A healthy and reliable rating profile expansion approach to address data sparsity in food recommendation systems

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Abstract Food recommendation systems have become increasingly popular due to the proliferation of online food service websites. Accordingly, the ratings assigned by users are one of the most important resources in these systems. However, users generally express their opinions about a few foods, which results in data sparsity. Furthermore, food recommendation is a health-critical task, as recommending unhealthy foods to users may threaten their health. In this paper, we developed a novel rating profile expansion approach for food recommenders that considers both health and reliability measures. This approach enhances the efficiency of the user’s rating profile by including healthy and reliable virtual ratings. Specifically, we introduce a probabilistic rating profile evaluation technique to determine whether a profile needs to be expanded. Then, those profiles with an insufficient number of ratings are automatically expanded by adding virtual ratings obtained using the opinions of users who belong to the target user’s community. For this purpose, the users are grouped using a novel time-aware community detection algorithm based on their preferences. Moreover, a health-aware reliability measure is proposed so that only the most reliable virtual ratings are accounted for in the target user’s rating profile expansion. Therefore, the developed approach not only mitigates issues stemming from sparse data in food recommendation systems but also makes them more effective in recommending healthy foods to users. Experiments conducted on two publicly available real-world datasets demonstrated that the developed system is superior to other baseline models.