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Classical recommender system methods typically face the filter bubble problemwhen users only receive recommendations of their familiar items, making thembored and dissatisfied. To address the filter bubble problem, unexpectedrecommendations have been proposed to recommend items significantly deviatingfrom user's prior expectations and thus surprising them by presenting "fresh"and previously unexplored items to the users. In this paper, we describe anovel Personalized Unexpected Recommender System (PURS) model that incorporatesunexpectedness into the recommendation process by providing multi-clustermodeling of user interests in the latent space and personalized unexpectednessvia the self-attention mechanism and via selection of an appropriate unexpectedactivation function. Extensive offline experiments on three real-world datasetsillustrate that the proposed PURS model significantly outperforms thestate-of-the-art baseline approaches in terms of both accuracy andunexpectedness measures. In addition, we conduct an online A/B test at a majorvideo platform Alibaba-Youku, where our model achieves over 3\% increase in theaverage video view per user metric. The proposed model is in the process ofbeing deployed by the company.

PURS: Personalized Unexpected Recommender System for Improving User Satisfaction