Dual-regularized matrix factorization with deep neural networks for recommender systems

Abstract In recommender systems, many efforts have been made on utilizing textual information in matrix factorization to alleviate the problem of data sparsity. Recently, some of the works have explored neural networks to do an in-depth understanding of textual item content and achieved impressive effectiveness by generating more accurate item latent models. Nevertheless, there remains an open issue as how to effectively exploit description documents of both users and items in matrix factorization. In this paper, we proposed dual-regularized matrix factorization with deep neural networks (DRMF) to deal with this issue. DRMF adopts a multilayered neural network model by stacking convolutional neural network and gated recurrent neural network, to generate independent distributed representations of contents of users and items. Then, representations serve to regularize the generation of latent models both for users and items in matrix factorization. We propose the corresponding algorithm for learning all parameters in DRMF. Experimental results proved that the dual-way regularization strategy significantly improves the matrix factorization methods on the accuracy of rating prediction and the recall of top-n recommendations. Also, as the components of DRMF, the new neural network model works better than the single convolutional neural network model.