Location recommendation in location-based social networks using user check-in data

Wang, Hao; Terrovitis, Manolis; Mamoulis, Nikos

doi:10.1145/2525314.2525357

Cited by 208 publications

(150 citation statements)

References 24 publications

(36 reference statements)

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“…Usually, a user prefers to visit locations that are close to his or her residential address or office [4,32,33]. When the distance of the location from a user's home increases, the user's probability of visiting that location decreases.…”

Section: Spatial Similaritymentioning

confidence: 99%

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An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks

Khazaei

Alimohammadi

2018

IJGI

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Spatial group recommendation refers to suggesting places to a given set of users. In a group recommender system, members of a group should have similar preferences in order to increase the level of satisfaction. Location-based social networks (LBSNs) provide rich content, such as user interactions and location/event descriptions, which can be leveraged for group recommendations. In this paper, an automatic user grouping model is introduced that obtains information about users and their preferences through an LBSN. The preferences of the users, proximity of the places the users have visited in terms of spatial range, users' free days, and the social relationships among users are extracted automatically from location histories and users' profiles in the LBSN. These factors are combined to determine the similarities among users. The users are partitioned into groups based on these similarities. Group size is the key to coordinating group members and enhancing their satisfaction. Therefore, a modified k-medoids method is developed to cluster users into groups with specific sizes. To evaluate the efficiency of the proposed method, its mean intra-cluster distance and its distribution of cluster sizes are compared to those of general clustering algorithms. The results reveal that the proposed method compares favourably with general clustering approaches, such as k-medoids and spectral clustering, in separating users into groups of a specific size with a lower mean intra-cluster distance.

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Section: Spatial Similaritymentioning

confidence: 99%

“…Visits are reported explicitly (by user check-ins in known venues and locations) or implicitly by allowing for smartphone applications to report visited locations to the LBSN. This information is then shared with other users who are socially related (e.g., friends) [4].…”

Section: Introductionmentioning

confidence: 99%

An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks

Khazaei

Alimohammadi

2018

IJGI

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“…Six approaches used implicit indications. For instance, to recommend locations of interest, Wang, et al [130] utilized users' check-in records of visited locations and the timestamps and Konstas, et al [65] used users' music play-counts to recommend a song to enjoy.…”

Section: Input Data Types Of Social Link-based Recommendationsmentioning

confidence: 99%

Recommendations Based on Social Links

Lee

Brusilovsky

2018

Social Information Access

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Abstract. The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues and the future direction. Among several branches of social recommendations, specifically, this chapter focuses on recommendations, which are founded on users' self-defined (i.e., explicit) social links and target to suggest desirable items, not social links of interests. This chapter provides a brief overview over the technical needs for social link-based recommendations and the studies explaining the viability of users' social networks as useful information sources from a social science point of view. Then, the in-depth understanding of existing recommendations based on users' social links will be addressed. Lastly, various issues and future directions of social link-based recommendation research are put forward. IntroductionThe remarkable popularity of social media encourages web users to participate in various online activities and to generate data on an unprecedented scale. Given exponentially growing social media data volumes, personalized recommendation technologies have proven the effectiveness as a solution of the information overload problem; have offered positive user experiences with more relevant contents; and have presented competitive advantage to the social media business [119]. As predictive analysis of online users' tastes, recommendation technology adaptively filters out unnecessary data and selectively chooses presumably favorable information according to users' preferences. At odds with the original principle of personalized recommendations, which is to give users control of information access, however, the typical and most popular collaborative filtering recommendation technology performs all steps autonomously and allows no room for users to get involved in information personalization of their own. The lacking user involvement causes several problems (illustrated in Section 12.3.1), and many researchers have called attention to solutions to improve the quality of typical recommendation technology and to cope with the relevant problems. Among the several streams of research to develop the solutions, one important evolution is to take advantage of users' self-defined online social networks and proposes to generate recommendations based on users' social linkssocial link-based recommendations. The idea to utilize users' online social links as a foundation of recommendation is undoubtedly spurred by the recent phenomenon of online social networks.Compared with the era when web users stayed in isolation, a number of social media systems have been adapting online sociability, helping users to find people of interest, and as well as encouraging them to socially associate with people of interest. In fact, web users' active and eager participation on social media are intrinsically motivated by not only personal desire for information management and knowledge acquisition but also social desire for engagement and communication with...

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“…Techniques of personalised POI recommendation with geographic influence and social connections mainly study these two elements separately and then combine their output together within a fused model. Social influence is usually modelled through friend-based collaborative filtering [4,11,12] with the assumption that a user tends to be friends with other users who are geographically close to him or would want to visit similar places to those visited by his friends. Ying et al [13] proposed to combine the social factor with individual preferences and location popularity within a regression-tree model to recommend POIs.…”

Section: Related Workmentioning

confidence: 99%

Spatio-semantic user profiles in location-based social networks

Mohamed

Abdelmoty

2017

Int J Data Sci Anal

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Knowledge of users' visits to places is one of the keys to understanding their interest in places. Usercontributed annotations of place, the types of places they visit, and the activities they carry out, add a layer of important semantics that, if considered, can result in more refined representations of user profiles. In this paper, semantic information is summarised as tags for places and a folksonomy data model is used to represent spatial and semantic relationships between users, places, and tags. The model allows simple co-occurrence methods and similarity measures to be applied to build different views of personalised user profiles. Basic profiles capture direct user interactions, while enriched profiles offer an extended view of users' association with places and tags that take into account relationships in the folksonomy. The main contributions of this work are the proposal of a uniform approach to the creation of user profiles on the Social Web that integrates both the spatial and semantic components of user-provided information, and the demonstration of the effectiveness of this approach with realistic datasets.

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Location recommendation in location-based social networks using user check-in data

Cited by 208 publications

References 24 publications

An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks

An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks

Recommendations Based on Social Links

Spatio-semantic user profiles in location-based social networks

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