Triple Factor-aware Task Recommendation for Crowdsourced Q&A Services

PhD Thesis Proposal Defence

Title: "Triple Factor-aware Task Recommendation for Crowdsourced Q&A Services"


Mr. Zheng LIU


Task Recommendation (TR) is one of the most important functions for 
crowdsourced Q&A services. Specifically, given a set of tasks to be solved, TR 
identifies certain groups of workers whom are expected to give timely answers 
with high qualities, and recommend the presented tasks to corresponding 
workers. To address the TR problem, recent studies have introduced a number of 
recommendation approaches, which take advantage of workers' expertises or 
preferences towards different types of tasks. However, without a thorough 
consideration of workers' characters, such approaches will lead to either 
inadequate task fulfillment or inferior answer quality.

In this work, we propose the Triple-factor Aware Task Recommendation framework, 
which collectively considers workers' expertises, preferences and activenesses 
to maximize the overall production of high quality answers. We construct the 
Latent Hierarchical Factorization Model, which is able to infer the tasks' 
underlying categories and workers' latent characters from the historical data; 
and we propose a novel parameter inference method, which only requires the 
processing of positive instances, giving rise to significantly higher time 
efficiency and better inference quality. What's more, the online greedy and 
offline sampling-based algorithms are developed for the stream-scenario and 
batch-scenario, where tasks are processed in a stream and in a batch, 
respectively. With the adoption of both algorithms, near-optimal recommendation 
results can be acquired with considerably reduced time consumption. 
Comprehensive experiments have been carried out using both real and synthetic 
datasets, whose results verify the effectiveness and efficiency of our proposed 

Date:			Thursday, 22 March 2018

Time:                  	10:30am - 12:30pm

Venue:                  Room 3494
                         (lifts 25/26)

Committee Members:	Prof. Lei Chen (Supervisor)
  			Dr. Yangqiu Song (Chairperson)
 			Dr. Wei Wang
 			Dr. Raymond Wong

**** ALL are Welcome ****