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首页> 外文期刊>Expert systems: The international journal of knowledge engineering >An effective points of interest recommendation approach based on embedded meta-path of spatiotemporal data
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An effective points of interest recommendation approach based on embedded meta-path of spatiotemporal data

机译：An effective points of interest recommendation approach based on embedded meta-path of spatiotemporal data

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摘要

With the development of mobile networks and the rapid prevalence of location-basedsocial networks (LBSN), a massive volume of spatiotemporal data has been generated,which is valuable for points of interest (POI) recommendation. However, current studieshave not unleashed the full power of such spatiotemporal data, which either explore onlya single dimension of the data or consider multiple factors in an asynchronous fashion. Inthis article, we propose a novel spatiotemporal network-based recommender framework(STNBR) to effectively recommend POIs for users. Specifically, we first establish a comprehensiveconceptual model of spatiotemporal data, involving various essential factorsfor POIs recommendation. On top of the conceptual model, we design a series of meaningfulmeta-paths that simultaneously consider the time and location factors to preciselycapture the semantics of user behaviours. By profiling users based on their embeddedmeta-paths, our approach can yield meaningful POIs recommendations. We have evaluatedour proposal using a realistic dataset obtained from Foursquare and Gowalla, theresults of which show that our STNBR model outperforms existing approaches.

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