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Computational User Intent Modeling

Computational User Intent Modeling. Hongning Wang March 6, 2013. Research Summary. Joint relevance and freshness learning [WWW’12] Content-Aware Click Modeling [WWW’13] Cross-Session Search Task Extraction [WWW’13]. Understanding User Intent is Important. “Apple Company” @ Oct. 4, 2011.

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Computational User Intent Modeling

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  1. Computational User Intent Modeling Hongning Wang March 6, 2013

  2. Research Summary • Joint relevance and freshness learning [WWW’12] • Content-Aware Click Modeling [WWW’13] • Cross-Session Search Task Extraction [WWW’13]

  3. Understanding User Intent is Important • “Apple Company” @ Oct. 4, 2011 Release of iPhone 4S

  4. Understanding User Intent is Important • “Apple Company” @ Oct. 5, 2011 Steve Jobs passed away Release of iPhone 4S

  5. Relevance v.s. Freshness • Relevance • Topical relatedness • Metric: tf*idf, BM25, Language Model • Freshness • Temporal closeness • Metric: age, elapsed time • Trade-off • Query specific • To meet user’s information need

  6. Our Contribution Joint Relevance and Freshness Learning JRFL: (Relevance, Freshness) -> Click • Query => trade-off • URL => relevance/freshness • Click => overall impression

  7. Quantitative Comparison • Ranking performance • Random bucket clicks

  8. Content-Aware Click Modeling • Study the underlying mechanism of user clicks Freshness weight=0.8 R=0.39 F=2.34 R=1.72 F=2.18 R=2.41 F=1.76 Y=1.95 Y=2.01 Y=2.09

  9. Modeling User Clicks Match my query? Redundant doc? Shall I move on?

  10. Our Contribution Content-Aware Click Modeling • Encode rich dependency within user browsing behaviors via descriptive features Chance to further examine the result documents: e.g., position, # clicks, distance to last click Chance to click on an examined and relevant document: e.g., clicked/skipped content similarity Relevance quality of a document: e.g., ranking features

  11. Experimental Results • Take advantage of both counting-based and feature-based methods

  12. Learning to Extract Search Tasks • An atomic information need that may result in one or more queries

  13. Our Contribution Solution Heuristic constraints Structural knowledge Same task => tasks sharing related queries Latent • Identical queries • Sub-queries • Identical clicked URLs Semi-supervised Structural Learning

  14. Our Contribution Semi-supervised Structural Learning • Structural inference • Hierarchical clustering on best links • Flexibility • Exact inference exists

  15. Experimental Results

  16. plausible explanation of task structure 1ilvolo singing tous les visages de l'amour 1.1frenchversion of album by ilvolo 1.1.1frenchversion of album by ilvolo 1.1.1.1frenchversion of album by ilvolo 2 amazon.com international sites 2.1 amazon.com international 3 pottery barn warehouse clearance sale 4 amazon.com phone number 4.1 amazon.com phone number 4.1.1 amazon customer service phone number 4.1.1.1 amazon customer service phone number 5 condo rentals in salter path, n.c. 6pierobarone's 19th birthday plans 6.1pierobaronefamily 6.1.1pierobaronefamily 6.2pierobarone's 19th birthday plans 6.2.1 +pierobarone's 19th birthday plans 6.2.2pierobarone's 19th birthday plans 6.2.2.1pierobarone singing piove 6.2.2.1.1pierobarone singing piove

  17. Publications • Hongning Wang, Anlei Dong, Lihong Li, Yi Chang and EvgeniyGabrilovich. Joint Relevance and Freshness Learning From Clickthroughs for News Search. The 2012 World Wide Web Conference (WWW'2012), p579-588. • Hongning Wang, ChengXiangZhai, Anlei Dong and Yi Chang. Content-Aware Click Modeling. The 23rd International World-Wide Web Conference (WWW'2013) (To Appear) • Hongning Wang, Yang Song, Ming-Wei Chang, Xiaodong He, Ryen White and Wei Chu. Learning to Extract Cross-Session Search Tasks. The 23rd International World-Wide Web Conference (WWW'2013) (To Appear) • Yang Song, Hao Ma, Hongning Wang and Kuansan Wang. Exploring and Exploiting User Search Behaviors on Mobile and Tablet Devices to Improve Search Relevance. The 23rd International World-Wide Web Conference (WWW'2013) (To Appear) • Ryen White, Wei Chu, Ahmed Hassan, Xiaodong He, Yang Song and Hongning Wang. Enhancing Personalized Search by Mining and Modeling Task Behavior. The 23rd International World-Wide Web Conference (WWW'2013) (To Appear) • Chi Wang, Hongning Wang, Jialu Liu, Ming Ji, Lu Su, Yuguo Chen, Jiawei Han. On the Detectability of Node Grouping in Networks. SIAM International Conference on Data Mining (SDM'2013) (To Appear) • Hongbo Deng, Jiawei Han, Hao Li, HengJi, Hongning Wang and Yue Lu. Exploring and Inferring User-User Pseudo-Friendship for Sentiment Analysis with Heterogeneous Networks. SIAM International Conference on Data Mining (SDM'2013) (To Appear) • Mianwei Zhou, Hongning Wang and Kevin Chen-Chuan Chang. Learning to Rank from Distant Supervision: Exploiting Noisy Redundancy for Relational Entity Search. The 29th IEEE International Conference on Data Engineering (ICDE'2013) • Yue Lu, Hongning Wang, ChengXiangZhai and Dan Roth. Unsupervised Discovery of Opposing Opinion Networks From Forum Discussions. The 21st ACM International Conference on Information and Knowledge Management (CIKM'2012), p1642-1646.

  18. Thank you! Q&A

  19. User’s Judgment on Relevance and Freshness Freshness v.s. Relevance • User’s searching behavior Freshness weight=0.8 R=0.39 F=2.34 R=1.72 F=2.18 R=2.41 F=1.76 Y=1.95 Y=2.01 Y=2.09

  20. User Clicks Are Biased • Position-bias • Higher position • More clicks • Not necessarily relevant Modeling Clicks => Decompose relevance-driven clicks from position-driven clicks

  21. Learning to Extract Search Tasks • An atomic information need that may result in one or more queries An impression tѱ = 30 minutes

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