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A Structured Approach to Query Recommendation With Social Annotation Data

This study explores a structured approach to query recommendation using social annotation data to enhance search queries. By leveraging query relation graphs and clustering with modularity, the system recommends relevant queries to users based on search interests and exploratory interests.

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A Structured Approach to Query Recommendation With Social Annotation Data

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  1. A Structured Approach to Query Recommendation With Social Annotation Data 童薇

  2. Outline • Motivation • Challenges • Approach • Experimental Results • Conclusions

  3. Outline • Motivation • Challenges • Approach • Experimental Results • Conclusions

  4. Motivation • Query Recommendation • Help users search • Improve the usability of search engines

  5. Recommend what? • Existing Work • Search interests: stick to user’s search intent • Anything Missing? • Exploratory Interests: some vague or delitescent interests • Unaware of until users are faced with one • May be provoked within a search session equivalent or highly related queries apple iphone smartphones nexus one apple products ipod touch mobileme

  6. Is the existence of exploratory interest commonand significant? • Identified from search user behavior analysis • Make use of one-week log search data • Verified by Statistical Tests(Log-likehood Ratio Test) • Analyze the causality between initial queries and consequ-ent queries • Results • In 80.9% of cases: Clicks on search results indeed affect the formulation of the next queries • In 43.1% of cases: Users would issue different next queries if they clicked on different results

  7. Two different heading directions of Query Recommendation • Emphasize search interests: • Help users easily refine their queries and find what they • need more quickly • Enhance the “search-click-leave” behavior equivalent or highly related queries apple iphone • Focus on exploratory interests: • Attract more user clicks and make search and browse more closely integrated • Increase the staying time and advertisement revenue nexus one Recommend queries to satisfy both search and exploratory interests of users simultaneously ipod touch mobileme

  8. Outline • Motivation • Challenges • Our Approach • Experimental Results • Conclusions

  9. Challenges • To leverage what kind of data resource? Search logs: Interactions between search users and search engines Social annotation data: Keywords according to the content of the pages “wisdom of crowds”

  10. Challenges • To leverage what kind of data resource? • How to present such recommendations to users? Refine queries Stimulate exploratory interests A Structured Approach to Query Recommendation With Social Annotation Data

  11. Outline • Motivation • Challenges • Approach • Experimental Results • Conclusions

  12. Approach • Query Relation Graph • A one-mode graph with the nodes representing all the unique queries and the edges capturing relationships between queries • Structured Query Recommendation • Ranking using Expected Hitting Time • Clustering with Modularity • Labeling each cluster with social tags

  13. Query Relation Graph • Query Formulation Model

  14. Query Relation Graph • Query Formulation Model 2 3 5 3 4 1 2

  15. Query Relation Graph • Query Formulation Model • Construction of Query Relation Graph 2 3 3 2 5 1 3 3 4 1 2 1 1 2

  16. Ranking with Hitting Time • Apply a Markov random walk on the graph • Employ hitting time as a measure to rank queries • The expected number of steps before node j is visited starting from node i • The hiting time T is the first time that the random walk is at node j from the start node i: • The mean hitting time h(j|i) is the expectation of T under the condition

  17. Ranking with Hitting Time • Apply a Markov random walk on the graph • Employ hitting time as a measure to rank queries • The expected number of steps before node j is visited starting from node i • Satisfies the following linear system

  18. Clustering with Modularity • Group the top k recommendations into clusters • It is natural to apply a graph clustering approach • Modularity function Note: In a network in which edges fall between vertices without regard for the communities they belong to ,we would have

  19. Clustering with Modularity • Group the top k recommendations into clusters • It is natural to apply a graph clustering approach • Modularity function • Employ the fast unfolding algorithm to perform clustering

  20. Clustering with Modularity • Group the top k recommendations into clusters • It is natural to apply a graph clustering approach • Modularity function • Employ the fast unfolding algorithm to perform clustering • Label each cluster explicitly with social tags The expected tag distribution given a query: The expected tag distribution under a cluster:

  21. Outline • Motivation • Challenges • Approach • Experimental Results • Conclusions

  22. Experimental Results • Data set • Query Logs: Spring 2006 Data Asset (Microsoft Research) • 15 million records (from US users) sampled over one month in May, 2006 • 2.7 million unique queries and 4.2 million unique URLs • Social Annotation Data: Delicious data • Over 167 million taggings sampled during October and November, 2008 • 0.83 million unique users, 57.8 unique URLs and 5.9 million unique tags • Query Relation Graph: 538, 547 query nodes • Baseline Methods • BiHit: Hitting Time approach based on query logs (Mei et al. CIKM ’08) • TriList: list-based approach to query recommendation considering both search and exploratory interests • TriStrucutre: Our approach

  23. Examples of Recommendation Results Query = espn

  24. Examples of Recommendation Results Query = 24

  25. Manual Evaluation • Comparison based on users’click behavior • A label tool to simulate the real search scenario • Label how likelihood the user would like to click (6-point scale) • Randomly sampled 300 queries, 9 human judges

  26. Experimental Results (cont.) • Overall Performance non-zero label score ➡ click Clicked Recommendation Number (CRN) Clicked Recommendation Score (CRS) Total Recommendation Score (TRS) Click Performance Comparison Distributions of Labeled Score over Recommendations

  27. Experimental Results (cont.) • How Structure Helps • How the structured approach affects users’ click willingness • Click Entropy The Average Click Entropy over Queries under the TriList and TriStructure Methods.

  28. Experimental Results (cont.) • How Structure Helps • How the structured approach affects users’ click patterns • Label Score Correlation Correlation between the Average Label Scores on Same Recommendations for Queries.

  29. Outline • Motivation • Challenges • Approach • Experimental Results • Conclusions

  30. Conclusions • Recommend queries in a structured way for better satisfying both search and exploratory interests of users • Introduce the social annotation data as an important resource for recommendation • Better satisfy users interests and significantly enhance user’s click behavior on recommendations • Future work • Trade-off between diversity and concentration • Tag propagation

  31. Thanks！

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