{"type":"ScholarlyArticle","title":"Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust","authors":[{"name":"Qitian Wu","slug":"qitian-wu","given":"Qitian","family":"Wu","affiliation":"Shanghai Key Laboratory of Scalable Computing and Systems , Department of Computer Science and Engineering , Shanghai Jiao Tong University"},{"name":"Lei Jiang","slug":"lei-jiang","given":"Lei","family":"Jiang","affiliation":"Shanghai Key Laboratory of Scalable Computing and Systems , Department of Computer Science and Engineering , Shanghai Jiao Tong University"},{"name":"Xiaofeng Gao","slug":"xiaofeng-gao","given":"Xiaofeng","family":"Gao","affiliation":"Shanghai Key Laboratory of Scalable Computing and Systems , Department of Computer Science and Engineering , Shanghai Jiao Tong University"},{"name":"Xiaochun Yang","slug":"xiaochun-yang","given":"Xiaochun","family":"Yang","affiliation":"Northeastern University"},{"name":"Guihai Chen","slug":"guihai-chen","given":"Guihai","family":"Chen","affiliation":"Shanghai Key Laboratory of Scalable Computing and Systems , Department of Computer Science and Engineering , Shanghai Jiao Tong University"}],"abstract":"Social recommendation could address the data sparsity and cold-start problems for collaborative filtering by leveraging user trust relationships as auxiliary information for recommendation. However, most existing methods tend to consider the trust relationship as preference similarity in a static way and model the representations for user preference and social trust via a common feature space. In this paper, we propose TrustEV and take the view of multi-task learning to unite collaborative filtering for recommendation and network embedding for user trust. We design a special feature evolution unit that enables the embedding vectors for two tasks to exchange their features in a probabilistic manner, and further harness a meta-controller to globally explore proper settings for the feature evolution units. The training process contains two nested loops, where in the outer loop, we optimize the meta-controller by Bayesian optimization, and in the inner loop, we train the feedforward model with given feature evolution units. Experiment results show that TrustEV could make better use of social information and greatly improve recommendation MAE over state-of-the-art approaches.","doi":"10.24963/ijcai.2019/538","url":"https://ijcai.org/proceedings/2019/538","pdf":"https://ijcai.org/proceedings/2019/0538.pdf","markdown":"https://ijcai.org/proceedings/2019/538.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/538","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":538,"pages":{"first":3877,"last":3883},"keywords":["Machine Learning: Recommender Systems","Machine Learning Applications: Networks","Multidisciplinary Topics and Applications: Social Sciences","Machine Learning: Learning Preferences or Rankings","Machine Learning: Transfer, Adaptation, Multi-task Learning"],"videos":[],"references":[{"position":1,"raw":"[Guo et al., 2015] Guibing Guo, Jie Zhang, and Neil Yorke- Smith. 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