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Published on November 19, 2007

Author: Alohomora

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Implicit User Modeling for Personalized Search:  Implicit User Modeling for Personalized Search Xuehua Shen, Bin Tan, ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign Current Search Engines are Mostly Document-Centered…:  Current Search Engines are Mostly Document-Centered… Documents Search Engine ... Search is generally non-personalized… … … Example of Non-Personalized Search:  Example of Non-Personalized Search Query = Jaguar Without knowing more about the user, it’s hard to optimize… Therefore, personalization is necessary to improve the existing search engines. :  Therefore, personalization is necessary to improve the existing search engines. However, many questions need to be answered… Research Questions:  Research Questions Client-side or server-side personalization? Implicit or explicit user modeling? What’s a good retrieval framework for personalized search? How to evaluate personalized search? … Client-Side vs. Server-Side Personalization :  Client-Side vs. Server-Side Personalization So far, personalization has mostly been done on the server side We emphasize client-side personalization, which has 3 advantages: More information about the user, thus more accurate user modeling (complete interaction history + other user activities) More scalable (“distributed personalization”) Alleviate the problem of privacy Implicit vs. Explicit User Modeling:  Implicit vs. Explicit User Modeling Explicit user modeling More accurate, but users generally don’t want to provide additional information E.g., relevance feedback Implicit user modeling Less accurate, but no extra effort for users E.g., implicit feedback We emphasize implicit user modeling “Jaguar” Example Revisited:  “Jaguar” Example Revisited “car” occurs far more frequently than “Apple” in pages browsed by the user in the last 20 days All the information is naturally available to an IR system Remaining Research Questions:  Remaining Research Questions Client-side or server-side personalization? Implicit or explicit user modeling? What’s a good retrieval framework for personalized search? How to evaluate personalized search? … Outline:  Outline A decision-theoretic framework UCAIR personalized search agent Evaluation of UCAIR Implicit user information exists in the user’s interaction history.:  Implicit user information exists in the user’s interaction history. We thus need to develop a retrieval framework for interactive retrieval… Modeling Interactive IR:  Modeling Interactive IR Model interactive IR as “action dialog”: cycles of user action (Ai ) and system response (Ri ) Retrieval Decisions:  Retrieval Decisions User U: A1 A2 … … At-1 At System: R1 R2 … … Rt-1 Given U, C, At , and H, choose the best Rt from all possible responses to At Document Collection C Query=“Jaguar” All possible rankings of C Best ranking for the query Click on “Next” button All possible rankings of unseen docs Best ranking of unseen docs Decision Theoretic Framework:  Decision Theoretic Framework User: U Interaction history: H Current user action: At Document collection: C Observed All possible responses: r(At)={r1, …, rn} A Simplified Two-Step Decision-Making Procedure :  Approximate the expected risk by the loss at the mode of the posterior distribution Two-step procedure Step 1: Compute an updated user model M* based on the currently available information Step 2: Given M*, choose a response to minimize the loss function A Simplified Two-Step Decision-Making Procedure Optimal Interactive Retrieval:  Optimal Interactive Retrieval User A1 U C Collection IR system Refinement of Decision Theoretic Framework:  Refinement of Decision Theoretic Framework r(At): decision space (At dependent) r(At) = all possible rankings of docs in C r(At) = all possible rankings of unseen docs M: user model Essential component: U = user information need S = seen documents L(ri,At,M): loss function Generally measures the utility of ri for a user modeled as M P(M|U, H, At, C): user model inference Often involves estimating U Case 1: Non-Personalized Retrieval:  Case 1: Non-Personalized Retrieval At=“enter a query Q” r(At) = all possible rankings of docs in C M= U, unigram language model (word distribution) p(M|U,H,At,C) = p(U |Q) Case 2: Implicit Feedback for Retrieval :  Case 2: Implicit Feedback for Retrieval At=“enter a query Q” r(At) = all possible rankings of docs in C M= U, unigram language model (word distribution) H={previous queries} + {viewed snippets} p(M|U,H,At,C) = p(U |Q,H) Implicit User Modeling Case 3: More General Personalized Search with Implicit Feedback :  Case 3: More General Personalized Search with Implicit Feedback At=“enter a query Q” or “Back” button, “Next” link r(At) = all possible rankings of unseen docs in C M= (U, S), S= seen documents H={previous queries} + {viewed snippets} p(M|U,H,At,C) = p(U |Q,H) Eager Feedback Benefit of the Framework :  Benefit of the Framework Traditional view of IR Retrieval  Match a query against documents Insufficient for modeling personalized search (user and the interaction history are not part of a retrieval model) The new framework provides a map for systematic exploration of Methods for implicit user modeling Models for eager feedback The framework also provides guidance on how to design a personalized search agent (optimizing responses to every user action) The UCAIR Toolbar:  The UCAIR Toolbar UCAIR Toolbar Architecture (http://sifaka.cs.uiuc.edu/ir/ucair/download.html):  UCAIR Toolbar Architecture (http://sifaka.cs.uiuc.edu/ir/ucair/download.html) Search Engine (e.g., Google) Search History Log (e.g.,past queries, clicked results) Query Modification Result Re-Ranking User Modeling Result Buffer UCAIR User query results clickthrough… Decision-Theoretic View of UCAIR :  Decision-Theoretic View of UCAIR User actions modeled A1 = Submit a keyword query A2 = Click the “Back” button A3 = Click the “Next” link System responses r(Ai) = rankings of the unseen documents History H = {previous queries, clickthroughs} User model: M=(X,S) X = vector representation of the user’s information need S = seen documents by the user Decision-Theoretic View of UCAIR (cont.):  Decision-Theoretic View of UCAIR (cont.) Loss functions: L(r, A2, M)= L(r, A3, M)  reranking, vector space model L(r,A1,M)  L(q,A1,M)  query expansion, favor a good q Implicit user model inference X* = argmaxx p(x|Q,H), computed using Rocchio feedback S* = all seens docs in H Vector of a seen snippet Newer versions of UCAIR have adopted language models UCAIR in Action:  UCAIR in Action In responding to a query Decide relationship of the current query with the previous query (based on result similarity) Possibly do query expansion using the previous query and results Return a ranked list of documents using the (expanded) query In responding to a click on “Next” or “Back” Compute an updated user model based on clickthroughs (using Rocchio) Rerank unseen documents (using a vector space model) Screenshot for Result Reranking:  Screenshot for Result Reranking A User Study of Personalized Search :  A User Study of Personalized Search Six participants use UCAIR toolbar to do web search Topics are selected from TREC web track and terabyte track Participants explicitly evaluate the relevance of top 30 search results from Google and UCAIR UCAIR Outperforms Google: Precision at N Docs:  UCAIR Outperforms Google: Precision at N Docs More user interactions  better user models  better retrieval accuracy UCAIR Outperforms Google: PR Curve:  UCAIR Outperforms Google: PR Curve Summary:  Summary Propose a decision theoretic framework to model interactive IR Build a personalized search agent for the web search Do a user study of web search and show that UCAIR personalized search agent can improve retrieval accuracy Slide32:  Thank you ! The End Current search engines are very useful, but far from optimal :  Current search engines are very useful, but far from optimal For one thing, they don’t really know about you… IR as Sequential Decision Making:  IR as Sequential Decision Making User System A1 : Enter a query Which documents to present? How to present them? R1: Present search results Which documents to view? A2 : View a document Which part of the document to show? Other documents? R2: Present document content Rerank other documents More result to view? A3 : Click on “Next” button (Information Need) (Model of Information Need) Case 4: User-Specific Result Summary :  Case 4: User-Specific Result Summary At=“enter a query Q” r(At) = {(D,)}, DC, |D|=k, {“snippet”,”overview”} M= (U, n), n{0,1} “topic is new to the user” p(M|U,H,At,C)=p(U,n|Q,H), M*=(*, n*) Choose k most relevant docs If a new topic (n*=1), give an overview summary; otherwise, a regular snippet summary User Models:  User Models Components of user model M User information need User viewed documents S User actions At and system responses Rt-1 … Loss Functions:  Loss Functions Loss function for result reranking Loss function for query expansion Implicit User Modeling:  Implicit User Modeling Update user information need given a new query Learn better user models given skipped top n documents and viewed the (n+1)-th document Case 1: Context-insensitive IR:  Case 1: Context-insensitive IR At=“enter a query Q” r(At) = all possible rankings of docs in C M= U, unigram language model (word distribution) p(M|U,H,At,C)=p(U |Q) Case 2: Context-sensitive IR :  Case 2: Context-sensitive IR At=“enter a query Q” r(At) = all possible rankings of docs in C M= U, unigram language model (word distribution) H={previous queries} + {viewed snippets} p(M|U,H,At,C)=p(U |Q,H) Case 3: General Context-sensitive IR :  Case 3: General Context-sensitive IR At=“enter a query Q” or “Back” button, “Next” button r(At) = all possible rankings of unseen docs in C M= (U, S), S= seen documents H={previous queries} + {viewed snippets} p(M|U,H,At,C)=p(U |Q,H) System Characteristics:  System Characteristics Client side personalization Implicit user modeling and eager feedback Bayesian decision theory as a guide Main Idea: Putting the User in the Center:  Main Idea: Putting the User in the Center Search Engine “jaguar” Personalized search agent WEB Search Engine Email Search Engine Desktop Files Personalized search agent “jaguar” User-Centered Adaptive IR (UCAIR):  User-Centered Adaptive IR (UCAIR) A novel retrieval strategy emphasizing user modeling (“user-centered”) search context modeling (“adaptive”) interactive retrieval Implemented as a personalized search agent that sits on the client-side (owned by the user) integrates information around a user (1 user vs. N sources as opposed to 1 source vs. N users) collaborates with each other Challenges in UCAIR:  Challenges in UCAIR What’s an appropriate retrieval framework for UCAIR? How do we optimize retrieval performance in interactive retrieval? How do we develop robust and accurate retrieval models to exploit user information and search context? How do we evaluate UCAIR methods? …… Non-Personalized Search:  Non-Personalized Search Jaguar Possibility of Personalized Search:  Other Context Info: Dwelling time Mouse movement Possibility of Personalized Search Apple software …

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