Eugene Agichtein, Emory University 11 July 2010 Inferring Searcher Intent Eugene Agichtein Emory University Tutorial Website (for expanded and updated bibliography): http://ir.mathcs.emory.edu/intent_tutorial/ Instructor contact information: Email: eugene@mathcs.emory.edu Web: http://www.mathcs.emory.edu/~eugene/ Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 Emory University Tutorial Overview • Part 1: Search Intent Modeling – Motivation: how intent inference could help search – Search intent & information seeking behavior in traditional IR – Searcher models: from eye tracking to clickthrough mining • Part 2: Inferring Web Searcher Intent – Inferring result relevance: clicks – Richer interaction models: clicks + browsing • Part 3: Applications and Extensions – Implicit feedback for ranking – Contextualized prediction: session modeling – Personalization, query suggestion, active learning Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 2 Emory University AAAI 2010 Tutorial: Inferring Searcher Intent 1
Eugene Agichtein, Emory University 11 July 2010 About the Instructor • Eugene Agichtein ( Ah-ghi-sh-tein ) http://www.mathcs.emory.edu/~eugene/ • Research: Information retrieval and data mining – Mining search behavior and interactions in web search – Text mining , information extraction, and question answering • Relevant experience: 2006 - Assistant Professor, Emory University Summer’07: Visiting Researcher, Yahoo! Research 2004-06: Postdoc, Microsoft Research 1998 - 2004: PhD student, Columbia • Databases/IR Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 3 Emory University Outline: Search Intent and Behavior � Motivation: how intent inference could help search • Search intent and information seeking behavior – Classical models of information seeking • Web searcher intent • Web searcher behavior – Levels of modeling: micro-, meso-, and macro- levels – Variations in web searcher behavior – Click models Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 4 Emory University AAAI 2010 Tutorial: Inferring Searcher Intent 2
Eugene Agichtein, Emory University 11 July 2010 Some Key Challenges for Web Search • Query interpretation (infer intent) • Ranking (high dimensionality) • Evaluation (system improvement) • Result presentation (information visualization) Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 5 Emory University Example: Task-Goal-Search Model car safety ratings consumer reports Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 6 Emory University AAAI 2010 Tutorial: Inferring Searcher Intent 3
Eugene Agichtein, Emory University 11 July 2010 Information Retrieval Process Overview Credit: Jimmy Lin, Doug Oard, … Source Search Engine Selection Resource Result Page (SERP) Query Query: car safety ratings Formulation Ranked List Search Selection Documents query reformulation, vocabulary learning, relevance feedback Examination Documents source reselection Delivery Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 7 Emory University Explicit Intentions in Query Logs Strohmaier et al., K-Cap 2009 • Match known goals (from ConceptNet) to query logs Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 8 Emory University AAAI 2010 Tutorial: Inferring Searcher Intent 4
Eugene Agichtein, Emory University 11 July 2010 Unfortunately, most queries are not so explicit… Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 9 Emory University Outline: Search Intent and Behavior � Motivation: how intent inference could help search � Search intent and information seeking behavior – Classical models of information seeking • Web Searcher Intent – Broder – Rose – More recent? • Web Searcher Behavior – Levels of modeling: micro-, meso-, and macro- levels – Variations in web searcher behavior – Click models • Challenges and open questions Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 10 Emory University AAAI 2010 Tutorial: Inferring Searcher Intent 5
Eugene Agichtein, Emory University 11 July 2010 Information Seeking Funnel D. Rose, 2008 Wandering: the user does not have an • information seeking-goal in mind. May have a meta-goal (e.g. “find a topic for my final paper.”) • Exploring: the user has a general goal (e.g. “learn about the history of communication technology”) but not a plan for how to achieve it. • Seeking : the user has started to identify information needs that must be satisfied (e.g. “find out about the role of the telegraph in communication.”), but the needs are open- ended. • Asking: the user has a very specific information need that corresponds to a closed-class question (“when was the telegraph invented?”). Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 11 Emory University Models of Information Seeking • “Information-seeking … includes recognizing … the information problem, establishing a plan of search, conducting the search, evaluating the results, and … iterating through the process.”- Marchionini, 1989 – Query formulation – Action (query) – Review results – Refine query Adapted from: M. Hearst, SUI, 2009 Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 12 Emory University AAAI 2010 Tutorial: Inferring Searcher Intent 6
Eugene Agichtein, Emory University 11 July 2010 Reviewing Results: Relevance Clues • What makes information or information objects relevant? What do people look for in order to infer relevance? – Topicality (subject relevance) – Extrinsic (task-, goal- specific) • Information Science “clues research”: – uncover and classify attributes or criteria used for making relevance inferences Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 13 Emory University Information Scent for Navigation • Examine clues where to find useful information Search results listings must provide the user with clues about which results to click Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 14 Emory University AAAI 2010 Tutorial: Inferring Searcher Intent 7
Eugene Agichtein, Emory University 11 July 2010 Dynamic “Berry Picking” Model • Information needs change during interactions [Bates, 1989] M.J. Bates. The design of browsing and berrypicking techniques for the on- line search interface. Online Review , 13(5):407–431, 1989. Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 15 Emory University Information Foraging Theory Pirolli and Card , CHI 1995 Goal: maximize rate of information gain. Patches of information � websites Basic Problem: should I continue in the current patch or look for another patch? Expected gain from continuing in current patch, how long to continue searching in that patch Eugene Agichtein Emory AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 16 University AAAI 2010 Tutorial: Inferring Searcher Intent 8
Eugene Agichtein, Emory University 11 July 2010 - Charnov’s Marginal Value Theorem Diminishing returns: 80% of users scan only first 3 pages of search results Eugene Agichtein Emory AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 17 University Hotel Search Goal: Find cheapest 4-star hotel in Paris. Step 1: pick hotel search site Step 2: scan list Step 3: goto 1 Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 18 Emory University AAAI 2010 Tutorial: Inferring Searcher Intent 9
Eugene Agichtein, Emory University 11 July 2010 Example: Hotel Search (cont’d) Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 19 Emory University Orienteering vs. Teleporting Teevan et al., CHI 2004 • Orienteering: – Searcher issues a quick, imprecise to get to approximately the right information space region – Searchers follow known paths that require small steps that move them closer to their goal – Easy (does not require to generate a “perfect” query) • Teleporting: – Issue (longer) query to jump directly to the target – Expert searchers issue longer queries – Requires more effort and experience. – Until recently, was the dominant IR model Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 20 Emory University AAAI 2010 Tutorial: Inferring Searcher Intent 10
Eugene Agichtein, Emory University 11 July 2010 Serendipity Andre et al., CHI 2009 Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 21 Emory University Summary of Models • Static, berry-picking, information foraging, orienteering, serendipity • Classical IR Systems research mainly uses the simplest form of relevance ( topicality ) • Open questions: – How people recognize other kinds of relevance – How to incorporating other forms of relevance (e.g., user goals/needs/tasks) into IR systems Eugene Agichtein AAAI 2010 Tutorial: Inferring Searcher Intent 7/11/2010 22 Emory University AAAI 2010 Tutorial: Inferring Searcher Intent 11
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