A systematic review on food recommender systems

The Internet has revolutionised the way information is retrieved, and the increase in the number of users has resulted in a surge in the volume and heterogeneity of available data. Recommender systems have become popular tools to help users retrieve relevant information quickly. Food Recommender Systems (FRS), in particular, have proven useful in overcoming the overload of information present in the food domain. However, the recommendation of food is a complex domain with specific characteristics causing many challenges. Additionally, very few systematic literature reviews have been conducted in the domain on FRS. This paper presents a systematic literature review that summarises the current state-of-the-art in FRS. Our systematic review examines the different methods and algorithms used for recommendation, the data and how it is processed, and evaluation methods. It also presents the advantages and disadvantages of FRS. To achieve this, a total of 67 high-quality studies were selected from a pool of 2,738 studies using strict quality criteria. The review reveals that the domain of food recommendation is very diverse, and most FRS are built using content-based filtering and ML approaches to provide non-personalised recommendations. The review provides valuable information to the research field, helping researchers in the domain to select a strategy to develop FRS. This review can help improve the efficiency of development, thus closing the gap between the development of FRS and other recommender systems.

• Publication year

  2023

• Venue

  Expert systems with applications

• Publication date

  2023-10-01

• Fields of study

  Agricultural and Food Sciences, Computer Science

• Identifiers
  DOI 10.1016/j.eswa.2023.122166
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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