FSASA: Sequential Recommendation Based on Fusing Session-Aware Models and Self-Attention Networks

Shangzhi Guo1, Xiaofeng Liao1, Fei Meng1, Qing Zhao1, Yuling Tang2, Hui Li3 and Qinqin Zong4

  1. College of Computer Science, Chongqing University
    Chongqing 400044, China
    {20211401018g, xfliao, 20211401024g, 20211401020g}@cqu.edu.cn
  2. Hunan Creator Information Technologies CO., LTD.
    Changsha 410000, China
  3. Jiangxi Institute of Land and Space Survey and Planning
    Nanchang 330000, China
  4. Jiangxi Biological Vocational College
    Nanchang 330200, China


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.

Key words

Recommendation Systems, Sequential Recommendation, Session-Aware Recommendation, Self-Attention, Gated Recurrent Unit

Digital Object Identifier (DOI)


Publication information

Volume 21, Issue 1 (January 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

DownloadAvailable in PDF
Portable Document Format

How to cite

Guo, S., Liao, X., Meng, F., Zhao, Q., Tang, Y., Li, H., Zong, Q.: FSASA: Sequential Recommendation Based on Fusing Session-Aware Models and Self-Attention Networks. Computer Science and Information Systems, Vol. 21, No. 1, 1–20. (2024), https://doi.org/10.2298/CSIS230522067G