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Special Topics in Contrastive and Non-contrastive Learning
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 9
2628-2634

Sequence recommendation based on side information and long-short term preferences

Liu Chao
Ren Mengyao
Feng Luhua
College of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0032

Abstract

To address the issue of user preference drift and capture dynamic user preferences more accurately in sequence recommendation, this paper proposed a novel model named SILSSRec. The model initially leveraged categories and frequencies of items as side information to generate personalized user embeddings based on users' historical interaction sequences. Then it created personalized temporal interval embeddings by considering the time intervals between historical and current interactions, and integrated these embeddings with item feature embeddings to form personalized temporal embeddings. The model employed attention mechanisms and gated recurrent networks to extract users' long-term and short-term preferences from the embedding representations. Furthermore, it used contrastive learning to reinforce the feature representation of preferences, and an adaptive aggregation network dynamically combined these two types of preferences to form the final preference representation of users. Experiments on eight public datasets demonstrate that SILSSRec outperforms existing baseline models on evaluation metrics, with an average increase of 3.82% in AUC, 7.2% in recall rate, and 0.3% in precision. The results validate that SILSSRec performs well in various scenarios, effectively mitigating the preference drift issue and enhancing recommendation performance.

Foundation Support

重庆市社科联资助项目(2021NDYB101)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0032
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 9
Section: Special Topics in Contrastive and Non-contrastive Learning
Pages: 2628-2634
Serial Number: 1001-3695(2024)09-009-2628-07

Publish History

[2024-05-14] Accepted Paper
[2024-09-05] Printed Article

Cite This Article

刘超, 任梦瑶, 冯禄华. 基于辅助信息与长短期偏好的序列推荐 [J]. 计算机应用研究, 2024, 41 (9): 2628-2634. (Liu Chao, Ren Mengyao, Feng Luhua. Sequence recommendation based on side information and long-short term preferences [J]. Application Research of Computers, 2024, 41 (9): 2628-2634. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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