Recently, the increasing number of mobile users in location-based social networks (LBSNs) has generated large amounts of data, which provides unprecedented opportunities to study mobile user preferences for location recommendation. However, the huge amount of LBSNs data and sparsity problem limited improvements of efficiency and accuracy on mobile user preferences for location recommendation. This paper proposes a context-aware user preferences prediction algorithm for location recommendation on LBSNs. It introduces cloud model and category information into estimating the similarity of users and locations. Furthermore, it predicts user preferences of new locations according to the categories of new locations and user visited. In particular, the algorithm is parallelized with MapReduce framework for significant improvement in efficiency. Experimental results on Foursquare dataset demonstrate the performance gains of the algorithm.

中文翻译：

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基于位置的社交网络的上下文感知用户偏好预测

最近，基于位置的社交网络（LBSN）中越来越多的移动用户产生了大量数据，这为研究移动用户对位置推荐的偏好提供了前所未有的机会。然而，大量的LBSNs数据和稀疏性问题限制了移动用户位置推荐偏好的效率和准确性的提高。本文提出了一种上下文感知的用户偏好预测算法，用于 LBSN 上的位置推荐。它引入了云模型和类别信息来估计用户和位置的相似性。此外，它根据新位置和用户访问的类别来预测新位置的用户偏好。特别是，该算法与 MapReduce 框架并行化以显着提高效率。