Visual Analytics of User Behavior from Web Log Data

PhD Thesis Proposal Defence


Title: "Visual Analytics of User Behavior from Web Log Data"

by

Mr. Conglei SHI


Abstract:

With the decreasing cost and the increasing storage capacity, more web log data 
can be recorded nowadays. Compared with the data collected from experiments, 
the log data can more accurately reflect the user behavior with little bias. 
These data provide an opportunity to understand user behavior and help improve 
user experience. For example, by analyzing how users use search engines and why 
they are not satisfied with the search result, we can improve the usability of 
search engines, such as search personalization and search accuracy. However, 
analyzing the log data is challenging. For instance, exploring the raw data is 
an essential step to formulate hypotheses and build models, but the log data 
size is large and increases over time. Visual analytic methods, in the case, 
can greatly help explore and analyze the data, because using visualization 
components enables to express a large amount of information in a very efficient 
and intuitive way, since human perceptual system can process visual information 
rapidly, and it can help start analysis without assumptions.

In this thesis, we focus on two types of log data. The first one is the search 
log data, which record how users use different search engines to perform 
queries and is collected from a world wide distributed web browser. The second 
one is the learning log data from a Massive Open Online Courses (MOOCs) 
platform. In order to better understand the actual needs when analyzing the log 
data, we conducted several rounds of interviews with domain experts who are the 
end users of visual analytics systems. After that, we follow the user centered 
design and iteratively design three analytics systems. In the first system, 
RankExplorer, we present a new visualization technique to intuitively show the 
ranking changes of queries in search log data. In the second system, 
LoyalTracker, we target on better understanding user loyalty and defection 
behavior in search log data. In the third system, VisMOOC, we focus on 
analyzing learning behavior through learning log data. All the three systems 
give domain experts new insights into user behavior. In order to validate the 
effectiveness and usefulness of proposed systems, we conducted case studies 
with domain experts and one user study for RankExplorer.


Date:			Thursday, 12 June 2014

Time:                   10:00am - 12:00noon

Venue:                  Room 3494
                         lifts 25/26

Committee Members:	Dr. Huamin Qu (Supervisor)
 			Prof. Long Quan (Chairperson)
 			Prof. Chiew-Lan Tai
 			Prof. Dit-Yan Yeung


**** ALL are Welcome ****