POI Recommendation Based on Heterogeneous Graph Embedding

With the development and popularity of social networks, many human beings prefer to share their experiences on these networks. There are various methods proposed by the researcher which utilized user-generated content in the location-based social networks (LBSN) and recommend locations to users. However, there is a high sparsity in the user check-in information makes it tough to recommend the appropriate and accurate location to the user. To fix this issue, we put forward a proposal as a framework which utilizes a wide range of information available in these networks, each of which has its own type and provides appropriate recommendation. For this purpose, we encode the information as a number of entities and its attributes in the form of a heterogeneous graph, then graph embedding methods are used to embed all nodes in unified semantic representation space. As a result, we are able to model relations between users and venues in an efficient way and ameliorate the accuracy of the proposed method that recommends a place to a user. Our method is implemented and evaluated using Foursquare dataset, and the evaluation results depict that our work, boost performance in terms of precision, recall, and f-measure compared to the baseline work.