A Dual-Attention Autoencoder Network for Efficient Recommendation System

Abstract

Accelerated development of mobile networks and applications leads to the exponential expansion of resources, which causes problems such as trek and overload of information. One of the practical approaches to ease these problems is recommendation systems (RSs) that can provide individualized service. Video recommendation is one of the most critical recommendation services. However, achieving satisfactory recommendation service on the sparse data is difficult for video recommendation service. Moreover, the cold start problem further exacerbates the research challenge. Recent state-of-the-art works attempted to solve this problem by utilizing the user and item information from some other perspective. However, the significance of user and item information changes under different applications. This paper proposes an autoencoder model to improve recommendation efficiency by utilizing attribute information and implementing the proposed algorithm for video recommendation. In the proposed model, we first extract the user features and the video features by combining the user attribute and the video category information simultaneously. Then, we integrate the attention mechanism into the extracted features to generate the vital features. Finally, we incorporate the user and item potential factor to generate the probability matrix and defines the user-item rating matrix using the factorized probability matrix. Experimental results on two shared datasets demonstrates that the proposed model can effectively ameliorate video recommendation quality compared with the state-of-the-art methods.