Personalized SearchXiao Liu

xl2230@columbia.edu

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