User-Meal Interaction Learning for Meal Recommendation: A Reproducibility Study
Paper
Paper/Presentation Title | User-Meal Interaction Learning for Meal Recommendation: A Reproducibility Study |
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Presentation Type | Paper |
Authors | Li, Ming, Li, Lin, Tao, Xiaohui and Zhong, Ning |
Journal or Proceedings Title | Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP '23) |
Journal Citation | pp. 104-113 |
Number of Pages | 10 |
Year | 2023 |
Publisher | Association for Computing Machinery (ACM) |
Place of Publication | United States |
ISBN | 9798400704086 |
Digital Object Identifier (DOI) | https://doi.org/10.1145/3624918.3625342 |
Web Address (URL) of Paper | https://dl.acm.org/doi/abs/10.1145/3624918.3625342 |
Web Address (URL) of Conference Proceedings | https://dl.acm.org/doi/proceedings/10.1145/3624918 |
Conference/Event | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP '23) |
Event Details | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP '23) Delivery In person Event Date 26 to end of 28 Nov 2023 Event Location Beijing, China |
Abstract | Recommendation systems are important in web3.0 as a technology to achieve high-quality interaction between people and the web. 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. Common methods include collaborative filtering, attention-based and graph-based ones, etc. User-meal interaction learning is the core of a meal recommendation and its key feature is category-constrained. However, there is no work to compare and study different types of methods. Moreover, existing work does not make full use of category information in the meal recommendation. In this paper, we conduct a reproducibility study about user-meal interaction learning for meal recommendation. We reproduce seven state-of-the-art meal and general bundle recommendation models and re-implement two models with considering category information. Extensive experiments are conducted on two datasets with different user-meal interaction densities to explore the impact of data density on different types of user-meal interaction learning, and investigate the effectiveness of different category-wise implementations. The experimental results are instructive and beneficial to the development and application of meal recommendation research. |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460502. Data mining and knowledge discovery |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Wuhan University of Technology, China |
University of Southern Queensland | |
Maebashi Institute of Technology, Japan |
https://research.usq.edu.au/item/z9w70/user-meal-interaction-learning-for-meal-recommendation-a-reproducibility-study
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