Designing a Recommender System Based on Social Networks and Location Based Services

Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.

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Location-sharing protocol for privacy protection in mobile online social networks

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01999-z ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Ou Ruan ◽

Lixiao Zhang ◽

Yuanyuan Zhang

Keyword(s):

Social Networks ◽

Private Information ◽

Online Social Networks ◽

Smart Cities ◽

Location Based Services ◽

Broadcast Encryption ◽

Location Sharing ◽

Identity Privacy ◽

And Performance ◽

Network Server

AbstractLocation-based services are becoming more and more popular in mobile online social networks (mOSNs) for smart cities, but users’ privacy also has aroused widespread concern, such as locations, friend sets and other private information. At present, many protocols have been proposed, but these protocols are inefficient and ignore some security risks. In the paper, we present a new location-sharing protocol, which solves two issues by using symmetric/asymmetric encryption properly. We adopt the following methods to reduce the communication and computation costs: only setting up one location server; connecting social network server and location server directly instead of through cellular towers; avoiding broadcast encryption. We introduce dummy identities to protect users’ identity privacy, and prevent location server from inferring users’ activity tracks by updating dummy identities in time. The details of security and performance analysis with related protocols show that our protocol enjoys two advantages: (1) it’s more efficient than related protocols, which greatly reduces the computation and communication costs; (2) it satisfies all security goals; however, most previous protocols only meet some security goals.

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