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Food recommender systems play an important role in assisting users toidentify the desired food to eat. Deciding what food to eat is a complex andmulti-faceted process, which is influenced by many factors such as theingredients, appearance of the recipe, the user's personal preference on food,and various contexts like what had been eaten in the past meals. In this work,we formulate the food recommendation problem as predicting user preference onrecipes 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 arecipe; and 3) the descriptive image of a recipe. To address this challengingproblem, we develop a dedicated neural network based solution HierarchicalAttention based Food Recommendation (HAFR) which is capable of: 1) capturingthe collaborative filtering effect like what similar users tend to eat; 2)inferring a user's preference at the ingredient level; and 3) learning userpreference from the recipe's visual images. To evaluate our proposed method, weconstruct a large-scale dataset consisting of millions of ratings fromAllRecipes.com. Extensive experiments show that our method outperforms severalcompeting recommender solutions like Factorization Machine and Visual BayesianPersonalized Ranking with an average improvement of 12%, offering promisingresults in predicting user preference for food. Codes and dataset will bereleased upon acceptance.

Hierarchical Attention Network for Visually-aware Food Recommendation