{"type":"ScholarlyArticle","title":"A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation","authors":[{"name":"Feng Zhu","slug":"feng-zhu","given":"Feng","family":"Zhu","affiliation":"Department of Computing , Macquarie University , Sydney , NSW 2109 , Australia"},{"name":"Yan Wang","slug":"yan-wang","given":"Yan","family":"Wang","affiliation":"Department of Computing , Macquarie University , Sydney , NSW 2109 , Australia"},{"name":"Chaochao Chen","slug":"chaochao-chen","given":"Chaochao","family":"Chen","affiliation":"Ant Financial Services Group , Hangzhou 310012 , China"},{"name":"Guanfeng Liu","slug":"guanfeng-liu","given":"Guanfeng","family":"Liu","affiliation":"Department of Computing , Macquarie University , Sydney , NSW 2109 , Australia"},{"name":"Xiaolin Zheng","slug":"xiaolin-zheng","given":"Xiaolin","family":"Zheng","affiliation":"College of Computer Science and Technology , Zhejiang University , Hangzhou 310027 , China"}],"abstract":"The conventional single-target Cross-Domain Recommendation (CDR) only improves the recommendation accuracy on a target domain with the help of a source domain (with relatively richer information). In contrast, the novel dual-target CDR has been proposed to improve the recommendation accuracies on both domains simultaneously. However, dual-target CDR faces two new challenges: (1) how to generate more representative user and item embeddings, and (2) how to effectively optimize the user/item embeddings on each domain. To address these challenges, in this paper, we propose a graphical and attentional framework, called GA-DTCDR. In GA-DTCDR, we first construct two separate heterogeneous graphs based on the rating and content information from two domains to generate more representative user and item embeddings. Then, we propose an element-wise attention mechanism to effectively combine the embeddings of common users learned from both domains. Both steps significantly enhance the quality of user and item embeddings and thus improve the recommendation accuracy on each domain. Extensive experiments conducted on four real-world datasets demonstrate that GA-DTCDR significantly outperforms the state-of-the-art approaches.","doi":"10.24963/ijcai.2020/415","url":"https://ijcai.org/proceedings/2020/415","pdf":"https://ijcai.org/proceedings/2020/0415.pdf","markdown":"https://ijcai.org/proceedings/2020/415.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/415","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":415,"pages":{"first":3001,"last":3008},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Recommender Systems"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24167/file_24167.m3u8","length_sec":301,"provider":"hls"}],"references":[{"position":1,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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