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BDAI重点实验室研究生沙龙第28期:Learning Explicit User Interest Boundary for Recommendation
日期:2022-05-23访问量:

大数据管理与分析方法研究北京市重点实验室(BDAI)研究生沙龙由beat365体育亚洲官网在线下载师生组织定期举行。5月25日研讨会由学院博士后朱倩男和准聘助理教授周骁老师指导的学生马逸君(工作在投)介绍自己的研究工作。欢迎同学们积极参与研讨!

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报告标题:Learning Explicit User Interest Boundary for Recommendation

报告人:朱倩男,高瓴人工智能学院,博士后

研究方向:推荐系统、知识图谱表示与推理、信息检索

摘要:The core objective of modelling recommender systems from implicit feedback is to maximize the positive sample score sp and minimize the negative sample score sn, which can usually be summarized into two paradigms: the pointwise and the pairwise. The pointwise approaches fit each sample with its label individually, which is flexible in weighting and sampling on instance-level but ignores the inherent ranking property. By qualitatively minimizing the relative score sn - sp , the pairwise approaches capture the ranking of samples naturally but suffer from training efficiency. Additionally, both approaches are hard to explicitly provide a personalized decision boundary to determine if users are interested in items unseen. To address those issues, we innovatively introduce an auxiliary score bu for each user to represent the User Interest Boundary (UIB) and individually penalize samples that cross the boundary with pairwise paradigms, i.e., the positive samples whose score is lower than bu and the negative samples whose score is higher than bu. In this way, our approach successfully achieves a hybrid loss of the pointwise and the pairwise to combine the advantages of both. Analytically, we show that our approach can provide a personalized decision boundary and significantly improve the training efficiency without any special sampling strategy. Extensive results show that our approach achieves significant improvements on not only the classical pointwise or pairwise models but also state-of-the-art models with complex loss function and complicated feature encoding.

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