[1]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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Recommendation system with long-term and short-term sequential self-attention network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 2 Page number: 353-361 Column: 学术论文—人工智能基础 Public date: 2021-03-05
Title:
Recommendation system with long-term and short-term sequential self-attention network
Author(s):
BAO Weike1;  YUAN Chun2
1. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
2. Shenzhen International Graduate School, Tsinghua University, Shenzhen 518000, China
Keywords:
recommendation system; sequence recommendation; attention model; dynamic weighting; self-attention model; sequence dependence; GRU; sequential preference
CLC:
TP391
DOI:
10.11992/tis.202005028
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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