Talents Recommendation with Multi-Aspect Preference Learning

Discovering talents has always been a crucial mission in recruitment and applicant selection program. Traditionally, hunting and identifying the best candidate for a particular job is executed by specialists in human resources department, which requires complex manual data collection and analysis. In this paper, we propose to seek talents for companies by leveraging a variety of data from not only online professional networks (e.g., LinkedIn), but also other popular social networks (e.g., Foursquare and Last.fm). Specifically, we extract three distinct features, namely global, user and job preference to understand the patterns of talent recruitment, and then a Multi-Aspect Preference Learning (MAPL) model for applicant recommendation is proposed. Experimental results based on a real-world dataset validate the effectiveness and usability of our proposed method, which can achieve nearly 75% accuracy at best in recommending candidates for job positions.