{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:16:15Z","timestamp":1784178975755,"version":"3.55.0"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62276196"],"award-info":[{"award-number":["62276196"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>\n            Food computing, a newly emerging topic, is closely linked to human life through computational methodologies. Meal recommendation, a food-related study about human health, aims to provide users a meal with courses constrained from specific categories (e.g., appetizers, main dishes) that can be enjoyed as a service. Historical interaction data, important user information, is often used by existing models to learn user preferences. However, if a user\u2019s preferences favor less healthy meals, the model will follow that preference and make similar recommendations, potentially negatively impacting the user\u2019s long-term health. This emphasizes the necessity for health-oriented and responsible meal recommendation systems. In this article, we propose a healthiness-aware and category-wise meal recommendation model called CateRec, which boosts healthiness exposure by using nutritional standards as knowledge to guide the model training. Two fundamental questions are raised and answered: (1) How can the healthiness of meals be\n            <jats:bold>evaluated?<\/jats:bold>\n            Two well-known nutritional standards from the World Health Organization and the United Kingdom Food Standards Agency are used to calculate the healthiness score of the meal. (2) How can the model training be\n            <jats:bold>guided<\/jats:bold>\n            in a health-oriented manner? We construct category-wise personalization partial rankings and category-wise healthiness partial rankings, and theoretically analyze that they meet the necessary properties and assumptions required to be trained by the maximum posterior estimator under Bayesian probability. The data analysis confirms the existence of user preferences leaning towards less healthy meals in two public datasets. A comprehensive experiment demonstrates that our CateRec effectively boosts healthiness exposure in terms of mean healthiness score and ranking exposure while being comparable to the state-of-the-art model in terms of recommendation accuracy.\n          <\/jats:p>","DOI":"10.1145\/3643859","type":"journal-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T12:24:39Z","timestamp":1707135879000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Boosting Healthiness Exposure in Category-Constrained Meal Recommendation Using Nutritional Standards"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4618-7988","authenticated-orcid":false,"given":"Ming","family":"Li","sequence":"first","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7553-6916","authenticated-orcid":false,"given":"Lin","family":"Li","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0020-077X","authenticated-orcid":false,"given":"Xiaohui","family":"Tao","sequence":"additional","affiliation":[{"name":"University of Southern Queensland, Toowoomba, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6346-0707","authenticated-orcid":false,"given":"Zhongwei","family":"Xie","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co Ltd, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4530-588X","authenticated-orcid":false,"given":"Qing","family":"Xie","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7924-8620","authenticated-orcid":false,"given":"Jingling","family":"Yuan","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"32","volume-title":"Proceedings of the 2nd International Workshop on Health Recommender Systems co-located with the 11th International Conference on Recommender Systems (CEUR Workshop Proceedings\u201917)","volume":"1953","author":"Agapito Giuseppe","year":"2017","unstructured":"Giuseppe Agapito, Mariadelina Simeoni, Barbara Calabrese, Pietro Hiram Guzzi, Giorgio Fuiano, and Mario Cannataro. 2017. DIETOS: A recommender system for health profiling and diet management in chronic diseases. In Proceedings of the 2nd International Workshop on Health Recommender Systems co-located with the 11th International Conference on Recommender Systems (CEUR Workshop Proceedings\u201917), Vol. 1953. CEUR-WS.org, 32\u201335."},{"key":"e_1_3_2_3_2","first-page":"4017","volume-title":"Proc. of the 25th Int. Conf. on Pattern Recognition (ICPR\u201920)","author":"Aguilar Eduardo","year":"2020","unstructured":"Eduardo Aguilar, Bhalaji Nagarajan, Rupali Khatun, Marc Bola\u00f1os, and Petia Radeva. 2020. Uncertainty-aware data augmentation for food recognition. In Proc. of the 25th Int. Conf. on Pattern Recognition (ICPR\u201920). 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