[1]鲍维克,袁春.面向推荐系统的分期序列自注意力网络[J].智能系统学报,2021,16(2):353-361.[doi:10.11992/tis.202005028]
BAO Weike,YUAN Chun.Recommendation system with long-term and short-term sequential self-attention network[J].CAAI Transactions on Intelligent Systems,2021,16(2):353-361.[doi:10.11992/tis.202005028]
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BAO Weike,YUAN Chun.Recommendation system with long-term and short-term sequential self-attention network[J].CAAI Transactions on Intelligent Systems,2021,16(2):353-361.[doi:10.11992/tis.202005028]
面向推荐系统的分期序列自注意力网络
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第2期
页码:
353-361
栏目:
学术论文—人工智能基础
出版日期:
2021-03-05
- Title:
- Recommendation system with long-term and short-term sequential self-attention network
- Keywords:
- recommendation system; sequence recommendation; attention model; dynamic weighting; self-attention model; sequence dependence; GRU; sequential preference
- 分类号:
- TP391
- 摘要:
- 在推荐系统中，为了充分表达用户反馈数据内部的相互依赖和序列性，准确提取用户的长期/一般偏好、应对数据的动态性，本文提出了一种分期序列自注意力网络(long-term & short-term sequential self-attention network，LSSSAN)进行序列推荐。模型采用自注意力机制和GRU捕捉了用户反馈数据之间的相互依赖和序列性；模型采用注意力机制为不同反馈数据赋予不同权重以动态捕捉重点信息，同时考虑了上下文的动态性；模型基于用户的长期反馈数据，准确表达了用户的长期/一般偏好。该模型在两个数据集上进行训练和测试，结果表明该模型的推荐效果整体优于之前的相关工作。
- Abstract:
- To fully express the internal interdependence, user interaction data sequentiality, and long-term or general preferences and deal with the dynamics of data, this paper proposes the long-term and short-term sequential self-attention network (LSSAN) for sequential recommendation in the recommendation system, and the LSSSAN model. This model uses self-attention and a GRU to capture the dependence and sequentiality among the user’s data. Moreover, the model uses Attention Net to combine user characteristics and the candidate item set for recommendation as context for capturing the dynamics of the recommendation task. The model accurately expresses the general preferences of users based on their long-term interaction data. We train and test the LSSSAN on two data sets, and its effect is generally better than that of the previous work.
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备注/Memo
收稿日期:2020-05-21。
作者简介:鲍维克，硕士研究生，主要研究方向为推荐系统;袁春，副研究员，博士，博士生导师，IEEE高级会员，清华大学?香港中文大学媒体科学、技术与系统联合研究中心常务副主任，主要研究方向为机器学习、计算机视觉。发表学术论文100余篇
通讯作者:袁春.E-mail:yuanc@sz.tsinghua.edu.cn
更新日期/Last Update:
2021-04-25