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Intent Contrastive Learning for Sequential Recommendation
[article]
2022
pre-print
Users' interactions with items are driven by various intents (e.g., preparing for holiday gifts, shopping for fishing equipment, etc.).However, users' underlying intents are often unobserved/latent, making it challenging to leverage such latent intents forSequentialrecommendation(SR). To investigate the benefits of latent intents and leverage them effectively for recommendation, we proposeIntentContrastiveLearning(ICL), a general learning paradigm that leverages a latent intent variable into
doi:10.1145/3485447.3512090
arXiv:2202.02519v1
fatcat:gft7ku773zcjha3i6troi5gf4y