A social recommender system using deep architecture and network embedding

A recommender system is an active tool for information filtering that can be deployed in a complex and dynamic online environment to provide the most relevant and accurate content to the users based on their unique preferences and tastes. The recent direction towards enhancing the recommender system leverages deep learning techniques and trust information. However, building a unified model for a recommender system that integrates deep architecture with trust information is an open challenge. Here, we propose a hybrid method by modeling a joint optimization function which extends deep Autoencoder with top-k semantic social information. We use network representation learning methods to capture the implicit semantic social information. We conducted experiments with various real-world data sets and evaluated the performance of the proposed method using different evaluation measures. Experimental results show the performance improvement of the proposed system compared to state-of-the-art methods.