App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 6

I2V-CLRec: time-aware sequential recommendation with Item2Vec and contrastive learning

Ai Jun
Geng Aiguo
Su Zhan
Zheng Zhengwei
Huang Peicong
School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2025.09.0428

Abstract

Existing sequential recommendation algorithms face three major challenges: item embeddings initialized randomly often lead to suboptimal convergence, learned representations lack robustness in data-sparse scenarios, and the significance of interaction time intervals is often overlooked. To address these issues, this paper proposes a novel algorithm named Time-Aware Sequential Recommendation with Item2Vec and Contrastive Learning (I2V-CLRec) . The algorithm introduces collaborative embedding to initialize the embedding matrix, injecting global collaborative signals as prior knowledge to optimize the starting point of representation learning. It employs a time-aware self-attention network as the backbone, incorporating logarithmic binning of time intervals to capture the dynamic evolution of user interests precisely. Additionally, the model integrates a contrastive learning framework as a self-supervised regularization task, leveraging data augmentation to enhance the robustness of sequence representations. Experimental results on MovieLens-1M, Beauty, and Digital-Music datasets demonstrate that the proposed algorithm significantly outperforms several state-of-the-art baseline methods. The study reveals that initialization with collaborative embedding guides the model convergence toward a more optimal solution space, and the integration of time-awareness with contrastive learning effectively enhances the capability to capture dynamic user intentions robustly under data sparsity.

Foundation Support

国家自然科学基金资助项目(61803264)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.09.0428
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 6

Publish History

[2026-02-25] Accepted Paper

Cite This Article

艾均, 耿爱国, 苏湛, 等. I2V-CLRec：协同嵌入与对比正则化增强的时间感知序列推荐 [J]. 计算机应用研究, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0428. (Ai Jun, Geng Aiguo, Su Zhan, et al. I2V-CLRec: time-aware sequential recommendation with Item2Vec and contrastive learning [J]. Application Research of Computers, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0428. )

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  • Application Research of Computers Monthly Journal
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    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.

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