An Associative Characterization of Click Models in Web Search

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering

PhD Thesis Defence

Title: "An Associative Characterization of Click Models in Web Search"


Mr. Weizhu Chen


Web search has become a fundamental means for massive users to find 
information. As a result, huge amounts of user interaction data are generated 
and acting as a valuable source for many web tasks. An important task is to 
understand user preference of each query-document pair based on their click 
behavior, so as to allow search engines to deliver better search results to 
effectively serve their users. In this thesis, we study the problem of modeling 
user click behavior in Web search, which is often formalized as a click model 
problem. Click models can automatically infer user-perceived relevance for each 
search result. This in turn enforces the search engines to deliver better 
search results.

In the context of a click, there are multiple objects: user, query, session, 
task, search result, page region, etc. Previous models generally treat each 
object in isolation, disregarding their associations by only considering 
individual queries and search results. This may bring an over-simplification to 
a model but sacrifice valuable associative information. The main contribution 
of this thesis is a family of models and algorithms to address these 
limitations via modeling the associations between these objects. The proposed 
model and algorithm family characterizes the associations from six facets. We 
first put forward a whole-page model to describe the interplay between organic 
search and sponsored search. We then propose a session-based model and an 
intent-bias model to study multiple queries with their corresponding clicks 
collectively. We then introduce a user-based model to complement query and 
document with user and characterize this triple relationship. We continue with 
a novel noise-aware model to capture the noise of a click by leveraging above 
objects as its context. Finally, we provide a new solution to combine multiple 
proposed click models together and solve a relevance prediction challenge. 
Furthermore, we verify all the proposed models through extensive experiments 
using large-scale data collected from a commercial search engine. Experimental 
results demonstrate the significant improvements over the state-of-the-art.

Date:			Thursday, 2 August 2012

Time:			10:00am – 12:00noon

Venue:			Room 3501
 			Lifts 25/26

Chairman:		Prof. Chi-Ming Chan (ENVR)

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Prof. Dik-Lun Lee
 			Prof. Dit-Yan Yeung
 			Prof. Rong Zheng (ISOM)
                         Prof. Michael Lyu (Comp. Sci. & Engg., CUHK)

**** ALL are Welcome ****