Addressing the complexity of personalized, context-aware and health-aware food recommendations: an ensemble topic modelling based approach

摘要

Food Recommender Systems (FRS) have the potential to support informed and satisfying food choices. However, to realize their full potential, FRS must engage with the complexity of the choices people make around food. For example, while taste and ingredients are important, contextual and practical factors also play a critical role in food choice. Much of the previous literature on FRS has focused on ingredient-based recommendations, often in limited food datasets. Here we describe a broader approach, focusing on the use of Ensemble Topic Modelling (EnsTM) to support personalized recipe recommendations that implicitly capture and account for multi-domain food preferences in any food-corpus. EnsTM has the additional advantage of enabling a reduced data representation format that facilitates efficient user-modelling and recommendation. This article describes the results of two studies. The first investigated EnsTM based recommendation in a cold-start scenario. We investigated three different EnsTM based variations using a large-scale, real-world corpus of 230,876 recipes, and compared them with a conventional content-based approach. In a user study with 48 participants, EnsTM-based recommenders significantly outperformed the content-based approach. Alongside excellent coverage, they enabled an implicit understanding of users' food preference across multiple food domains. The second study investigated the use of EnsTM in a long-term or regular-use scenario. We implemented multiple variations of feature and/or topic based hybrid recipe recommenders, which updated users' profiles in real-time and predicted their preferences for new recipes. When compared against the current state of the art EnsTM-based recommenders performed significantly better, providing higher accuracy and coverage.