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Sequential Recommendation with Decomposed Item Feature Routing
2022
Proceedings of the ACM Web Conference 2022
Sequential recommendation basically aims to capture user evolving preference. Intuitively, a user interacts with an item usually because of some specific feature, and user evolving preference is essentially determined by a series of important features along the time line. However, existing sequential models usually represent each item by a unified embedding, which fails to distinguish item features, let along modeling the feature sequences. To bridge this gap, in this paper, we propose a novel
doi:10.1145/3485447.3512101
fatcat:54pm7fq34zcv7ehrl5f6mmn4ca