Basket-Enhanced Heterogenous Hypergraph for Price-Sensitive Next Basket Recommendation
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916399383838720 |
|---|---|
| author | Zhou, Yuening Wang, Yulin Cui, Qian Guan, Xinyu Cisternas, Francisco |
| author_facet | Zhou, Yuening Wang, Yulin Cui, Qian Guan, Xinyu Cisternas, Francisco |
| contents | Next Basket Recommendation (NBR) is a new type of recommender system that predicts combinations of items users are likely to purchase together. Existing NBR models often overlook a crucial factor, which is price, and do not fully capture item-basket-user interactions. To address these limitations, we propose a novel method called Basket-augmented Dynamic Heterogeneous Hypergraph (BDHH). BDHH utilizes a heterogeneous multi-relational graph to capture the intricate relationships among item features, with price as a critical factor. Moreover, our approach includes a basket-guided dynamic augmentation network that could dynamically enhances item-basket-user interactions. Experiments on real-world datasets demonstrate that BDHH significantly improves recommendation accuracy, providing a more comprehensive understanding of user behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11695 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Basket-Enhanced Heterogenous Hypergraph for Price-Sensitive Next Basket Recommendation Zhou, Yuening Wang, Yulin Cui, Qian Guan, Xinyu Cisternas, Francisco Information Retrieval Next Basket Recommendation (NBR) is a new type of recommender system that predicts combinations of items users are likely to purchase together. Existing NBR models often overlook a crucial factor, which is price, and do not fully capture item-basket-user interactions. To address these limitations, we propose a novel method called Basket-augmented Dynamic Heterogeneous Hypergraph (BDHH). BDHH utilizes a heterogeneous multi-relational graph to capture the intricate relationships among item features, with price as a critical factor. Moreover, our approach includes a basket-guided dynamic augmentation network that could dynamically enhances item-basket-user interactions. Experiments on real-world datasets demonstrate that BDHH significantly improves recommendation accuracy, providing a more comprehensive understanding of user behavior. |
| title | Basket-Enhanced Heterogenous Hypergraph for Price-Sensitive Next Basket Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2409.11695 |