ACM SIGIR 2009 Workshop on Redundancy, Diversity, and Interdependent Document Relevance, July 23, 2009, Boston, MA 1 Modeling Diversity in Information Retrieval ChengXiang (Cheng) Zhai Department of Computer Science Graduate School of Library & Information Science Institute for Genomic Biology Department of Statistics University of Illinois, Urbana-Champaign Joint work with John Lafferty, William Cohen, and Xuehua Shen

Different Reasons for Diversification Redundancy reduction Diverse information needs –Mixture of users –Single user with an under-specified query –Aspect retrieval –Overview of results Active relevance feedback … 2

Outline Risk minimization framework Capturing different needs for diversification Language models for diversification 3

4 IR as Sequential Decision Making UserSystem A 1 : Enter a query Which documents to present? How to present them? R i : results (i=1, 2, 3, …) Which documents to view? A 2 : View document Which part of the document to show? How? R: Document content View more? A 3 : Click on Back button (Information Need) (Model of Information Need)

5 Retrieval Decisions User U: A 1 A 2 … … A t-1 A t System: R 1 R 2 … … R t-1 Given U, C, A t, and H, choose the best R t from all possible responses to A t History H={(A i,R i )} i=1, …, t-1 Document Collection C Query=Jaguar All possible rankings of C The best ranking for the query Click on Next button All possible size-k subsets of unseen docs The best k unseen docs R t r(A t ) R t =?

6 A Risk Minimization Framework User: U Interaction history: H Current user action: A t Document collection: C Observed All possible responses: r(A t )={r 1, …, r n } User Model M=(S, U …) Seen docs Information need L(r i,A t,M)Loss Function Optimal response: r* (minimum loss) ObservedInferred Bayes risk

7 Approximate the Bayes 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

8 Optimal Interactive Retrieval User A1A1 UC M* 1 P(M 1 |U,H,A 1,C) L(r,A 1,M* 1 ) R1R1 A2A2 L(r,A 2,M* 2 ) R2R2 M* 2 P(M 2 |U,H,A 2,C) A3A3 … Collection IR system

At enter a query, click on Back button, click on Next button, …} r(A t ): decision space (A t dependent) –r(A t ) = all possible subsets of C + presentation strategies –r(A t ) = all possible rankings of docs in C –r(A t ) = all possible rankings of unseen docs –… M: user model –Essential component: U = user information need –S = seen documents –n = Topic is new to the user L(R t,A t,M): loss function –Generally measures the utility of R t for a user modeled as M –Often encodes retrieval criteria (e.g., using M to select a ranking of docs) P(M|U, H, A t, C): user model inference –Often involves estimating a unigram language model U 9 Refinement of Risk Minimization

10 Generative Model of Document & Query [Lafferty & Zhai 01] observed Partially observed U User S Source inferred d Document q Query R

11 Risk Minimization with Language Models [Lafferty & Zhai 01, Zhai & Lafferty 06] Choice: (D 1, 1 ) Choice: (D 2, 2 ) Choice: (D n, n )... query q user U doc set C source S q 1 N Loss L

12 Optimal Ranking for Independent Loss Decision space = {rankings} Sequential browsing Independent loss Independent risk = independent scoring Risk ranking principle [Zhai 02, Zhai & Lafferty 06]

Risk Minimization for Diversification Redundancy reduction: loss function includes a redundancy/novelty measure –Special case: list presentation + MMR [Zhai et al. 03] Diverse information needs: loss function defined on latent topics –Special case: PLSA/LDA + aspect retrieval [Zhai 02] Active relevance feedback: loss function considers both relevance and benefit for feedback –Special case: feedback only (hard queries) [Shen & Zhai 05] 13

Subtopic Retrieval Query: What are the applications of robotics in the world today? Find as many DIFFERENT applications as possible. Example subtopics: A 1 : spot-welding robotics A 2 : controlling inventory A 3 : pipe-laying robots A 4 : talking robot A 5 : robots for loading & unloading memory tapes A 6 : robot [telephone] operators A 7 : robot cranes … Subtopic judgments A 1 A 2 A 3 …... A k d … 0 0 d … 0 0 d … 1 0 …. d k Need to model interdependent document relevance

Diversify = Remove Redundancy [Zhai et al. 03] 15 Willingness to tolerate redundancy C2<C3, since a redundant relevant doc is better than a non-relevant doc Greedy Algorithm for Ranking: Maximal Marginal Relevance (MMR)

A Mixture Model for Redundancy P(w|Background) Collection P(w|Old) Ref. document 1- =? p(New|d)= = probability of new (estimated using EM) p(New|d) can also be estimated using KL-divergence

Evaluation metrics Intuitive goals: – Should see documents from many different subtopics appear early in a ranking (subtopic coverage/recall) – Should not see many different documents that cover the same subtopics (redundancy). How do we quantify these? –One problem: the intrinsic difficulty of queries can vary.

Evaluation metrics: a proposal Definition: Subtopic recall at rank K is the fraction of subtopics a so that one of d1,..,dK is relevant to a. Definition: minRank(S,r) is the smallest rank K such that the ranking produced by IR system S has subtopic recall r at rank K. Definition: Subtopic precision at recall level r for IR system S is: This generalizes ordinary recall-precision metrics. It does not explicitly penalize redundancy.

Evaluation metrics: rationale recall K minRank(Sopt,r) minRank(S,r) precision For subtopics, the minRank(Sopt,r) curves shape is not predictable and linear.

Evaluating redundancy Definition: the cost of a ranking d1,…,dK is where b is cost of seeing document, a is cost of seeing a subtopic inside a document (before a=0). Definition: minCost(S,r) is the minimal cost at which recall r is obtained. Definition: weighted subtopic precision at r is will use a=b=1

Evaluation Metrics Summary Measure performance (size of ranking minRank, cost of ranking minCost) relative to optimal. Generalizes ordinary precision/recall. Possible problems: –Computing minRank, minCost is NP-hard! –A greedy approximation seems to work well for our data set

Experiment Design Dataset: TREC interactive track data. –London Financial Times: 210k docs, 500Mb –20 queries from TREC 6-8 Subtopics: average 20, min 7, max 56 Judged docs: average 40, min 5, max 100 Non-judged docs assumed not relevant to any subtopic. Baseline: relevance-based ranking (using language models) Two experiments –Ranking only relevant documents –Ranking all documents

S-Precision: re-ranking relevant docs

WS-precision: re-ranking relevant docs

Results for ranking all documents Upper bound: use subtopic names to build an explicit subtopic model.

Summary: Remove Redundancy Mixture model is effective for identifying novelty in relevant documents Trading off novelty and relevance is hard Relevance seems to be dominating factor in TREC interactive-track data

Diversity = Satisfy Diverse Info. Need [Zhai 02] Need to directly model latent aspects and then optimize results based on aspect/topic matching Reducing redundancy doesnt ensure complete coverage of diverse aspects 27

Aspect Generative Model of Document & Query U User q Query S Source d Document =( 1,…, k ) PLSI: LDA:

Aspect Loss Function Uq S d

Aspect Loss Function: Illustration Desired coverage p(a| Q ) Already covered p(a| 1 )... p(a| k -1 ) Combined coverage p(a| k ) New candidate p(a| k ) non-relevant redundant perfect

Evaluation Measures Aspect Coverage (AC): measures per-doc coverage – #distinct-aspects/#docs Aspect Uniqueness(AU): measures redundancy –#distinct-aspects/#aspects Examples #doc … … #asp … … #uniq-asp AC: 2/1=2.0 4/2=2.0 5/3=1.67 AU: 2/2=1.0 4/5=0.8 5/8= …... d1d1 d3d3 d2d2

Effectiveness of Aspect Loss Function (PLSI)

Effectiveness of Aspect Loss Function (LDA)

Comparison of 4 MMR Methods CC - Cost-based Combination QB - Query Background Model MQM - Query Marginal Model MDM - Document Marginal Model

Summary: Diverse Information Need Mixture model is effective for capturing latent topics Direct modeling of latent aspects/topics is more effective than indirect modeling through MMR in improving aspect coverage, but MMR is better for improving aspect uniqueness With direct topic modeling and matching, aspect coverage can be improved at the price of lower relevance-based precision

Diversify = Active Feedback [Shen & Zhai 05] Decision problem: Decide subset of documents for relevance judgment

Independent Loss

Independent Loss (cont.) Uncertainty Sampling Top K

Dependent Loss Heuristics: consider relevance first, then diversity Gapped Top K Select Top N documents Cluster N docs into K clusters K Cluster Centroid MMR …

Illustration of Three AF Methods Top-K (normal feedback) … Gapped Top-K K-cluster centroid Aiming at high diversity …

Evaluating Active Feedback Query Select K docs K docs Judgment File + Judged docs Initial Results No feedback (Top-k, gapped, clustering) Feedback Results

Retrieval Methods (Lemur toolkit) Query Q Document D Results Kullback-Leibler Divergence Scoring Feedback Docs F={d 1, …, d n } Active Feedback Default parameter settings unless otherwise stated Mixture Model Feedback Only learn from relevant docs

Comparison of Three AF Methods CollectionActive FB Method #Rel Include judged docs HARD Top-K Gapped Clustering AP88-89 Top-K Gapped *0.389* Clustering Top-K is the worst! bold font = worst * = best Clustering uses fewest relevant docs

Appropriate Evaluation of Active Feedback New DB (AP88-89, AP90) Original DB with judged docs (AP88-89, HARD) Original DB without judged docs Cant tell if the ranking of un- judged documents is improved Different methods have different test documents See the learning effect more explicitly But the docs must be similar to original docs

Comparison of Different Test Data Test DataActive FB Method oc AP88-89 Including judged docs Top-K Gapped Clustering AP90Top-K Gapped Clustering Clustering generates fewer, but higher quality examples Top-K is consistently the worst!

Summary: Active Feedback Presenting the top-k is not the best strategy Clustering can generate fewer, higher quality feedback examples

Conclusions There are many reasons for diversifying search results (redundancy, diverse information needs, active feedback) Risk minimization framework can model all these cases of diversification Different scenarios may need different techniques and different evaluation measures 47

References Risk Minimization –[Lafferty & Zhai 01] John Lafferty and ChengXiang Zhai. Document language models, query models, and risk minimization for information retrieval. In Proceedings of the ACM SIGIR 2001, pages –[Zhai & Lafferty 06] ChengXiang Zhai and John Lafferty, A risk minimization framework for information retrieval, Information Processing and Management, 42(1), Jan. 2006, pages Subtopic Retrieval –[Zhai et al. 03] ChengXiang Zhai, William Cohen, and John Lafferty, Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval, In Proceedings of ACM SIGIR – [Zhai 02] ChengXiang Zhai, Language Modeling and Risk Minimization in Text Retrieval, Ph.D. thesis, Carnegie Mellon University, Active Feedback –[Shen & Zhai 05] Xuehua Shen, ChengXiang Zhai, Active Feedback in Ad Hoc Information Retrieval, Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ( SIGIR'05), 59-66, 2005 ACM SIGIR 2009 Workshop on Redundancy, Diversity, and Interdependent Document Relevance, July 23, 2009, Boston, MA 48

49 Thank You!