Interpretable next basket prediction boosted with representative recipes

Food is an essential element of our lives, cultures, and a crucial part of human experience. The study of food purchases can drive the design of practical services such as next basket predictor and shopping list reminder. Current approaches aimed at realizing these services do not exploit a contextual dimension involving food, i.e., recipes. To this aim, we design a next basket predictor based on representative recipes able to exploit the interest of customers towards certain ingredients when making the recommendation. The proposed method first identifies the representative recipes of a customer by analyzing her purchases and then estimates the rating of the items for the prediction. The ratings are based on both the purchases and the ingredients of the representative recipes. In addition, through our method, it is easy to justify why a specific set of items is predicted while such explanations are often not easily available in many other effective but opaque recommenders. Experimentation on a real-world dataset shows that the usage of recipes leverages the performance of existing next basket predictors.