Search

Recently, Yahoo! began to use their new ad system Panama and hopes to reduce the gap of money-making power between Google and Yahoo. From an article on December 26, 2006 of Business Week, I got to know that Tim Boyd, a financial analyst of Caris & Co. estimated that Google makes 20 cents per search while Yahoo! makes 10 cents per search. During a visit, I told this number to a friend. My friend said he got a different number and sometimes the number from a financial analyst should be double-checked. I agree with the viewpoint of my friend. Moreover, Jon Bentley also suggested that we should use “back-of-the-envelope” calculations, a standard fare in engineering schools. Here is my “back-of-the-envelope” calculation about the Google’s money-making power.

In Q3 2006, the total revenue of Google is $2.690 Billion according to Google income statement. According to Nielson//NetRating data, Google received 2.776 Billion queries (49% US search share) in July 2006, 3.003 Billion queries (50%) in August 2006, and 2.826 Billion queries (50%) in September 2006. Thus in Q3 2006, there are 8.605 Billion queries submitted to Google. If we assume that all revenue of Google comes from Ad (AdWord or AdSense), then on average Google makes $2.690Billion / 8.605Billion query = $0.31/query, i.e., 31 cents per query.

In Q4 2006, the total revenue of Google is 3.205 Billion. According to Nielson//NetRating data, Google received 3.022 Billion queries (50%) in October 2006, 3.098 Billion queries (50%) in November 2006, and 3.036 Billion queries (51%) queries in December 2006. Thus in Q4 2006, there are 9.156 Billion queries submitted to Google. On average Google makes $3.022 Billion / $9.156 Billion query = $0.33/query, i.e., 33 cents per query.

From the simple calculation of Q3 2006 and Q4 2006, we can see Google indeed makes around 30 cents per query on average. Since Yahoo! revenue comes from diverse sources, it is difficult to compute the Yahoo! number according to the number of Nielson/NetRating and Financial report.

Recently, Google pushes personalized search. They now have the personalized homepage, search history and personalized search results. I tried the personalized search and it seems that it is not clear whether they do personalized search or not for a specific query. I think it is one aspect that Google can improve, i.e., get each user informed when personalization happens and which results are personalized results. About this, Marissa Mayer said in an interview

One thing that we’ve struggled with is if we should actually mark the results are entering the page as a result of personalization but because team is currently and frequently doing experiments, we didn’t want to settle on a particular model or marker at this exact moment.

Marissa Mayer, VP of Google, said in the interview

The actual implementation of personalized search is that as many as two pages of content, that are personalized to you, could be lifted onto the first page and I believe they never displace the first result, because that’s a level of relevance that we feel comfortable with. So right now, at least eight of the results on your first page will be generic, vanilla Google results for that query and only up to two of them will be results from the personalized algorithm. I think the other thing to remember is, even when personalization happens and lifts those two results onto the page, for most users it happens one out of every five times.

I like the idea of combining personalized search results and generic search results together. In my thesis, I proposed progressive personalization. When the search engine is not confident about the user intention, it can present generic results to the user and at least must not annoy the user by pushing unrelated personalized results; when the search engine are confident about the user intention, it can push personalized results to the user.

In a summary, Google is pushing personalized search in a conservative way.

There are two talks related with search or search personalization.

One is a talk about search privacy, by Dr. Lorrie Faith Cranor, a professor at CMU.

The other is a talk about Secure Personalization: Towards Trustworthy Recommender Systems, by  Dr. Bamshad Mobasher, a professor at Depaul.

Today, I got to know that Findory, a personalized news website,  “rides into the sunset“.  It is a sad news. But I believe that personalization technology will succeed somewhere in the real-world applications.

I found that there is a talk by Dr. Alfred Kobsa, a professor at UCI. The title of the talk is Privacy-Enhanced Personalization. It should be very relevant to my thesis research on privacy-preserving personalized search.

Long time ago, I mentioned the Vivisimo CEO’s comments about personalization (see my post on September 30, 2005). I just found that on Greg Linden’s blog, Greg has a post and there are some interesting follow-up comments.

Again, generally I disagree with the “dead end” viewpoints. But we need to do solid work to demonstrate the advantage of personalization technology.

There is a seminar course (Search Engines: Technology, Society, and Business) offered in SIMS, Berkeley in fall 2005. From the course website, it is said “A set of top-notch experts have agreed to give lectures for fall 2005.” Among them, Dr. Susan Dumais from Microsoft Research and Dr. Sepandar Kamvar (co-founder of Kaltrix) from Google will give lectures. Both of them are doing personalized search. Thus the topics of them probably are related with the personalized search. The slides and videos for some talks are available at the website.

Google Web API provides a way for programmers to develop interesting search related applications utilizing the power of Google search engine. But currently there are some limitations for programmer to develop a large-scale application. I notice that there are at least two limitations. One is that one account can at most submit 1000 requests one day and the other is that for each query the user can only get at most 10 search results. With these two limitations, the client-side programs can not get many results frequently from Google through Google Web API and thus can not do many interesting processing such as result reranking at a large scale.

Yahoo Web API permits 5000 queries per IP per day and 50 search results per query. So Yahoo Web API is friendlier to developers. Meanwhile, MSN is also preparing to release their Web APIs (see news from News.com). Hope the competition will boost the upgrade of Web APIs of all search engines in the near future, which will benefit developers and eventually end users.

Besides personalized search engines in industry, there are personalized portal and recommendation system, which is briefly discussed as follows.

Personalized Portal: My Yahoo is the pioneer in the personalized web portal, which includes personalized news, weather forecast, comics, and TV listing. The user can customize the personalized portal by setting user interested content, color, layout and etc. Findory is a web site which provides the personalized news service. Unlike My Yahoo, the user does not need to explicitly specify the user interest. Instead, the web site implicitly infers the user interests through the user interaction history on the web site. The more user browsing history is collected, the better personalized news articles selection is presented.

Recommendation System: Many E-Commerce web sites try to build personalized stores for each online customer. Amazon is the most famous one in building personalized web stores. They use collaborative filtering techniques to recommend stuff for the customers according to product purchased or viewed by customers before.

Web search engines have achieved great successes in helping people find information on the Web, especially for simple information need such as homepage finding. However, search engines still perform poorly in many other tasks. There are many reasons to cause the poor performance of the search engine. Among them, two important reasons are frequently pointed out. First, many user queries are ambiguous or the user himself does not know how to specify the information need exactly. Thus the search engine can not infer the real user information need just according to the current user query. Second, information retrieval is an interactive process; users will adjust their queries during this process. Therefore, the search engine should also adjust the inference of user information need. Nevertheless, currently most, if not all, search engines use only the user’s current query to do the search. Some search engine companies such as Google, MSN Search and Yahoo are trying to use contextual and personal information to help the search. Some search engines have already released the test version of personalized search such as Google. Yahoo co-founder Jerry Young said that the relevance of search is still the Holy Grail for any search application and the key challenge for Yahoo and all search companies going forward will be to find ways to increase the personalization of results, i.e., making sure that a user truly finds what he or she is looking for when typing in a keyword search.