Exploiting Geographical Neighborhood Characteristics for Location Recommendation

Liu, Yong; Wei, Wei; Sun, Aixin; Miao, Chunyan

doi:10.1145/2661829.2662002

Cited by 296 publications

(175 citation statements)

References 26 publications

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“…LRT [4], which investigates the temporal properties of user check-in behaviors and proposes a location recommendation framework with temporal effects. IRenMF [14], which incorporates two levels of geographical neighborhood into the learning process of latent features of users and locations.…”

Section: Datasets Metrics and Set Upmentioning

confidence: 99%

A Point-of-Interest Recommender System Based on Factorization Machines

Tu¹,

Wang²

2018

dtcse

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Abstract. With the rapid development of location-based social networks (LBSNs), Point-of-Interest (POI) recommendation which can benefit both users and service providers has attracted much academic and industrial interest. Although various aspects of users' behavior data have been utilized in POI recommendation, challenges such as data sparsity and how to accurately model users' preferences exist. In this paper, we propose a model named as Geo-STFM which exploits social, temporal and geographical information for POI recommendation based on factorization machines. Specifically, we first give a precise analysis of the self-similar and other-similar characteristics in users' behaviors, and accordingly propose an improved factorization machines (FM) model with social and temporal regularization terms. Then, geographical information is fused into the model to build a POI recommender system. The experimental results on two real-world data sets show that our proposed method achieves remarkable improvements compared to the state-of-the-art POI recommendation techniques.

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Section: Datasets Metrics and Set Upmentioning

confidence: 99%

A Point-of-Interest Recommender System Based on Factorization Machines

Tu¹,

Wang²

2018

dtcse

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show abstract

“…In [46,47], matrix factorization has evolved as a critical algorithm in location recommendation, where a user's preference of a venue is modeled as an inner product of latent factors. To take advantage of mutually exploring the latent features of users and locations with implicit incorporation of external information beyond users' check-in data to alleviate the data sparsity problem, Ye et al [48], Gao et al [49], and Noulas et al [37] employed collaborative filtering models, leveraging the similarity between users on mobility patterns and social relationships, for POI recommendations.…”

Section: Location Recommendationmentioning

confidence: 99%

Prediction of Suspect Location Based on Spatiotemporal Semantics

Duan

et al. 2017

IJGI

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Abstract:The prediction of suspect location enables proactive experiences for crime investigations and offers essential intelligence for crime prevention. However, existing studies have failed to capture the complex social location transition patterns of suspects and lack the capacity to address the issue of data sparsity. This paper proposes a novel location prediction model called CMoB (Crime Multi-order Bayes model) based on the spatiotemporal semantics to enhance the prediction performance. In particular, the model groups suspects with similar spatiotemporal semantics as one target suspect. Then, their mobility data are applied to estimate Markov transition probabilities of unobserved locations based on a KDE (kernel density estimating) smoothing method. Finally, by integrating the total transition probabilities, which are derived from the multi-order property of the Markov transition matrix, into a Bayesian-based formula, it is able to realize multi-step location prediction for the individual suspect. Experiments with the mobility dataset covering 210 suspects and their 18,754 location records from January to June 2012 in Wuhan City show that the proposed CMoB model significantly outperforms state-of-the-art algorithms for suspect location prediction in the context of data sparsity.

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“…The authors found that geographical influence has a significant impact on the accuracy of POI recommendations, whereas the social friends contribute little to the accuracy. Liu et al [22] incorporated instance and region level of geographical neighborhood characteristics into the learning of latent features of users and locations. Gao et al [9] propose the concept of geo-social correlations of users' check-in activities, which considers both social networks and geographical distance to model four types of social correlations (i.e., local friends, distant friends, local non-friends and distant non-friends).…”

Section: Related Workmentioning

confidence: 99%

Selecting and weighting users in collaborative filtering based POI recommendation

2017

APH

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Exploiting Geographical Neighborhood Characteristics for Location Recommendation

Cited by 296 publications

References 26 publications

A Point-of-Interest Recommender System Based on Factorization Machines

A Point-of-Interest Recommender System Based on Factorization Machines

Prediction of Suspect Location Based on Spatiotemporal Semantics

Selecting and weighting users in collaborative filtering based POI recommendation

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