期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 22, 期 6, 页码 1647-1659出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2019.2945180
关键词
Visualization; Recommender systems; Collaboration; Encoding; Task analysis; Feature extraction; History; Food Recommender Systems; Hierarchical Attention; Collaborative Filtering; Ingredients; Recipe Image
资金
- National Key Research and Development Program of China [2016QY03D0602]
- National Natural Science Foundation of China (Key Program) [61751201]
- China Scholarship Council (CSC)
- NExT++ project - National Research Foundation, Prime Ministers Office, Singapore under its IRC@Singapore Funding Initiative
Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. This work formulates the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, this work develops a dedicated neural network-based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, this work constructs a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference on food.
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