TICRec: A Probabilistic Framework to Utilize Temporal Influence Correlations for Time-Aware Location Recommendations
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Cites background from "TICRec: A Probabilistic Framework t..."
..., a multi-center Gaussian distribution [2], a power-law distribution [9, 13, 15, 22, 23, 28, 29, 32], or a personalized nonparametric distribution for each user [30, 33, 34]....
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103 citations
Cites background from "TICRec: A Probabilistic Framework t..."
...This observation inspires the researches exploiting this periodic pattern for POI recommendation [8,11,73,77]....
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84 citations
Cites background or methods from "TICRec: A Probabilistic Framework t..."
...Furthermore, other papers [Lian et al. 2015; Zhang and Chow 2013, 2015a, 2015b; Zhang et al. 2014a] personalize the geographical in.uence by modeling a personalized nonparametric distribution for each user....
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...This method [Zhang and Chow 2015a] employs social in.uence in the same way as USG, but it models a personalized geographical check-in probability distribution over latitude and longitude coordinates for each user and combines the social and geographical in.uences by a more robust product rule…...
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...To this end, a recent study [Zhang and Chow 2015c] develops a continuous temporal model based on the kernel density estimation method to build the continuous time probability density of a user visiting a new location....
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...CoRe: This method fuses social collaborative .ltering with geographical check-in probability density over latitude and longitude coordinates [Zhang and Chow 2015a]....
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76 citations
References
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Additional excerpts
...E-mail: jzhang26-c@my.cityu.edu.hk, chiychow@cityu.edu.hk....
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1,048 citations
"TICRec: A Probabilistic Framework t..." refers background in this paper
...To leverage the TIC, TICRec considers both user-based TIC (i.e., different users’ check-in behaviors to the same location at different times) and location-based TIC (i.e., the same user’s check-in behaviors to different locations at different times)....
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...To the best of our knowledge, although there are some studies [16], [17], [18], [19] that investigate the importance of temporal dynamics in human activities, only three existing methods [21], [22], [23] consider the temporal influence to recommend POIs for users in LBSNs....
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...However, all these studies cannot suggest appropriate time for users to visit a recommended POI, because they do not consider the influence of the temporal context when users visiting locations on their check-in behaviors, called temporal influence for short hereafter....
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