The Japanese Society for Artificial Intelligence

Collaborative Metric Learning (CML) is a recommendation model based on implicit data, i.e. behavior history such as clicks and browsings. CML learns an metric space to embed not only the relationship between users and items, but also the similarity between items and that between users. Moreover, CML recommends the items which are close to each user in the trained embedding space. However, CML tends to learn by focusing on items that are popular among many users, and the accuracy of embedded representations of other minor items is often neglected. On the other hand, it is necessary to learn embedded representations of many minor items that match the user's preferences with high accuracy in order to provide unexpected recommendations that users may not have recognized. In this study, we propose a method to learn the embedded representations that capture user's preferences by weighting according to the number of observations of implicit data, and to make unexpected recommendations that include minor items. Finally, we apply the proposed method to actual movie evaluation data set, and show the usefulness of the proposed method in making unexpected recommendations based on the users' preferences.