New Perspectives on Search Click Modeling

MPhil Thesis Defence


Title: "New Perspectives on Search Click Modeling"

By

Miss Si Shen


Abstract

Click modeling aims to interpret the users' search click data in order to 
predict their clicking behavior. Existing models can well characterize the 
position bias of documents and snippets in relation to users' mainstream 
click behavior in either organic search block or ads block. Yet, current 
advances limit their focus solely on position bias, while click modeling 
possesses the potential as a much wider topic. In this thesis, we propose 
two directions of extending existing click model works: (1) expanding 
query-document relevance score with a user dimension, hence personalized 
click models capturing user intrinsic preferences by matrix and tensor 
factorization; and (2) using previous click models as a micro layer for 
each user click out of a macro click chain, which includes search click 
logs for every click-able block on a whole search result page. Either one 
of our perspectives on search click modeling produces a general framework 
that could incorporate existing click models and remains valid for 
possible future developments on position bias depiction. We verify both 
models through extensive experiments using large-scale data collected from 
a real search engine, and their improvements over current models are 
significant. In addition, our models are very capable of handling 
challenging problems in the literature, including prediction on rare 
queries and ads click interpretation, which may offer inspirations for 
future research.


Date:			Tuesday, 24 April 2012

Time:			9:30am – 11:30am

Venue:			Room 3311
 			Lifts 17/18

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Prof. Dit-Yan Yeung (Chairperson)
 			Dr. Raymond Wong


**** ALL are Welcome ****