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Research on comprehensive point of interest (POI) recommendation based on spark

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Abstract

With the rapid development of mobile devices, global positioning system and Web2.0 technology, more and more users are choosing to share rich information in location-based social networks. Point of interest (POI) recommendation is a hot research direction in recent years, this paper first based on the existed recommending research results on the POI, using methods of linear weighting and cascading combination to integrate social factors, geographical factors and time factors to recommend POI, for the sake of improving effectiveness of results. Then, in the Spark cluster environment, it was extended to improve the efficiency and scalability of POI recommendation. Experimental results showed that the implementation of comprehensive POI recommendation methods in Spark cluster environment was more efficient, as the amount of data increased, efficiency advantage was increasingly obvious.

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Acknowledgement

Funding is provided by Chongqing Big Data Engineering Laboratory for Children, Chongqing Electronics Engineering Technology Research Center for Interactive Learning.

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Authors and Affiliations

  1. Chongqing University of Education, Chongqing, China

    Fangcheng He & Pengcheng Wei

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  1. Fangcheng He
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  2. Pengcheng Wei
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Correspondence to Pengcheng Wei.

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He, F., Wei, P. Research on comprehensive point of interest (POI) recommendation based on spark. Cluster Comput 22 (Suppl 4), 9049–9057 (2019). https://doi.org/10.1007/s10586-018-2061-y

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  • DOI: https://doi.org/10.1007/s10586-018-2061-y

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