A study on a recommender model based on the Neural Collaborative Ranking model utilizing multiple implicit feedback with ordering relationships

Abstract

In the field of conventional recommendation systems, most of the models have been based on the prediction of evaluation values using evaluation value data directly assigned by users to their satisfaction with items. Recently, recommendation models that utilize behavioral history data such as implicit evaluation have been widely used. Neural Collaborative Ranking is a method for estimating and ranking the next most likely items to be observed in the list of items. Whereas, there are cases in which multiple implicit evaluations at different levels are observed, such as purchasing and browsing. However, the conventional NCR model cannot distinguish and learn multiple implicit evaluations, and cannot fully utilize the observed data. Therefore, in this study, we propose a model that takes into account multiple implicit evaluations with different levels in the NCR by adopting the method of Ding et al. In addition, we demonstrate the effectiveness of the proposed method.