1. Personalized Search Xiao Liu xl2230@columbia.edu

2. Background My presentation will be based on my paper “Analysis and Evaluation of Personalized Search Technologies”.

3. What’s Personalized Search? User Context Domain Context Task/Use Context Query Words Ranked List Query Words Ranked List

4. Personalization and Search  Source of personalization  How to get personalized information?  User modeling in personalized systems  How can we model a person’s interests?  Three types to implement personalized search  What are the main features for these types?  Comparison between explicit and implicit ways  What are the pros and cons for each type?

5. Sources of personalization – User data: content-based Choose right categories Mark the relevant documents – Usage data: behavior-based Click-through Selecting a particular article User Modeling in Personalized Systems Three types to implement personalized search Comparison between explicit and implicit ways Personalization and Search

7. Profile Information  Behavior-based  Click-through  Selecting an article  Content-based  Choose right categories  Mark relevant documents Server information Web page index Link graph Group behavior

8. Server-Side v. Client-Side Profile  Server-side  Pros: Access to rich Web/group information  Cons: Personal data stored by someone else  Client-side  Pros: Privacy  Cons: Need to approximate Web statistics  Hybrid solutions  Server sends necessary Web statistics  Client sends some profile information to server

9. Overview  Sources of personalization  User modeling in personalized systems  In retrieval process  Re-ranking  Query modification  Three types to implement personalized search  Comparison between explicit and implicit ways

11. Overview  Sources of personalization  User Modeling in Personalized Systems  Three types to implement personalized search  Explicit feedback personalization  Implicit feedback personalization  Combined feedback personalization  Comparison between explicit and implicit ways

12. Explicit feedback personalization  Adaptive Result Clustering – Needs external feedback – Users’ additional effort are always involved – Supports the reuse of clustering

13. Web search engine - CLUSTY

14. Web search engine - KARTOO  Organizes the returned resources on a graphic interactive map  The size of the icons corresponds to the relevance of the site to the given query  Closed down in January 2010

15. Implicit feedback personalization – Without requiring any effort from the user – Based on the user’s profile and prior behavior  Current Context  Search Histories

16. Just-in-Time IR (JITIR) based on Current Context

17. Google Web History based on Search Histories

18. Combined feedback personalization  Collaborative Search Engines – An emerging trend for Web Search

19. EUREKSTER search engine

20. Overview  Sources of personalization  User Modeling in Personalized Systems  Three types to implement personalized search  Comparison between explicit and implicit ways  Pros and cons of explicit feedback

21. Explicitly vs. Implicitly  Explicit  User shares more about query intent  User shares more about interests  Hard to express interests explicitly columbia Query Words university NYC or British? sportswear

22. Learning More Explicitly v. Implicitly  Explicit  User shares more about query intent  User shares more about interests  Hard to express interests explicitly  Implicit  Query context inferred  Profile inferred about the user  Less accurate, needs lots of data

23. Summary  Source of personalization  User data and usage data  User modeling in personalized systems  In retrieval process, re-ranking and query modification  Three types to implement personalized search  Explicit, implicit and combined  Comparison between explicit and implicit ways  Collaborative search is an emerging trend