A Location Recommender System for Location-Based Social Networks

Pavlos Kosmides, Chara Remoundou, K. Demestichas, Ioannis V. Loumiotis, Evgenia F. Adamopoulou, M. Theologou
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引用次数: 14

Abstract

Location-Based Social media have evolved rapidly during the last decade. Most Social Networks provide a plethora of venues and points of interest, while at the same time, users are able to declare their presence in specific locations (a process often referred to as "check-ins"), to provide ratings about the visited places or even suggest them to their friends. Location recommendations depending on users' needs have been a subject of interest for many researchers, while location prediction schemes have been developed in order to provide user's possible future location. In this paper, we present a method for predicting a user's location based on machine learning techniques. The dataset we used was based on input from a well-known Location-Based Social Network. Prediction results can be used in order to make appropriate suggestions for venues or points of interests to users, based on their interests and social connections. We propose a Probabilistic Neural Network and confirm its superior performance against two other types of neural networks.
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基于位置的社交网络的位置推荐系统
基于位置的社交媒体在过去十年中发展迅速。大多数社交网络提供了大量的地点和兴趣点,同时,用户可以声明他们在特定地点的存在(这个过程通常被称为“签到”),提供访问过的地方的评级,甚至推荐给他们的朋友。基于用户需求的位置推荐一直是许多研究人员感兴趣的主题,而位置预测方案则是为了提供用户可能的未来位置。在本文中,我们提出了一种基于机器学习技术预测用户位置的方法。我们使用的数据集是基于一个著名的基于位置的社交网络。预测结果可以根据用户的兴趣和社会关系,为用户提供合适的场地或兴趣点建议。我们提出了一种概率神经网络,并证实了它相对于其他两种类型的神经网络的优越性能。
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