Multi-intent Aware Contrastive Learning for Sequential Recommendation
<|reference_start|>Multi-intent Aware Contrastive Learning for Sequential Recommendation: Intent is a significant latent factor influencing user-item interaction sequences. Prevalent sequence recommendation models that utilize contrastive learning predominantly rely on single-intent representations to direct the training process. However, this paradigm oversimplifies real-world recommendation scenarios, attempting to encapsulate the diversity of intents within the single-intent level representation. SR models considering multi-intent information in their framework are more likely to reflect real-life recommendation scenarios accurately.<|reference_end|>
@article{huang2024multi-intent,
    title={Multi-intent Aware Contrastive Learning for Sequential Recommendation},
    author={Junshu Huang, Zi Long, Xianghua Fu and Yin Chen},
    journal={arXiv preprint arXiv:2409.08733},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08733},
    primaryClass={cs.LG}
}