Personalized ranking metric embedding for next new POI recommendation
Citations
1,202 citations
687 citations
Cites background or methods from "Personalized ranking metric embeddi..."
...And PFMC improves the performance greatly comparing with TF. PFMC-LR and PRME achieve further improvement with via incorporating distance information....
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...• PRME (Feng et al. 2015): It takes distance between destination location and recent vistaed ones into consideration for learning embeddings....
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...And Personalized Ranking Metric Embedding (PRME) method (Feng et al. 2015) learns embeddings as well as calculating the distance between destination location and recent visited ones....
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425 citations
Additional excerpts
...Beyond e-commerce, sequential recommendation has also been applied to various application scenarios such as POI recommendation [3, 4], music recommendation [1, 8, 29], browsing recommendation [35], etc....
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340 citations
Additional excerpts
...10, 8, 26], and currently-visited POI [4, 6]....
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332 citations
Cites background from "Personalized ranking metric embeddi..."
...PRME proposed by Feng et al. [6] is also the typical one which exploits pair-wise ranking scheme....
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...[6] is also the typical one which exploits pair-wise ranking scheme....
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...However, JIM slightly outperforms PRME-G on Foursquare dataset while PRME-G exceeds JIM on Gowalla....
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...However, our work is a graph-based method, which integrates various factors into a shared metric by different bipartite graphs while PRME embeds user preference and sequential patterns in two different metric respectively, and only considered sequential patterns of POIs and geographical influence....
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...PRME [6] is a personalized ranking metric embedding algorithm that jointly models the sequential transition of POIs and user preferences....
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References
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"Personalized ranking metric embeddi..." refers background in this paper
...For example, Gaussian Mixture distribution[Cho et al., 2011; Cheng et al., 2012] and power law distribution [Ye et al., 2011; Yuan et al., 2013] have been proposed to model the geographical influence....
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...The first dataset is the FourSquare check-ins within Singapore [Yuan et al., 2013] while the second one is the Gowalla check-ins dataset within California and Nevada [Cho et al., 2011]....
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..., 2013] while the second one is the Gowalla check-ins dataset within California and Nevada [Cho et al., 2011]....
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...Importance of POI recommendation has attracted a significant amount of research interest on developing recommendation techniques [Cho et al., 2011; Ye et al., 2011; Cheng et al., 2012; Yuan et al., 2013; Lian et al., 2014; Li et al., 2015]....
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...For example, Gaussian Mixture distribution[Cho et al., 2011; Cheng et al., 2012] and power law distribution [Ye et al....
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1,788 citations
1,593 citations