FoodRecNet: a comprehensively personalized food recommender system using deep neural networks

被引：0
作者：
Saeed Hamdollahi Oskouei
Mahdi Hashemzadeh
机构：
[1] Azarbaijan Shahid Madani University,Faculty of Information Technology and Computer Engineering
[2] Azarbaijan Shahid Madani University,Artificial Intelligence and Machine Learning Research Laboratory
来源：
Knowledge and Information Systems | 2023年 / 65卷
关键词：
Recommender systems; Artificial neural networks; Deep learning; Food recommender; Food; Health rules;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Today, the huge variety of foods and the existence of different food preferences among people have made it difficult to choose the right food according to people's food preferences for different meals. Also, achieving a pleasant balance between users’ food preferences and health requirements, considering the physical condition, diseases/allergies of users, and having a suitable dietary diversity, has become a requirement in the field of nutrition. Therefore, the need for an intelligent system to recommend and choose the proper food based on these criteria is felt more and more. In this paper, a deep learning-based food recommender system, termed “FoodRecNet”, is presented using a comprehensive set of characteristics and features of users and foods, including users’ long-term and short-term preferences, users’ health conditions, demographic information, culture, religion, food ingredients, type of cooking, food category, food tags, diet, allergies, text description, and even the images of the foods. The appropriate combination of features used in the proposed system has been identified based on detailed investigations conducted in this research. In order to achieve a desired architecture of the deep artificial neural network for our purpose, five different architectures are designed and evaluated, considering the specific characteristics of the intended application In addition, to evaluate the FoodRecNet, this work constructs a large-scale annotated dataset, consisting of 3,335,492 records of food information, users and their scores, and 54,554 food images. The experiments conducted on this dataset and the “FOOD.COM” benchmark dataset confirm the effectiveness of the combination of features used in FoodRecNet. The RMSE rates obtained by FoodRecNet on these two datasets are 0.7167 and 0.4930, respectively, which are much better than that of competitors. All the implementation source codes and datasets of this research are made publicly available at https://github.com/saeedhamdollahi/FoodRecNet.
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页码：3753 / 3775
页数：22
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