Journal's Facebook pageFSASA: Sequential Recommendation Based on Fusing Session-Aware Models and Self-Attention Networks
- College of Computer Science, Chongqing University
Chongqing 400044, China
{20211401018g, xfliao, 20211401024g, 20211401020g}@cqu.edu.cn - Hunan Creator Information Technologies CO., LTD.
Changsha 410000, China
yuling.tang@chinacreator.com - Jiangxi Institute of Land and Space Survey and Planning
Nanchang 330000, China
lihcool@126.com - Jiangxi Biological Vocational College
Nanchang 330200, China
honeybabyqinqin@gmail.com
Abstract
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)
https://doi.org/10.2298/CSIS230522067G
Publication information
Volume 21, Issue 1 (January 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
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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
