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FSASA: Sequential Recommendation Based on Fusing Session-Aware Models and Self-Attention Networks
Computer Science and Information Systems, Tome 21 (2024) no. 1.

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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 
@article{CSIS_2024_21_1_a4,
     author = {Shangzhi Guo and Xiaofeng Liao and Fei Meng and Qing Zhao and Yuling Tang and Hui Li and Qinqin Zong},
     title = {FSASA: {Sequential} {Recommendation} {Based} on {Fusing} {Session-Aware} {Models} and {Self-Attention} {Networks}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {21},
     number = {1},
     year = {2024},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2024_21_1_a4/}
}
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AU  - Shangzhi Guo
AU  - Xiaofeng Liao
AU  - Fei Meng
AU  - Qing Zhao
AU  - Yuling Tang
AU  - Hui Li
AU  - Qinqin Zong
TI  - FSASA: Sequential Recommendation Based on Fusing Session-Aware Models and Self-Attention Networks
JO  - Computer Science and Information Systems
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PB  - mathdoc
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%T FSASA: Sequential Recommendation Based on Fusing Session-Aware Models and Self-Attention Networks
%J Computer Science and Information Systems
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%U http://geodesic.mathdoc.fr/item/CSIS_2024_21_1_a4/
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Shangzhi Guo; Xiaofeng Liao; Fei Meng; Qing Zhao; Yuling Tang; Hui Li; Qinqin Zong. FSASA: Sequential Recommendation Based on Fusing Session-Aware Models and Self-Attention Networks. Computer Science and Information Systems, Tome 21 (2024) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2024_21_1_a4/