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Enhancing Web Search Result Preference Prediction through User Interaction Models

This research investigates user interaction models to improve web search result preferences. The authors propose a comprehensive set of features to effectively represent user behavior, incorporating elements like query text, browsing patterns, and clickthrough data. The study highlights the predictive power of metrics such as reading time and scrolling behaviors in forecasting user interest. Utilizing a dataset of 3,500 queries and their top results, this work contrasts traditional ranking algorithms with new user interaction-based techniques, showcasing the potential for improved precision in search outcomes.

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Enhancing Web Search Result Preference Prediction through User Interaction Models

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  1. Learning User Interaction Models for Predicting Web Search Result Preference Eugene Agichtein et al. Microsoft Research SIGIR ‘06

  2. Objective • Provide a rich set of features for representing user behavior • Query-text • Browsing • Clickthough • Aggregate various feature • RankNet

  3. Browsing feature • Related work • The amount of reading time could predict • interest level on news articles • rating in recommender system • The amount of scrolling on a page also have strong relationship with interest

  4. Browsing feature • How to collect browsing feature? • Obtain the information via opt-in client-side instrumentation

  5. Browsing feature • Dwell time

  6. Browsing feature • Average & Deviation • Properties of the click event

  7. Clickthrough feature • 1. Clicked VS. Unclicked • Skip Above (SA) • Skip Next (SN) • Advantage • Propose preference pair • Disadvantage • Inconsistency • Noisiness of individual

  8. Clickthrough feature • 2. Position-biased

  9. Clickthrough feature

  10. Clickthrough feature

  11. Clickthrough feature • Disadvantage of SA & SN • User may click some irrelevant pages

  12. Clickthrough feature • Disadvantage of SA & SN • User often click part of relevant pages

  13. Clickthrough feature • 3. Feature for learning

  14. Feature set

  15. Feature set

  16. Evaluation • Dataset • Random sample 3500 queries and their top 10 results • Rate on a 6-point scale manually • 75% training, 25% testing • Convert into pairwise judgment • Remove tied pair

  17. Evaluation • Pairwise judgment • Input • UrlA, UrlB • Outpur • Positive: rel(UrlA) > rel(UrlB) • Negative: rel(UrlA) ≤ rel(UrlB) • Measurement • Average query precision & recall

  18. Evaluation 1. Current • Original rank from search engine • 2. Heuristic rule without parameter • SA, SA+N • 3. Heuristic rule with parameter • CD, CDiff, CD + CDiff • 4. Supervised learning • RankNet

  19. Evaluation

  20. Evaluation

  21. Evaluation

  22. Conclusion • Recall is not a important measurement • Heuristic rule • very low recall and low precision • Feature set • Browsing features have higher precision

  23. Discussion • Is user interaction model better than search engine • Small coverage • Only pairwise judgment • Given the same training data, which one is better, traditional ranking algorithm or user interaction? • Which feature is more useful?

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