1. Author: Kazunari Sugiyama, etc. (WWW2004)
Adaptive Web Search Based On User Profile Constructed Without Any Effort from Users
Author: Kazunari Sugiyama, etc. (WWW2004)
Presenter: Xuehua Shen
2018/11/26
Xuehua UIUC

2. Presentation Layout Problem Description Related Work
User Profile Construction
Experiment Design
Experiment Result
Discussion
2018/11/26
Xuehua UIUC

3. Problem Description
Problem: improve relevance of search engine results.
From one size fits all to personalization.
User profile to do query expansion/result reranking. But the user do NOT want to spend efforts on user profile construction.
Construct user profile implicitly.
How to effectively construct user profile implicitly
2018/11/26
Xuehua UIUC

4. Related Work Personalized PageRank [Haveliwala WWW02], [Jeh WWW03]
Server side personalization
Assume there is a user profile (and long-term context)
Personalized Websites (e.g., My Yahoo!)
The user explicitly inputs the user profile
Recommendation System (e.g., Amazon)
Collaborative Filtering
The system uses the user’s implicit feedback
2018/11/26
Xuehua UIUC

5. General Description Client side personalization
Privacy
More user personal information
No global picture
User profile movement
Construct user profile from implicit feedback ( web page browsed)
Without any effort from users
Quality of implicit feedback?
Result reranking
Can also do query expansion
2018/11/26
Xuehua UIUC

6. System Overview
No real system
2018/11/26
Xuehua UIUC

7. User Profile Information Source: browsing history
Only web pages, no other information used
Browsed web pages -> preferred? (vs. clickthrough)
Persistent preference vs. ephemeral preference
i days ago, today and current information session (session boundary detection?)
2018/11/26
Xuehua UIUC

8. User Profile Figure
2018/11/26
Xuehua UIUC

9. User Profile cont. Representation: one term weight vector
Multiple term vectors to represent different topics [Cetintemel, etc ICDE2000]
Term vector computation (online computation?) and maintenance (when to update)
Usage: reranking of search results
Cosine similarity (user profile, result summary)
2018/11/26
Xuehua UIUC

10. User Profile Construction
Two methods
Pure personal browsing history
Collaborative filtering
browsed web pages of the Group (share browsing history?)
Smooth term weights only for missing terms of the current session using term weights of other users and correlation with others
2018/11/26
Xuehua UIUC

11. Method 1: Pure Personal Profile
For each web page, construct a term probability vector based on Maximum Likelihood estimator
Only use web pages on which the user spent enough time
2018/11/26
Xuehua UIUC

12. Term Vector of Current Session
For term probability vectors in current session, average them, get P(cur)
2018/11/26
Xuehua UIUC

13. Term Vector of Today
For term probability vectors of today, first average term probability vector in the same session, then do a summation over different session of today, get P(br)
2018/11/26
Xuehua UIUC

14. Term vector of Persistent Preference
For term probability vector 1…N days ago, compute the time-dacay average of term probability vectors, get P(per)
2018/11/26
Xuehua UIUC

15. Term vector of User Profile
Linear interpolation of P(cur) , P(br) and P(per)
2018/11/26
Xuehua UIUC

16. Method 2: Collaborative Filtering
Similarity of users are computed through Pearson correlation of corresponding term weight vectors
Not clear which term vectors (P(cur) , P(br) or P(per) ?) are used
Is it reasonable to use Pearson correlation?
2018/11/26
Xuehua UIUC

17. Compute Term Weight Select n term vectors
Static: n term vectors of neighboring (similar) users
Dynamic: do KNN clustering, select n cluster centroid term vectors
Smooth term weight for missing terms according to weighted average of selected n term vector V(pre) (replace P(cur) )
2018/11/26
Xuehua UIUC

18. Experiment Design TREC WT10g topic, WWW as text database
(not R-precison) as evaluation metric
Subject judge relevance of top 30 documents
Compare performance results using relevance feedback, pure browsing history and collaborative filtering (static and dynamic)
2018/11/26
Xuehua UIUC

19. Experiment Results
The performance of using user profile is competitive with that using relevance feedback
Current session history much more useful than browsing history today
Persistent term vectors matters, 18 days is optimal
Dynamic collaborative filtering seems to be better than static collaborative filtering
2018/11/26
Xuehua UIUC

20. Experiment Results Figures
2018/11/26
Xuehua UIUC

21. Pro. and Con.
Propose 3 algorithms for constructing user profiles from browsing history, which is proved to be effective by experiments
Efficiency issues: online result reranking
How to share personal data
Browsing history is not best information source
User profile as one term vector is too simple
No study for user profile maintenance
2018/11/26
Xuehua UIUC

22. Thank you!
2018/11/26
Xuehua UIUC