Interest Inference via Structure-Constrained Multi-Source Multi-Task Learning

User interest inference from social networks is a fundamental problem to many applications. It usually exhibits dual-heterogeneities: a user’s interests are complementarily and comprehensively reflected by multiple social networks; interests are inter-correlated in a nonuniform way rather than independent to each other. Although great success has been achieved by previous approaches, few of them consider these dual-heterogeneities simultaneously. In this work, we propose a structure-constrained multi-source multi-task learning scheme to co-regularize the source consistency and the tree-guided task relatedness. Meanwhile, it is able to jointly learn the tasksharing and task-specific features. Comprehensive experiments on a real-world dataset validated our scheme. In addition, we have released our dataset to facilitate the research communities.

Authors

• Xuemeng Song Shandong University
• Liqiang Nie Harbin Institute of Technology (Shenzhen)
• Luming Zhang
• Maofu Liu
• Tat-Seng Chua National University of Singapore

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