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Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. However, recent reports from industry show that social recommender systems consistently fail in practice. According to the negative findings, the failure is attributed to: (1) A majority of users only have a very limited number of neighbors in social networks and can hardly benefit from social relations; (2) Social relations are noisy but they are indiscriminately used; (3) Social relations are assumed to be universally applicable to multiple scenarios while they are actually multi-faceted and show heterogeneous strengths in different scenarios. Most existing social recommendation models only consider the homophily in social networks and neglect these drawbacks. In this paper we propose a deep adversarial framework based on graph convolutional networks (GCN) to address these problems. Concretely, for (1) and (2), a GCN-based autoencoder is developed to augment the relation data by encoding high-order and complex connectivity patterns, and meanwhile is optimized subject to the constraint of reconstructing the social profile to guarantee the validity of the identified neighborhood. After obtaining enough purified social relations for each user, a GCN-based attentive social recommendation module is designed to address (3) by capturing the heterogeneous strengths of social relations. Finally, we adopt adversarial training to unify all the components by playing a Minimax game and ensure a coordinated effort to enhance recommendation performance. Extensive experiments on multiple open datasets demonstrate the superiority of our framework and the ablation study confirms the importance and effectiveness of each component. [ABSTRACT FROM AUTHOR]

Recommender systems are popular for personalization in online communities. Users, items, and other affiliated information such as tags, item genres, and user friends of an online community form a heterogenous information network. User profiling is the foundation of personalized recommender systems. It provides the basis to discover knowledge about an individual user’s interests to items. Typically, users are profiled with their direct explicit or implicit ratings, which ignored the inter-connections among users, items, and other entity nodes of the information network. This paper proposes a deep reinforcement user profiling approach for recommender systems. The user profiling process is framed as a sequential decision making problem which can be solved with a Reinforcement Learning (RL) agent. The RL agent interacts with the external heterogenous information network environment and learns a decision making policy network to decide whether there is an interest or preference path between a user and an unobserved item. To effectively train the RL agent, this paper proposes a multi-iteration training process to combine both expert and data-specific knowledge to profile users, generate meta-paths, and make recommendations. The effectiveness of the proposed approaches is demonstrated in experiments conducted on three datasets. [ABSTRACT FROM AUTHOR]