Computational User Intent ModelingHongning WangMarch 6, 2013

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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

Release of iPhone 4S

Understanding User Intent is Important

• “Apple Company” @ Oct. 5, 2011

Steve Jobs passed away

Release of iPhone 4S

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

Joint Relevance and Freshness Learning

• JRFL: (Relevance, Freshness) -> Click

Query => trade-off

URL => relevance/freshness

Click => overall impression

Our Contribution

Quantitative Comparison

• Ranking performance– Random bucket clicks

Content-Aware Click Modeling

• Study the underlying mechanism of user clicksFreshness weight=0.8

R=0.39F=2.34 Y=1.95

R=1.72F=2.18 Y=2.01

R=2.41F=1.76 Y=2.09

Modeling User Clicks

Match my query?

Redundant doc?

Shall I move on?

Content-Aware Click Modeling

• Encode rich dependency within user browsing behaviors via descriptive features

Relevance quality of a document: e.g., ranking 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

Our Contribution

Experimental Results

• Take advantage of both counting-based and feature-based methods

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• An atomic information need that may result in one or more queries

Learning to Extract Search Tasks

5/29/2012 S15/29/2012 5:26 bank of america

5/29/2012 S25/29/2012 11:11 macy's sale5/29/2012 11:12 sas shoes

5/30/2012 S15/30/2012 10:19 credit union

5/30/2012 S25/30/2012 12:25 6pm.com5/30/2012 12:49 coupon for 6pm shoes

5/29/2012 S15/29/2012 5:26 bank of america

5/29/2012 S25/29/2012 11:11 macy's sale5/29/2012 11:12 sas shoes

5/30/2012 S15/30/2012 10:19 credit union

5/30/2012 S25/30/2012 12:25 6pm.com5/30/2012 12:49 coupon for 6pm shoes

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Solution

Heuristic constraints• Identical queries• Sub-queries• Identical clicked URLs

Structural knowledge• Same task => tasks sharing

related queries• Latent

Semi-supervised Structural Learning

Our Contribution

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Semi-supervised Structural Learning

• Structural inference– Hierarchical clustering on best links• Flexibility• Exact inference exists

Our Contribution

Experimental Results

1 il volo singing tous les visages de l'amour1.1 french version of album by il volo

1.1.1 french version of album by il volo1.1.1.1 french version of album by il volo

2 amazon.com international sites2.1 amazon.com international

3 pottery barn warehouse clearance sale4 amazon.com phone number

4.1 amazon.com phone number4.1.1 amazon customer service phone number

4.1.1.1 amazon customer service phone number5 condo rentals in salter path, n.c.6 piero barone's 19th birthday plans

6.1 piero barone family 6.1.1 piero barone family

6.2 piero barone's 19th birthday plans 6.2.1 +piero barone's 19th birthday plans 6.2.2 piero barone's 19th birthday plans

6.2.2.1 piero barone singing piove 6.2.2.1.1 piero barone singing piove

plausible explanation of task structure

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Publications1. Hongning Wang, Anlei Dong, Lihong Li, Yi Chang and Evgeniy Gabrilovich. Joint Relevance and Freshness

Learning From Clickthroughs for News Search. The 2012 World Wide Web Conference (WWW'2012), p579-588.2. Hongning Wang, ChengXiang Zhai, Anlei Dong and Yi Chang. Content-Aware Click Modeling. The 23rd

International World-Wide Web Conference (WWW'2013) (To Appear)3. 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)4. 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)

5. 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)

6. 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)

7. Hongbo Deng, Jiawei Han, Hao Li, Heng Ji, 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)

8. 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)

9. Yue Lu, Hongning Wang, ChengXiang Zhai 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.

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Thank you!• Q&A

• User’s searching behaviorFreshness weight=0.8

R=0.39F=2.34 Y=1.95

R=1.72F=2.18 Y=2.01

R=2.41F=1.76 Y=2.09

Freshness v.s.

Relevance

User’s Judgment on Relevance and Freshness

User Clicks Are Biased

• Position-bias– Higher positionÞMore clicksÞNot necessarily relevant

Modeling Clicks=> Decompose relevance-driven clicks from position-driven clicks

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Learning to Extract Search Tasks

• An atomic information need that may result in one or more queries

tѱ = 30 minutes

An impression