Modeling human location data with mixtures of kernel densities

Lichman, Moshe; Smyth, Padhraic

doi:10.1145/2623330.2623681

Cited by 119 publications

(83 citation statements)

References 21 publications

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“…We implement and evaluate our method based on the CARSKit Lib [10] and compare our method with the following stat-of-the-art POI techniques(the parameters are from our experiments or suggested by previous or related works): PMF [12] which out performs traditional collaborative filtering methods in the situation of big data sets or data sparsity.WRMF [13] which is a matrix factorization method widely used for modeling implicit feedback, many works have used this method for user preference modeling. USG [8], which integrates user preferences,social and geographical information for location recommendation. LRT [4], which investigates the temporal properties of user check-in behaviors and proposes a location recommendation framework with temporal effects.…”

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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“…is calculated as follows. Recall that visited location may have more than one category: = getBinaryDataSet( , , ); (8) train binary classifier using data set ; (9) calculate accuracy , of classifier on validation data set; (10) if > Acc then (11) A c c = ; (12) = ; (13) end ( Considering independence assumption between previous visited categories we can write (2) as…”

Section: Considering Previous Visited Place and Timementioning

confidence: 99%

“…or where he was when given tweets were published? References [11][12][13] have shown that by using Twitter data we can predict the location of user with high accuracy. But the granularity level of predicting the user location using tweets is at either the country level or regional level.…”

Section: Related Workmentioning

confidence: 99%

Twitter Can Predict Your Next Place of Visit

Chauhan

Tejwani

Toshniwal

2016

International Journal of Distributed Sensor Networks

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The present work focuses on predicting users' next place of visit using their past tweets. We hypothesize that tweets of the person have predictive power on his location and therefore can be used to predict his next place of visit. This problem is important for location based advertising and recommender based services. To predict the next place of visit, we calculate the probabilities of visiting different types of places using bank of binary classifiers and Markov models. More specifically, we train bank of binary classifiers on past tweets and calculated the probabilities of visiting next places. Since bank of binary classifiers is based on a bagof-words model, to account for time of last visited place and place itself, we built Markov models for different time duration to calculate probabilities of visiting next place. Empirical evaluation shows that by combining the probabilities obtained from bank of binary classifiers and Markov models the accuracy of predicting next place increased from 65% to 80%.

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“…A wide range of approaches for providing mobility predictions, including Markov-based [1], Compression-based [6], Mixture model-based [7], Trajectory-based [8] and many others have been proposed, all with the singular aim of providing a predicted future location or locations, either in the short term or the long term, for a given mobile user. However, this format of predictions is too restrictive.…”

Section: Introductionmentioning

confidence: 99%