Personalized Search

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

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. Personalization and Search • 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

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

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 Query Words columbia university sportswear NYC or British? • Explicit • User shares more about query intent • User shares more about interests • Hard to express interests explicitly

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