Unexp-DIN: Unexpected Deep Interest Network for Recommendation

Studies on recommender systems persuaded accuracy of predictions, leading to the filter bubble problem which narrowed the user's interest areas. Unexpected recommendation (UR) is one of the solutions in handling the filter bubble. However, existing research on UR still faces two shortcomings. On the one hand, it is difficult to measure the unexpectedness of items to users accurately. On the other hand, there is a decrease in accuracy when making unexpected item recommendations. In this paper, we propose an Unexpected Deep Interest Network (Unexp-DIN) model to solve these problems. We use mean-shift for multiple clustering and Cluster-attention to maximize the unexpectedness. Then we use the constraint function and the hybrid utility function to simultaneously improve accuracy and unexpectedness. Experiments on three publicly available benchmark datasets show that our model has different degrees of improvement in both accuracy and unexpectedness.