Food Recommendation: Framework, Existing Solutions, and Challenges

• Published in: IEEE transactions on multimedia Vol. 22; no. 10; pp. 2659 - 2671
• Main Authors: Min, Weiqing, Jiang, Shuqiang, Jain, Ramesh
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Artificial intelligence; Biomedical monitoring; Blood pressure; data mining; Diabetes; Domains; Food; health information management; Heart diseases; Heart rate; image recognition; knowledge based systems; Multimedia; Temperature sensors
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2019.2958761

A growing proportion of the global population is becoming overweight or obese, leading to various diseases (e.g., diabetes, ischemic heart disease and even cancer) due to unhealthy eating patterns, such as increased intake of food with high energy and high fat. Food recommendation is of paramount importance to alleviate this problem. Unfortunately, modern multimedia research has enhanced the performance and experience of multimedia recommendation in many fields such as movies and POI, yet largely lags in the food domain. This article proposes a unified framework for food recommendation, and identifies main issues affecting food recommendation including incorporating various context and domain knowledge, building the personal model, and analyzing unique food characteristics. We then review existing solutions for these issues, and finally elaborate research challenges and future directions in this field. To our knowledge, this is the first survey that targets the study of food recommendation in the multimedia field and offers a collection of research studies and technologies to benefit researchers in this field.