Computer Science and Information Systems 2024 Volume 21, Issue 1, Pages: 1-20
https://doi.org/10.2298/CSIS230522067G
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FSASA: Sequential recommendation based on fusing session-aware models and self-attention networks
Guo Shangzhi (College of Computer Science, Chongqing University Chongqing, China), 20211401018g@cqu.edu.cn
Liao Xiaofeng (College of Computer Science, Chongqing University Chongqing, China), xfliao@cqu.edu.cn
Meng Fei (College of Computer Science, Chongqing University Chongqing, China), 20211401024g@cqu.edu.cn
Zhao Qing (College of Computer Science, Chongqing University Chongqing, China), 20211401020g@cqu.edu.cn
Tang Yuling (Hunan Creator Information Technologies CO., LTD. Changsha, China), yuling.tang@chinacreator.com
Li Hui (Jiangxi Institute of Land and Space Survey and Planning Nanchang, China), lihcool@.com
Zong Qinqin (Jiangxi Biological Vocational College Nanchang, China), honeybabyqinqin@gmail.com
The recommendation system can alleviate the problem of “information overload”, tap the potential value of data, push personalized information to users in need, and improve information utilization. Sequence recommendation has become a hot research direction because of its practicality and high precision. Deep Neural Networks (DNN) have the natural advantage of capturing comprehensive relations among different entities, thus almost occupying a dominant position in sequence recommendation in the past few years. However, as Deep Learning (DL)-based methods are widely used to model local preferences under user behavior sequences, the global preference modeling of users is often underestimated, and usually, only some simple and crude user latent representations are introduced. Therefore, this paper proposes a sequential recommendation based on Fusing Session-Aware models and Self-Attention networks (FSASA). Specifically, we use the Self-Attentive Sequential Recommendation (SASRec) model as a global representation learning module to capture long-term preferences under user behavior sequences and further propose an improved session-aware sequential recommendation model as a local learning representation module from user model the user’s dynamic preferences in the historical behavior, and finally use the Gated Recurrent Unit (GRU) module to calculate their weights. Experiments on three widely used recommendation datasets show that FSASA outperforms state-of-the-art baselines on two commonly used metrics.
Keywords: Recommendation Systems, Sequential Recommendation, Session-Aware Recommendation, Self-Attention, Gated Recurrent Unit
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