The rapid growth of users’ involvement in Location-Based Social Networks has led to the expeditious growth of the data on a global scale. The need of accessing and retrieving relevant information close to users’ preferences is an open problem which continuously raises new challenges for recommendation systems. The exploitation of points-of-interest (POIs) recommendation by existing models is inadequate due to the sparsity and the cold start problems. To overcome these problems many models were proposed in the literature; however, most of them ignore important factors, such as: geographical proximity, social influence, or temporal and preference dynamics, which tackle their accuracy while personalize their recommendations. In this work, we investigate these problems and present a unified model that jointly learns user’s and POI dynamics. Our proposal is termed RELINE (REcommendations with muLtIple Network Embeddings). More specifically, RELINE captures: (i) the social, (ii) the geographical, (iii) the temporal influence, and (iv) the users’ preference dynamics, by embedding eight relational graphs into one shared latent space. We have evaluated our approach against state-of-the-art methods with three large real-world datasets in terms of accuracy. Additionally, we have examined the effectiveness of our approach against the cold-start problem. Performance evaluation results demonstrate that significant performance improvement is achieved in comparison to existing state-of-the-art methods.

用户对基于位置的社交网络的参与的迅速增长导致数据在全球范围内的快速增长。需要访问和检索接近用户喜好的相关信息是一个开放的问题，不断给推荐系统提出新的挑战。由于稀疏性和冷启动问题，现有模型无法充分利用推荐的兴趣点（POI）。为了克服这些问题，文献中提出了许多模型。但是，大多数人忽略了重要因素，例如：地理邻近性，社会影响力或时间和偏好动态，这些因素在个性化其建议的同时解决了准确性。在这项工作中，我们将研究这些问题，并提出一个统一的模型，共同学习用户和POI的动态。RE表扬与亩大号吨我PLE Ñ etwork ë mbeddings）。更具体地说，RELINE捕获：（i）社会，（ii）地理，（iii）时间影响和（iv）用户的偏好动态，方法是将八个关系图嵌入到一个共享的潜在空间中。在准确性方面，我们已针对具有三个大型真实世界数据集的最新方法对我们的方法进行了评估。此外，我们还研究了针对冷启动问题的方法的有效性。性能评估结果表明，与现有的最新技术相比，可以显着提高性能。