---
title: "Interest Inference via Structure-Constrained Multi-Source Multi-Task Learning"
authors:
  - "Xuemeng Song"
  - "Liqiang Nie"
  - "Luming Zhang"
  - "Maofu Liu"
  - "Tat-Seng Chua"
doi: ""
url: "https://ijcai.org/proceedings/2015/335"
pdf: "https://ijcai.org/proceedings/2015/0335.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "2371-2376"
track: "Main Track"
keywords:
---

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

Xuemeng Song, Liqiang Nie, Luming Zhang, Maofu Liu, Tat-Seng Chua

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 2371-2376.

## Abstract

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 task-sharing 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.