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Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation
[article]
2022
arXiv
pre-print
User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data. Among existing user behavior modeling solutions, attention networks are widely adopted for its effectiveness and relative simplicity. Despite being extensively studied, existing attentions still suffer from two limitations: i) conventional attentions mainly take into account the spatial correlation between user behaviors,
arXiv:2204.06517v1
fatcat:7kwki6cnrzeilcxgrpsziw7jxy