Recommendation with the Mixture of Multiple Object Similarities

Abstract

In recommendation systems, users and objects are vectorized based on data describing the relationship between the object and the user, such as the users' five-point rating of the object and similarity between objects, and the user's preference of an unknown object is predicted according to the distance between the user and the object. There are many types of similarities and previous research improves performance by converting them into features and mixed by means of DNNs. However, it is difficult to analyze which similarities are important for the individual user because of the complex mechanism of DNNs. In this study, we propose a model that predicts the mixing ratio of similarities for each user and calculates the vector of objects so that the mixing ratio of the vector directly corresponds to that of similarity. We also show that interpretable mixing improves precision experimentally.