标题:LarA: Attribute-to-feature adversarial learning for new-item recommendation    (Open Access)
作者:Sun, Changfeng ;Liu, Han ;Liu, Meng ;Ren, Zhaochun ;Gan, Tian ;Nie, Liqiang
作者机构:[Sun, Changfeng ;Liu, Han ;Liu, Meng ;Ren, Zhaochun ;Gan, Tian ;Nie, Liqiang ] School of Computer Science and Technology, Shandong University, China;[ 更多
会议名称:13th ACM International Conference on Web Search and Data Mining, WSDM 2020
会议日期:February 3, 2020 - February 7, 2020
来源:WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining
出版年:2020
页码:582-590
DOI:10.1145/3336191.3371805
摘要:Recommending new items in real-world e-commerce portals is a challenging problem as the cold start phenomenon. To address this problem, we propose a novel recommendation model, i.e., adversarial neural network with multiple generators, to generate users from multiple perspectives of items’ attributes. Namely, the generated users are represented by attribute-level features. As both users and items are attribute-level representations, we can implicitly obtain user-item attribute-level interaction information. In light of this, the new item can be recommended to users based on attribute-level similarity. Extensive experimental results on two item cold-start scenarios, movie and goods recommendation, verify the effectiveness of our proposed model as compared to state-of-the-art baselines.
© 2020 Association for Computing Machinery.
收录类别:EI
资源类型:会议论文
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