Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-term preferences. Despite their success, these approaches still struggle to model sparse data, on which they struggle to learn high-quality item representations. We propose to model user dynamics from shopping intents and interacted items simultaneously. The learned intents are coarse-grained and work as prior knowledge for item recommendation. To this end, we present a coarse-to-fine self-attention framework, namely CaFe, which explicitly learns coarse-grained and fine-grained sequential dynamics. Specifically, CaFe first learns intents from coarse-grained sequences which are dense and hence provide high-quality user intent representations. Then, CaFe fuses intent representations into item encoder outputs to obtain improved item representations. Finally, we infer recommended items based on representations of items and corresponding intents. Experiments on sparse datasets show that CaFe outperforms state-of-the-art self-attentive recommenders by 44.03% NDCG@5 on average.
|Original language||English (US)|
|Title of host publication||SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||5|
|State||Published - Jul 6 2022|
|Event||45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022 - Madrid, Spain|
Duration: Jul 11 2022 → Jul 15 2022
|Name||SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Conference||45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022|
|Period||7/11/22 → 7/15/22|
Bibliographical notePublisher Copyright:
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- coarse-to-fine framework
- sequential recommendation
- sparse data