{"type":"ScholarlyArticle","title":"Interest Inference via Structure-Constrained Multi-Source Multi-Task Learning","authors":[{"name":"Xuemeng Song","slug":"xuemeng-song","given":"Xuemeng","family":"Song","affiliation":""},{"name":"Liqiang Nie","slug":"liqiang-nie","given":"Liqiang","family":"Nie","affiliation":""},{"name":"Luming Zhang","slug":"luming-zhang","given":"Luming","family":"Zhang","affiliation":""},{"name":"Maofu Liu","slug":"maofu-liu","given":"Maofu","family":"Liu","affiliation":""},{"name":"Tat-Seng Chua","slug":"tat-seng-chua","given":"Tat-Seng","family":"Chua","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2015/335","pdf":"https://ijcai.org/proceedings/2015/0335.pdf","markdown":"https://ijcai.org/proceedings/2015/335.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/335","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Web Mining","track":"Main Track","number":335,"pages":{"first":2371,"last":2376},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}