A Joint Deep Recommendation Framework for Location-Based Social Networks

Location-based social networks, such as Yelp and Tripadvisor, which allow users to share experiences about visited locations with their friends, have gained increasing popularity in recent years. However, as more locations become available, the need for accurate systems able to present personalized suggestions arises. By providing such service, point-of-interest recommender systems have attracted much interest from different societies, leading to improved methods and techniques. Deep learning provides an exciting opportunity to further enhance these systems, by utilizing additional data to understand users' preferences better. In this work we propose Textual and Contextual Embedding-based Neural Recommender (TCENR), a deep framework that employs contextual data, such as users' social networks and locations' geo-spatial data, along with textual reviews. To make best use of these inputs, we utilize multiple types of deep neural networks that are best suited for each type of data. TCENR adopts the popular multilayer perceptrons to analyze historical activities in the system, while the learning of textual reviews is achieved using two variations of the suggested framework. One is based on convolutional neural networks to extract meaningful data from textual reviews, and the other employs recurrent neural networks. Our proposed network is evaluated over the Yelp dataset and found to outperform multiple state-of-the-art baselines in terms of accuracy, mean squared error, precision, and recall. In addition, we provide further insight into the design selections and hyperparameters of our recommender system, hoping to shed light on the benefit of deep learning for location-based social network recommendation.