personalized search xiao liu [email protected]
TRANSCRIPT
Background
My presentation will be based on my paper “Analysis and Evaluation of Personalized Search Technologies”.
What’s Personalized Search?
User Context
Domain Context
Task/Use Context
Query Words
Ranked List
Query Words
Ranked List
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?
• 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
MatchingImplicit Feedback
Explicit Feedback
User Profile
Web Information Representation
Personalized ResultsUsage Data
User Data
Web Pages, Documents
User Involved
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
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
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
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
Explicit feedback personalization
Adaptive Result Clustering– Needs external feedback– Users’ additional effort are always
involved– Supports the reuse of clustering
Web search engine - CLUSTY
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
Implicit feedback personalization
– Without requiring any effort from the user
– Based on the user’s profile and prior behavior
Current Context Search Histories
Just-in-Time IR (JITIR) based on Current
Context
Google Web History based on Search
Histories
Combined feedback personalization
Collaborative Search Engines– An emerging trend for Web Search
EUREKSTER search engine
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
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
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
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