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

Authors :
Shangzhi Guo
Xiaofeng Liao
Fei Meng
Qing Zhao
Yuling Tang
Hui Li
Qinqin Zong
Source :
Computer Science & Information Systems; Jan2024, Vol. 21 Issue 1, p1-20, 20p
Publication Year :
2024

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18200214
Volume :
21
Issue :
1
Database :
Complementary Index
Journal :
Computer Science & Information Systems
Publication Type :
Academic Journal
Accession number :
175427931
Full Text :
https://doi.org/10.2298/CSIS230522067G