Time interval enhanced contrastive learning sequence recommendation

Jian Feng; Xinzheng Liu

doi:10.1117/12.3034009

16 October 2024 Time interval enhanced contrastive learning sequence recommendation

Jian Feng, Xinzheng Liu

Proceedings Volume 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024); 132914T (2024) https://doi.org/10.1117/12.3034009
Event: Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 2024, Changchun, China

Abstract

Existing sequence recommendation models usually ignore the time interval of the sequence when modeling long-term preferences and short-term interests, which may lead to bias in user interests and fail to model user interests well for recommendations. In this paper, Time interval enhanced contrastive learning sequence recommendation (TICLSR) is proposed. Specifically, when modeling long-term preferences, we change the non-uniform sequence into a uniform sequence by enhancing the time interval, so that the model can model long-term preferences more effectively. In addition, we decouple long-term preferences and short-term interests, introduce contrastive learning, and explicitly model long-term preferences and short-term interests to obtain more accurate preference information. Comparative experiments with baseline models show that our proposed model exhibits better performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jian Feng and Xinzheng Liu "Time interval enhanced contrastive learning sequence recommendation", Proc. SPIE 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 132914T (16 October 2024); https://doi.org/10.1117/12.3034009

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