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Mining user preferences of new locations on location-based social networks: a multidimensional cloud model approach

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Abstract

In recent years, the prevalent of location-based social networks contributes massive data for location recommendation. Although collaborative filtering (CF) algorithm has been widely employed for location recommendation, it suffers the data sparsity and the high time complexity as it estimates the similarity of users by the common locations. In this paper, we extend the two-dimensional cloud model to the multidimensional cloud model and utilize it to the measure the similarity of user preferences and user behaviors. This method not only considers the multiple attributes of users (e.g., the diversity of user preferences), but also alleviates the sparsity of location recommendation based on CF algorithm to some extent. Then we integrate the similarity of user preferences, social ties and user behaviors into CF algorithm, which is expected to mine user preferences of new locations (MUPNL) more precisely. Furthermore, in order to improve the efficiency of the MUPNL algorithm, we parallelize it with Mapreduce framework. Experimental results on Yelp academic dataset demonstrate the good performance of the distributed MUPNL algorithm in accuracy and efficiency.

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Acknowledgments

This work is funded by the National Science Fund of China (Grant No. 60872051)

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Authors and Affiliations

  1. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China

    Fan Wang, Xiangwu Meng, Yujie Zhang & Chaohui Zhang

  2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

    Fan Wang, Xiangwu Meng, Yujie Zhang & Chaohui Zhang

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  1. Fan Wang
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  2. Xiangwu Meng
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  3. Yujie Zhang
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  4. Chaohui Zhang
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Correspondence to Xiangwu Meng.

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Wang, F., Meng, X., Zhang, Y. et al. Mining user preferences of new locations on location-based social networks: a multidimensional cloud model approach. Wireless Netw 24, 113–125 (2018). https://doi.org/10.1007/s11276-016-1316-x

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  • DOI: https://doi.org/10.1007/s11276-016-1316-x

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