A HITS-based POI recommendation algorithm for location-based social networks

Long, Xuelian; Joshi, James

doi:10.1145/2492517.2492652

Cited by 30 publications

(15 citation statements)

References 16 publications

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“…The datasets for POI recommendations usually include global-positioning-system-(GPS-) based trajectory and the check-in data of LBSNs [14]. While numerous studies [15][16][17] have developed POI recommendations based on GPS trajectory data, these approaches first mined the sequence of semantic POIs visited, which is represented by the check-in data. Additionally, check-in data provide additional information markers (e.g., social interactions, POI types, or semantics in the spatial layout) that are especially useful in capturing latent relationships among users of the same POI type.…”

Section: Related Workmentioning

confidence: 99%

Discovering Travel Community for POI Recommendation on Location-Based Social Networks

et al. 2019

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Point-of-interest (POI) recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks (LBSNs). The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users’ check-in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple users. The proposed method aims to predict destination POIs of a user and search for similar users of the same regions of interest, thus optimizing the user acceptance rate for each recommendation. The proposed method also employs the variable-order Markov model to determine the distribution of a user’s POIs based on his or her travel histories in LBSNs. To further enhance the user’s experience, we also apply linear discriminant analysis to cluster the topics related to “Travel” and connect to users with social links or similar interests. The probability of POIs based on users’ historical trip data and interests in the same topics can be calculated. The system then provides a list of the recommended destination POIs ranked by their probabilities. We demonstrate that our work outperforms collaborative-filtering-based and other methods using two real-world datasets from New York City. Experimental results show that the proposed method is better than other models in terms of both accuracy and recall. The proposed POI recommendation algorithms can be deployed in certain online transportation systems and can serve over 100,000 users.

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Section: Related Workmentioning

confidence: 99%

Discovering Travel Community for POI Recommendation on Location-Based Social Networks

et al. 2019

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“…Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm also known as hub and authorities, wherein the authority value estimates the value of the content of a page and the hub value estimates the value of its links to other pages. In [7] proposal for a HITS based POI recommendation algorithm is discussed. It incorporates the impact of the social relationships on recommendations.…”

Section: Related Work On Recommendation Systemsmentioning

confidence: 99%

A Study on Recommendation Systems in Location Based Social Networking

Kullappa

Kumar²,

Kullappa³

2017

J. inf. organ. sci. (Online)

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Smart devices in the hands of people are revolutionizing the social lifestyle of one's self. Everyone across the world are using smart devices linked to their social networking activities one such activity is to share location data by uploading the tagged media content like photos, videos. The data is of surroundings, events attended/attending and travel experiences. Users share their experiences at a given location through localization techniques.  Using such data from social networks an attempt is made to analyse tagged media content to acquire information on user context, individual’s interests, tastes, behaviours and derive meaningful relationships amongst them are referred to as Location Based Social Networks (LBSNs). The resulting information can be used to market a product and to improve business, as well recommend a travel and plan an itinerary. This paper presents a comprehensive survey of recommended systems for LBSNs covering the concepts of LBSNs, terminologies of LBSN and various recommendation systems.

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“…In [7], the authors investigated various features for location prediction on LBSNs, and reported that the number of check-ins made by friends is a significant predictor. In [23], the authors proposed a HITS-based POI recommendation algorithm to recommend POIs to LBSN users with the consideration of social relationships. In [36], the checkin information from nearby friends was utilized for location recommendation while other users were ignored.…”

Section: Related Workmentioning

confidence: 99%

Addressing the cold-start problem in location recommendation using geo-social correlations

Gao

Tang

Liu

2014

Data Min Knowl Disc

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Location-based social networks (LBSNs) have attracted an increasing number of users in recent years, resulting in large amounts of geographical and social data. Such LBSN data provide an unprecedented opportunity to study the human movement from their socio-spatial behavior, in order to improve location-based applications like location recommendation. As users can check-in at new places, traditional work on location prediction that relies on mining a user's historical moving trajectories fails as it is not designed for the cold-start problem of recommending new check-ins. While previous work on LBSNs attempting to utilize a user's social connections for location recommendation observed limited help from social network information. In this work, we propose to address the cold-start location recommendation problem by capturing the correlations between social networks and geographical distance on LBSNs with a geo-social correlation model. The experimental results on a real-world LBSN dataset demonstrate that our approach properly models the geo-social correlations of a user's cold-start check-ins and significantly improves the location recommendation performance.

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A HITS-based POI recommendation algorithm for location-based social networks

Cited by 30 publications

References 16 publications

Discovering Travel Community for POI Recommendation on Location-Based Social Networks

Discovering Travel Community for POI Recommendation on Location-Based Social Networks

A Study on Recommendation Systems in Location Based Social Networking

Addressing the cold-start problem in location recommendation using geo-social correlations

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