This paper, entitled “Personalizing Web Search using Long Term Browsing History,” was submitted to WSDM in 2011, but only recently came to our attention. The authors, Nicolaas Matthijs from the University of Cambridge and Filip Radlinski from Microsoft, write about “a user interest
profile using users’ complete browsing behavior,” but we thank them for taking a moment to recognize Surf Canyon’s contribution to the field:
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.
Mark Cramer, CEO of Surf Canyon, has been honored with an invitation to present Dynamic Ranked Retrieval at the next Bay Area Search Meetup. Anyone near the offices of eBay in San Jose on Wednesday, January 23rd, at 6:30pm, and who would like to attend, should click over to the Meetup website. Here is an Abstract of the talk:
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.
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  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 .
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.
This week Brandon McMullin started work at Surf Canyon as a Software Engineer. He will focus primarily on the development of our new social search application, Chummo.
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!
Last month, the High Tech Practice at McKinsey & Co., one of the most prestigious global management consulting firms, authored “Impact of Internet Technologies: Search” which “examines the value of technologies used to navigate the Internet and is part of a series that focuses on different, Internet-related technologies.” Surf Canyon is honored to be one of the few companies, and the only private company besides Facebook and Twitter, mentioned in “The future of search” section:
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.
Mark Cramer, CEO of Surf Canyon, has been selected to present at the exclusive Search Insider Summit to be held at the South Sea Island Resort on Captiva Island, Florida from May 4 to 7. The semiannual event is chaired by Gord Hotchkiss, CEO of Enquiro, and “brings the best minds in the search industry together to share cutting edge information and experience.”
In the session “Reinventing the Search Experience,” between Microsoft, Yahoo! and Google, Mark will be talking about “Search as a Conversation – The End of the ‘Stateless’ Results Page.” Having dynamically re-ranked over 1.3 billion queries over the past few years, Surf Canyon is uniquely positioned to discuss the potential of one of the most significant relevance-enhancing innovations since PageRank.
[Update 5/10/11] The video of Mark’s presentation is now online:
Surf Canyon's mission is to transform search into a dynamic experience where fluid result pages respond to user actions in real time. We develop Dynamic Search, a real-time contextual search technology. By transforming static lists of links into dynamic search pages that automatically and immediately re-order results in response to user behavior signals, searchers are able to more quickly and easily find pertinent information that might otherwise have remained buried as deep as page 100.