Network-Constrained Tensor Factorization for Personal Recommendation in an Enterprise Network

While standard product recommendation systems have proven to be useful for e-commerce, they mainly rely on some prior information about users and products, such as ratings and intrinsic properties of products as well as profile attributes of users. In e-commerce settings, however, a more complete understanding of the demands of customers and the enterprise network constructed by suppliers and manufacturers can be utilized to improve the quality of product recommendations. Moreover, user ratings may be very sparse in some domains. Standard approaches suffer from such data sparsity and neglect to account for important additional dependencies that can be taken into consideration. This motivates us to design a new recommendation model, which incorporates information of network into rating prediction. In this paper, we propose a network-constrained tensor factorization approach, which imposes network constraints as regularization terms on tensor non-negative factorization to improve the accuracy of prediction. To solve the network-constrained regularization problem in our model, we use the Alternating Direction Method of Multipliers (ADMM) method. Experiment results on real-world dataset demonstrate that our approach outperforms other state-of-the-art baselines.