Predicting diversification scores of videos in recommendation network

Recommendation systems are crucial in various platforms from shopping to online social networks. A key challenge in recommendation systems is to leverage diversity, exposing or recommending diverse items to individuals. Despite much effort on studying diversity in the recommendation systems, little work has focused on estimating how much an item will potentially affect user's diversity experiences by contributing to consecutive recommendations in a session. In this work, we propose a deep learning model that can predict diversification scores, which is a degree of potential contribution to users' diversity experiences of an item. The proposed model adopts multiple graph neural network layers with a novel attention mechanism that can capture the features of a given item and its related items in terms of recommendation. To prove the effectiveness of our approach, we collect a large dataset of video recommendations from YouTube and conduct random-walk experiments to simulate user traces. The evaluation results on the dataset shows that the proposed model accurately predicts each item's contribution on user diversity experiences.