Work-force Recommendation for Collaborative Labor Market

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


PhD Thesis Defence


Title: "Work-force Recommendation for Collaborative Labor Market"

By

Mr. Zheng LIU


Abstract

Collaborative Labor Market is the underlying paradigm for a large number of 
popular web services. By introducing the power of online crowd, many 
far-reaching real-world applications, such as crowdsourced question answering 
and ride-sharing, are now effectively conducted at low cost. Despite the 
current flourish of collaborative labor market, the Quality of Service (QoS) 
and Throughput of Service (ToS) remain its central issues. Motivated by such a 
point, we target on addressing the above issues from the perspective of 
work-force recommendation. Particularly, by allocating appropriate work-force 
for people's demands in collaborative labor market, quality service can be 
timely generated at high throughput rate.

In our work, work-force recommendation strategies are studied in depth for the 
following two application scenarios.

First of all, we study the application of crowdsourced Q\&A services, where 
workers need to be recommended for people's questions of interest. Given such a 
problem, we come up with the triple-factor aware approach, which characterizes 
workers with their activeness, preference and expertise. On top of the above 
factors, worker recommendation is judiciously generated to maximize the timely 
acquisition of high-quality answer. According to experimental studies on the 
Stack Overflow dataset, the exploitation of triple-factor significantly 
improves the recommendation effectiveness in terms of answer quality and 
throughput.

Secondly, we work on the application of context-aware academic collaborator 
recommendation, where new potential collaborators are suggested w.r.t. people's 
interested research topics. Inspired by the success of representative learning 
on graph, we come up with the collaborative entity embedding network, which 
deeply excavates the researchers' relationship in academia and research topics' 
semantic meaning. To further improve the performance in finding new 
collaborators, we propose a probabilistic graphical model to take advantage of 
researchers' inherent activeness and conservativeness. With experimental 
studies on the Aminer dataset, it is verified that the effectiveness of finding 
academic collaborators is greatly enhanced with our proposed mechanisms.


Date:			Tuesday, 31 July 2018

Time:			2:30pm - 4:30pm

Venue:			Room 5560
 			Lifts 27/28

Chairman:		Prof. Yang Wang (MATH)

Committee Members:	Prof. Lei Chen (Supervisor)
 			Prof. Dik-Lun Lee
 			Prof. Raymond Wong
 			Prof. Jiheng ZHANG (IEDA)
 			Prof. Xiaokui XIAO (National Univ of Singapore)


**** ALL are Welcome ****