_:n6921723ffdd64b259fcfb202929b506axb73
<http://schema.org/description>
"Recent reports from industry show that social recommender systems\nconsistently fail in practice. According to the negative findings, the failure\nis attributed to: (1) a majority of users only have a very limited number of\nneighbors in social networks and can hardly benefit from relations; (2) social\nrelations are noisy but they are often indiscriminately used; (3) social\nrelations are assumed to be universally applicable to multiple scenarios while\nthey are actually multi-faceted and show heterogeneous strengths in different\nscenarios. Most existing social recommendation models only consider the\nhomophily in social networks and neglect these drawbacks. In this paper we\npropose a deep adversarial framework based on graph convolutional networks\n(GCN) to address these problems. Concretely, for the relation sparsity and\nnoises problems, a GCN-based autoencoder is developed to augment the relation\ndata by encoding high-order and complex connectivity patterns, and meanwhile is\noptimized subject to the constraint of reconstructing the original social\nprofile to guarantee the validity of new identified neighborhood. After\nobtaining enough purified social relations for each user, a GCN-based attentive\nsocial recommendation module is designed to capture the heterogeneous strengths\nof social relations. These designs deal with the three problems faced by social\nrecommender systems respectively. Finally, we adopt adversarial training to\nunify and intensify all components by playing a minimax game and ensure a\ncoordinated effort to enhance social recommendation. Experimental results on\nmultiple open datasets demonstrate the superiority of our framework and the\nablation study confirms the importance and effectiveness of each component."
<https://deepai.org/profile/lizhen-cui>