Title: Joint modelling of task requirements and worker preferences based on heterogeneous features and multiple interactions for knowledge-intensive crowdsourcing recommendation

Authors: Biyu Yang; Xu Wang; Shuai Zhang; Min Gao; Jiejie Tian; Guangzhu Tan; Linda Yang; Jiafu Su

Addresses: College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, China ' College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, China ' School of Big Data and Software Engineering, Chongqing University, Chongqing, 400044, China ' School of Big Data and Software Engineering, Chongqing University, Chongqing, 400044, China ' School of Data Science, City University of Hong Kong, Hong Kong, 999077, China ' Big Data Department, Zhubajie Co. Ltd (ZBJ.COM), Chongqing, 401120, China ' School of Engineering, University of Portsmouth, Portsmouth, PO1 3HE, UK ' International College, Krirk University, Bangkok 10220, Thailand

Abstract: Automatic worker recommendation has become a key technology in knowledge-intensive crowdsourcing (KIC). However, KIC recommendation encounters the task cold-start problem in nature as only newly posted tasks need to be matched with workers. Current studies fail to accurately model both tasks and workers in the task cold-start scenario, and ignore the problem of task clarity in task requirements understanding and treat task features linearly in worker preferences estimation. Therefore, this paper proposed a heterogeneous features and multiple interactions-based deep neural framework (called HFMIRec) to assist new task completion more smartly in KIC. Specifically, different types of task features can be flexibly incorporated to tackle the cold-start problem. To accurately model both tasks and workers, multiple interactions between tasks and workers are identified and learned by attentive neural networks in the framework. Extensive experiments on a real-world dataset demonstrate the effectiveness of the proposed model.

Keywords: crowdsourcing; task cold-start; worker recommendation; supply-demand matching; recommender system.

DOI: 10.1504/IJBIC.2023.134974

International Journal of Bio-Inspired Computation, 2023 Vol.22 No.2, pp.105 - 116

Received: 19 Jul 2022
Accepted: 21 Apr 2023
Published online: 22 Nov 2023 *

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