Query Similarity by Projections on the Query-Flow Graph

Query similarity by projecting the Query-Flow graph Ilaria Bordino1 , Carlos Castillo2 , Debora Donato2 and Aristides Gionis2 1 Department of Computer and System Sciences Sapienza University of Rome Rome, Italy 2 Yahoo! Research Barcelona, Spain. 33rd Annual ACM SIGIR Conference Geneva, Switzerland July 22nd, 2010

Query similarity by projecting the Query-Flow Graph Query similarity by projecting the Query-Flow grah I. Bordino Query similarity is a key ingredient for Motivation Related work 1 Query recommendation Projecting the Query-Flow graph Clustering query recom- 2 Query expansion mendations Diversifying Query Recom- mendations 3 Improving advertising algorithms Conclusions Critical issues: Dependence on user intent, polysemy, synonymy, various types of noise

Query-log analysis Query similarity by projecting the Query-Flow Query logs of search engines store valuable information grah about user searches I. Bordino Motivation Related work Queries and the following actions are the wisdom of all Projecting the Web searchers Query-Flow graph Clustering query recommendations Diversifying Query Recom- mendations Conclusions Applications: ranking, helping users

Graphs from search-engine query logs Query similarity by projecting the Query-Flow grah Graph Reference Nodes Edges I. Bordino Word graph Baeza-Yates[SOFSEM 2007] Queries Common words Motivation Related work Session graph Baeza-Yates[SOFSEM 2007] Queries Consecutive ap- pearance in a Projecting the Query-Flow session graph URL Cover graph Baeza-Yates[SOFSEM 2007] Queries Common clicked Clustering results query recommendations Click graph Beeferman et al. [KDD Queries and Docu- Documents clicked Diversifying 2000], Wen et al. [WWW ments for a query Query Recom- 2001], Craswell et al. mendations [WSDM 2008] Conclusions View graph Castillo et al. [AirWEB 2008] Queries and Docu- Documents viewed ments for a query Anti-click graph Castillo et al. [AirWEB 2008] Queries and Docu- Documents ments viewed, but not clicked for a query Query-Flow graph Boldi et al. [CIKM 2008] Queries Sequentiality of similar queries

Using query logs for query-recommendation Query similarity by projecting the Query-Flow Most query-recommendation methods use similarity grah measures based on I. Bordino Motivation Related work Clicked documents Projecting the [J. Wen et al. WWW 2001],[R. Baeza-Yates et al. EDBT 2004], [N. Craswell and M. Szummer SIGIR 2007], Query-Flow graph [A. Fuxman et al. WWW 2008],[Q. Mei et al. CIKM 2008] Clustering query recommendations Query reformulations Diversifying Query Recom- [B.M. Fonseca et al. LA-WEB 2003], [R. Jones et al. WWW 2006], [Z. Zhang et al. WWW 2006], mendations [R.W. White et al. SIGIR 2007], [J. Luxenberger et al. CIKM 2008], [M. Richardson ACM Trans. Web Conclusions 2008], [R.W. White et al. ACM Trans. Web 2008],[E. Sadikov et al. WWW 2010] Our approach: use the Query-Flow graph to derive a new similarity measure for queries

The Query-Flow graph Query similarity by projecting the Query-Flow Focus: sequentiality of similar queries grah I. Bordino Motivation Goal: modeling user behavioral patterns and query Related work dependencies Projecting the Query-Flow graph Clustering query recommendations Nodes represent queries submitted to the search engine Diversifying Query Recom- mendations Conclusions Edges connect queries expressing similar information needs [P Boldi et al. CIKM 2008] .

The Query-Flow graph Query similarity by projecting the Query-Flow grah I. Bordino Motivation Related work Projecting the Query-Flow graph Clustering query recommendations Diversifying Query Recom- mendations Conclusions Applications: session ﬁnding, query recommendation [P Boldi et al. CIKM 2008],[P Boldi et al. WSCD 2009], [P Boldi et al. WI 2009] . . .

Our contribution Query similarity by projecting the Query-Flow Intuition: related queries tend to cluster in local grah neighborhoods of the Query-Flow graph I. Bordino Motivation Related work Projecting the Query-Flow graph Clustering Idea: project the Query-Flow graph query recommendations Diversifying Query Recom- mendations Conclusions Measure similarity between queries in the resulting geometric space

Graph projections: Spectral embedding Query similarity by projecting the Query-Flow Graph projections allow to measure distances in graphs grah I. Bordino Motivation Related work Many techniques: Multidimensional scaling, spectral Projecting the Query-Flow projection, IsoMap, Maximum-Variance unfolding graph Clustering query recommendations Diversifying Query Recom- mendations We use Spectral embedding Conclusions Distance metric: Cosine Similarity

Similarity with graph projections Query similarity by projecting the Query-Flow Baseline similarity measure: Cosine similarity of grah neighborhoods in the Query-Flow graph I. Bordino Motivation Related work Projecting the Query-Flow graph Clustering query recommendations Diversifying Query Recom- mendations Conclusions

Projecting the Query-Flow graph Query similarity by projecting the Query-Flow Project the whole graph and store 5 spectral grah coordinates for each query I. Bordino Motivation Related work Projecting the Query-Flow graph Clustering query recommendations Diversifying Query Recom- mendations Conclusions

Projecting query-dependent subgraphs Query similarity by projecting the Query-Flow Goal: obtain similarities of better qualities grah I. Bordino Online processing: project a small subgraph centered in the input query Motivation Related work Projecting the Query-Flow graph Clustering query recommendations Diversifying Query Recom- mendations Conclusions

Projecting query-independent subgraphs Query similarity by projecting the Query-Flow We need to trade-off performance and costs grah I. Bordino Project query-independent partitions of the Query-Flow Motivation graph (METIS) Related work Projecting the Query-Flow graph Clustering query recommendations Diversifying Query Recom- mendations Conclusions

Further optimizations Query similarity by projecting the Query-Flow Varying the number of dimensions of the projected grah space I. Bordino Motivation Related work Projecting the Query-Flow graph Clustering Taking the edge weights into account query recommendations Diversifying Query Recom- mendations Conclusions Query-independent subgraphs: Cluster expansion methods

Application 1: Clustering query recommendations Query similarity by projecting the Query-Flow Choose 150 sample queries from the log grah I. Bordino Motivation Test set of a query: top Query-Flow successors Related work Projecting the Query-Flow graph Clustering Each set is manually clustered (ground truth) query recommendations Diversifying Query Recom- mendations Apply the graph-projection method and obtain a Conclusions similarity measure between queries Evaluate agreement with the ground truth: average intra-cluster / inter-cluster similarity

Example of manually deﬁned clusters Input query: sun Query similarity by projecting the Query-Flow grah I. Bordino Motivation Related work Projecting the Query-Flow graph Clustering query recommendations Diversifying Query Recom- mendations Conclusions

Example of manually deﬁned clusters Input query: watch Query similarity by projecting the Query-Flow grah I. Bordino Motivation Related work Projecting the Query-Flow graph Clustering query recommendations Diversifying Query Recom- mendations Conclusions

Performance evaluation: Example Input query: watch Query similarity by projecting the Query-Flow grah Query: watch rolex watch citizen watch seiko watch watch free watch I. Bordino movie online movies.net rolex watch - 0.052 0.044 0.003 0.002 citizen watch 0.052 - 0.087 0.004 0.002 Motivation seiko watch 0.044 0.087 - 0.004 0.003 Related work watch free movie online 0.003 0.004 0.004 - 0.04 Projecting the watch movies.net 0.002 0.002 0.003 0.04 - Query-Flow (a) Cosine similarity between vectors of neighbors in the QFG graph Query: watch rolex watch citizen watch seiko watch watch free watch Clustering movie online movies.net query recommendations rolex watch - 0.99 0.99 0.12 0.12 citizen watch 0.99 - 1.00 0.09 0.08 Diversifying seiko watch 0.99 1.00 - 0.12 0.11 Query Recom- mendations watch free movie online 0.12 0.09 0.12 - 1.00 watch movies.net 0.12 0.08 0.11 1.00 - Conclusions (b) Spectral embedding of the full graph Query: watch rolex watch citizen watch seiko watch watch free watch movie online movies.net rolex watch - 1.00 1.00 -0.57 -0.58 citizen watch 1.00 - 1.00 -0.56 -0.57 seiko watch 1.00 1.00 - -0.57 -0.57 watch free movie online -0.57 -0.56 -0.57 - 1.00 watch movies.net -0.58 -0.57 -0.57 1.00 - (c) Projecting the 2-step neighborhood of the query

Performance evaluation: Summary Query similarity by projecting the Query-Flow grah I. Bordino Motivation Related work Projecting the Query-Flow graph Clustering query recommendations Diversifying Query Recom- mendations Conclusions X axis: Projection Method N: baseline F1 :1-step neighborhood (induced subgraph) S2 , S3 : 2(3)-step neighborhood (BFS subgraph) G: projecting the full graph Y axis: MC Average intra-cluster/inter-cluster similarity

Application 2: generating diverse recommendations Query similarity by projecting the Query-Flow Baseline: top frequent Query-Flow successors grah I. Bordino Motivation Diversiﬁcation method: greedy search that maximizes Related work diversity while maintaining high relevance Projecting the Query-Flow graph Clustering query recom- Distance measure: distance in mendations Diversifying Query Recom- mendations 1 the Query-Flow graph Conclusions 2 Projection of the whole graph 3 Projection of the 2-step neighborhood of the query

Manual evaluation Query similarity by projecting the Query-Flow 1 Perceived diversity: grah I. Bordino Motivation Query-dependent subgraphs yield the best results Related work Projecting the Query-Flow graph 2 Perceived relevance: Clustering query recom- Baseline algorithm: 97% mendations Diversifying Query Recom- mendations Graph with no projections: 97% Conclusions Projecting the whole graph: 90% Projecting the 2-step neighborhood of the query: 92%

Conclusions & Future work Query similarity by projecting the Query-Flow Projecting the Query-Flow graph we obtain a good grah similarity measure for queries I. Bordino Motivation Offline method: Store 3-5 spectral coordinates per Related work query and then use them at query time at constant cost Projecting the Query-Flow graph Online method: Compute and project a small Clustering query recom- query-dependent subgraph at query time mendations Diversifying Query Recom- Applications: Clustering and Diversifying query mendations Conclusions recommendations Future work: More effective offline methods Alternative projection methods and graph definitions

That’s it! Query similarity by projecting the Query-Flow grah Thanks for your attention! I. Bordino Motivation Related work Projecting the Query-Flow graph Clustering query recommendations Diversifying Query Recom- mendations Questions? Conclusions

Query Similarity by Projections on the Query-Flow Graph

by Carlos Castillo

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