A method and apparatus for utilizing user behavior to immediately modify sets of search results so that the most relevant documents are moved to the top. In one embodiment of the invention, behavior data, which can come from virtually any activity, is used to infer the user’s intent. The updated inferred implicit user model is then exploited immediately by re-ranking the set of matched documents to best reflect the information need of the user. The system updates the user model and immediately re-ranks documents at every opportunity in order to constantly provide the most optimal results. In another embodiment, the system determines, based on the similarity of results sets, if the current query belongs in the same information session as one or more previous queries. If so, the current query is expanded with additional keywords in order to improve the targeting of the results.

Along with U.S. Patent No. 8,095,582 for “Dynamic Search Engine Results Employing User Behavior” and U.S. Patent No. 8,117,197 for “Adaptive User Interface for Real-Time Search Relevance Feedback,” the new patent rounds out Surf Canyon’s portfolio concerning the real-time re-ranking of search results in response to implicit user feedback.

To commemorate this momentous occasion, we have designed and will be giving away a special, universally loved item of tchotchke: t-shirts! You may check out our design and, should you like one for yourself, submit a request on the sign-up sheet. If you leave your address, we’ll do our best to get you one.

Recently, personalized search has also been made available in some mainstream web search engines including Google and Yahoo!. These appear to use a combination of explicitly and implicitly collected information about the user. Many more companies are engaging in personalization both for search (e.g. surfcanyon.com) and for advertising based on user behavior.

Dynamic Ranked Retrieval – “Unsticking” the SERP

Search is stuck. And, frequently, so too are searchers.  The problem stems from the fact that, as Jaime Teevan described, “Web queries are very short, and it is unlikely that a two- or three-word query can unambiguously describe a user’s information goal.” Search engines respond to this by attempting to divine the user’s intent by exploiting massive quantities of data prior to delivering a static SERP, but the onus of either unambiguously describing the intent, or digging through pages of “best guesses,” often falls upon the user. This can be a daunting task depending on the searcher’s skill and knowledge of the subject matter as well as the clarity of the information need.

Dynamic Ranked Retrieval addresses this problem by creating a fluid SERP that re-ranks results “on the fly” in response to real-time implicit feedback from the user.  The strongest signals are the immediate ones, and the half-life of their informational value is extremely short, so waiting until a subsequent query to exploit them is deficient. Adapting the SERP dynamically to a real-time model of inferred user intent built from implicit feedback will maximize relevance and searcher satisfaction without requiring additional effort. We will demonstrate the technology and present real-world data on the impact to IR.

[Update 1/28/2013] The video from the presentation is now available online:

“To be honest, I don’t google anymore. Search engines like Surf Canyon work just as well…”

We would point out that, for many queries, the innovative Dynamic Ranked Retrieval technology that we developed and embedded into our search page significantly improves the search experience over Google by automatically digging out relevant results for the user, but we are flattered nonetheless.

ChengXiang received his Ph.D. in Computer Science from Nanjing University in 1990 and another Ph.D. in Language and Information Technologies from Carnegie Mellon in 2002. His research interests include information retrieval, text mining, natural language processing, machine learning and bioinformatics, and he has published over 100 papers in major conferences and journals in these areas, including five award papers.

He is an Associate Editor of ACM Transactions on Information Systems and Information Processing and Management, and serves on the editorial board of the Information Retrieval Journal. He is a program co-chair of ACM CIKM 2004, NAACL HLT 2007 and ACM SIGIR 2009, where Mark Cramer was a presenter. He is an ACM Distinguished Scientist and has received many awards, including the 2004 Presidential Early Career Award for Scientists and Engineers (PECASE), the 2008 Alfred P. Sloan Research Fellowship, the 2007 Microsoft Beyond Search – Semantic Computing and Internet Economics Award, the 2009 IBM Faculty Award, the 2010 UIUC Rose Award for Teaching Excellence and the 2011 HP Innovation Research Award.

We are thrilled to have ChengXiang on the team as we look forward to exploring new avenues to improve the efficiency of information retrieval as well as continuing our collaboration with the UIUC.

For ambiguous queries, conventional retrieval systems are bound by two conflicting goals. On the one hand, they should diversify and strive to present results for as many query intents as possible. On the other hand, they should provide depth for each intent by displaying more than a single result. Since both diversity and depth cannot be achieved simultaneously in the conventional static retrieval model, we propose a new dynamic ranking approach. In particular, our proposed two-level dynamic ranking model allows users to adapt the ranking through interaction, thus overcoming the constraints of presenting a one-size-fits-all static ranking.

Surf Canyon is again referenced, along with our 2009 SIGIR research paper:

We argue that a key to solving the conflict between depth and diversity lies in the move to dynamic retrieval models [2] that can take advantage of user interactions. Instead of presenting a single one-size-fits-all ranking, dynamic retrieval models allow users to adapt the ranking dynamically through interaction, as is done by surfcanyon.com [5].

In February 2008, Surf Canyon launched its Dynamic Ranked Retrieval application to rave reviews. As the body of research relating to Dynamic Ranked Retrieval grows, we continue to be encouraged by the potential of this technology to vastly enhance the quality of information retrieval.

In addition to his passion for software engineering, Brandon studies Shorinji Kempo, an art that focuses on personal improvement and the balance between strength and compassion. He is also a father. His 13-month-old daughter just recently gained some proficiency in walking and is intensely interested in exploring the world around her. She’s also very interested in daddy’s toys (like the laptop and Xbox controller) and has begun building structures with Mega Bloks. Clearly an engineer already in the making!

Our first hire in a few years, we’re thrilled to welcome Brandon to the team!

Importantly, relevant search results are increasingly deemed to be personalized. Autonomous search agents that make suggestions based on personal data, including the user’s location, metadata, and more advanced algorithms, are in sight. For example, Surf Canyon, a US company, is developing real-time, personalized search capabilities that transform static lists of search results into dynamic pages that rerank results based on a user’s real-time online activity.

Surf Canyon is proud to be a sponsor of the Fifth Annual International Conference on Web Search and Data Mining (WSDM 2012). After having our research selected for oral presentation at SIGIR ’09 in Boston and then having attended other academic conferences, we have a strong appreciation for the effort and dedication required to produce high-quality research in the very challenging field of search. While we didn’t have a paper to submit this time, we will naturally be attending the conference and look forward to the presentations as well as connecting, and reconnecting, with talented researches from around the world.