Category-Wise Meal Recommendation
Paper
Li, Ming, Li, Lin, Tao, Xiaohui, Xie, Qing and Yuan, Jingling. 2024. "Category-Wise Meal Recommendation." 30th International Conference on Neural Information Processing, Part XIV (ICONIP2023). Changsha, China 20 - 23 Nov 2023 Singapore . Springer. https://doi.org/10.1007/978-981-99-8181-6_22
| Paper/Presentation Title | Category-Wise Meal Recommendation |
|---|---|
| Presentation Type | Paper |
| Authors | Li, Ming, Li, Lin, Tao, Xiaohui, Xie, Qing and Yuan, Jingling |
| Journal or Proceedings Title | Proceedings of the 30th International Conference on Neural Information Processing, Part XIV (ICONIP2023) |
| Journal Citation | 1968, pp. 282-294 |
| Number of Pages | 13 |
| Year | 2024 |
| Publisher | Springer |
| Place of Publication | Singapore |
| ISBN | 9789819981809 |
| 9789819981816 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-99-8181-6_22 |
| Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-981-99-8181-6_22 |
| Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-981-99-8181-6 |
| Conference/Event | 30th International Conference on Neural Information Processing, Part XIV (ICONIP2023) |
| Event Details | 30th International Conference on Neural Information Processing, Part XIV (ICONIP2023) Parent International Conference on Neural Information Processing Delivery In person Event Date 20 to end of 23 Nov 2023 Event Location Changsha, China |
| Abstract | Meal recommender system, as an application of bundle recommendation, aims to provide courses from specific categories (e.g., appetizer, main dish) that are enjoyed as a meal for a user. Existing bundle recommendation methods work on learning user preferences from user-bundle interactions to satisfy users’ information need. However, users in food scenarios may have different preferences for different course categories. It is a challenge to effectively consider course category constraints when predicting meals for users. To this end, we propose a model CMRec: Category-wise Meal Recommendation model. Specifically, our model first decomposes interactions and affiliations between users, meals, and courses according to category. Secondly, graph neural networks are utilized to learn category-wise user/meal representations. Then, the likelihood of user-meal interactions is estimated category by category. Finally, our model is trained by a category-wise enhanced Bayesian Personalized Ranking loss. CMRec outperforms state-of-the-art methods in terms of Recall@K and NDCG@K on two public datasets. |
| Keywords | Category-wise representation |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 460510. Recommender systems |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Series | Communications in Computer and Information Science |
| Byline Affiliations | Wuhan University of Technology, China |
| University of Southern Queensland |
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https://research.usq.edu.au/item/z5v49/category-wise-meal-recommendation
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