Incorporating Spatial, Temporal, and Social Context in Recommendations for Location-Based Social Networks

Stepan, Torin; Morawski, Jason M.; Dick, Scott; Miller, James

doi:10.1109/tcss.2016.2631473

Cited by 46 publications

(24 citation statements)

References 30 publications

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“…We integrate this geographical model with the Probabilistic Factor Model (PFM). -LMFT [26]: A method that applies temporal information on the user's recent activities and multiple visits to a POI.…”

Section: Methodsmentioning

confidence: 99%

Joint Geographical and Temporal Modeling Based on Matrix Factorization for Point-of-Interest Recommendation

Rahmani

Aliannejadi²,

Baratchi

et al. 2020

Lecture Notes in Computer Science

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With the popularity of Location-based Social Networks, Pointof-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs. Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem. However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences. In this paper, we perform a study on the user mobility patterns where we find out that users' check-ins happen around several centers depending on their current temporal state. Next, we propose a spatio-temporal activity-centers algorithm to model users' behavior more accurately. Finally, we demonstrate the effectiveness of our proposed contextual model by incorporating it into the matrix factorization model under two different settings: i) static and ii) temporal. To show the effectiveness of our proposed method, which we refer to as STACP, we conduct experiments on two well-known real-world datasets acquired from Gowalla and Foursquare LBSNs. Experimental results show that the STACP model achieves a statistically significant performance improvement, compared to the state-of-the-art techniques. Also, we demonstrate the effectiveness of capturing geographical and temporal information for modeling users' activity centers and the importance of modeling them jointly.

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Section: Methodsmentioning

confidence: 99%

Joint Geographical and Temporal Modeling Based on Matrix Factorization for Point-of-Interest Recommendation

Rahmani

Aliannejadi²,

Baratchi

et al. 2020

Lecture Notes in Computer Science

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“…The statistical details of the datasets are presented in Table 1. [18]: A method that considers a user's recent activities as more important than their past activities and multiple visits to a location, as indicates of a stronger preference for that location. -iGLSR 7 [21]: A method that personalizes social and geographical influence on location recommendation using a Kernel Density Estimation (KDE) approach.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…To address this problem and improve the accuracy of POI recommendation, other contextual information such as geographical, temporal, and categorical have been incorporated in the recommendation process [8,16,3]. The analysis of users' behavior indicates that geographical information has a higher impact on users' preference than other contextual information [18,22,6]. As a consequence, several POI recommendation methods have been proposed considering the geographical context [8,11,12,21].…”

Section: Introductionmentioning

confidence: 99%

LGLMF: Local Geographical Based Logistic Matrix Factorization Model for POI Recommendation

Rahmani

Aliannejadi

Ahmadian

et al. 2020

Lecture Notes in Computer Science

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With the rapid growth of Location-Based Social Networks, personalized Points of Interest (POIs) recommendation has become a critical task to help users explore their surroundings. Due to the scarcity of check-in data, the availability of geographical information offers an opportunity to improve the accuracy of POI recommendation. Moreover, matrix factorization methods provide effective models which can be used in POI recommendation. However, there are two main challenges which should be addressed to improve the performance of POI recommendation methods. First, leveraging geographical information to capture both the user's personal, geographic profile and a location's geographic popularity. Second, incorporating the geographical model into the matrix factorization approaches. To address these problems, a POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users' and locations' points of view. To this end, an effective geographical model is proposed by considering the user's main region of activity and the relevance of each location within that region. Then, the proposed local geographical model is fused into the Logistic Matrix Factorization to improve the accuracy of POI recommendation. Experimental results on two well-known datasets demonstrate that the proposed approach outperforms other state-of-the-art POI recommendation methods.

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“…It simulates the generation of POI while simulating the generation of words. The related sampling equations are listed as follows (Corresponding specific parameters are described in Table 1): 1) Firstly, we calculate the posterior probability of the word on the content, as known in Formula (2), and then, sample the content of the word according to the posterior probability,…”

Section: Parameter Estimation Of the Modelmentioning

confidence: 99%

“…In recent years, with the constant development of mobile Internet technology, such as satellite communications, GPS devices, wireless sensor networks and Internet communications, the positioning function of smart terminals provided by people is increasingly accurate and convenient. In this context, Location-Based Social Networks (LBSNs) are rapidly growing and related location-based service are being loved by the majority of users [1,2]. In LBSNs, users are able to publish their geo-tagged information and physical locations in the form of sign-ups and share their experiences with friends on points-of-interest (e.g., shopping malls, restaurants, museums, entertainment venues, hotels, etc.)…”

Section: Introductionmentioning

confidence: 99%

PCRM: Increasing POI Recommendation Accuracy in Location-Based Social Networks

2018

TIIS

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Nowadays with the help of Location-Based Social Networks (LBSNs), users of Point-of-Interest (POI) recommendation service in LBSNs are able to publish their geo-tagged information and physical locations in the form of sign-ups and share their experiences with friends on POI, which can help users to explore new areas and discover new points-of-interest, and promote advertisers to push mobile ads to target users. POI recommendation service in LBSNs is attracting more and more attention from all over the world. Due to the sparsity of users' activity history data set and the aggregation characteristics of sign-in area, conventional recommendation algorithms usually suffer from low accuracy. To address this problem, this paper proposes a new recommendation algorithm based on a novel Preference-Content-Region Model (PCRM). In this new algorithm, three kinds of information, that is, user's preferences, content of the Point-of-Interest and region of the user's activity are considered, helping users obtain ideal recommendation service everywhere. We demonstrate that our algorithm is more effective than existing algorithms through extensive experiments based on an open Eventbrite data set.

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Incorporating Spatial, Temporal, and Social Context in Recommendations for Location-Based Social Networks

Cited by 46 publications

References 30 publications

Joint Geographical and Temporal Modeling Based on Matrix Factorization for Point-of-Interest Recommendation

Joint Geographical and Temporal Modeling Based on Matrix Factorization for Point-of-Interest Recommendation

LGLMF: Local Geographical Based Logistic Matrix Factorization Model for POI Recommendation

PCRM: Increasing POI Recommendation Accuracy in Location-Based Social Networks

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