Trust-aware location recommendation in location-based social networks: A graph-based approach

Date

2023-03-01

Author

Canturk, Deniz
Karagöz, Pınar
Kim, Sang-Wook
Toroslu, İsmail Hakkı

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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© 2022 Elsevier LtdWith the increase in the use of mobile devices having location-related capabilities, the use of Location-Based Social Networks (LBSN) has also increased, allowing users to share location-embedded information with other users in the social network. By leveraging check-in activities provided by LBSNs, personalized recommendations can be provided. Trust is an important concept in social networks to improve recommendation quality. In this work, we develop a method for predicting the trust scores of LBSN users and propose a trust-aware recommendation technique, TLoRW, to recommend locations to users based on their previous check-ins, the social network, and predicted trust scores of users. In the proposed model, global trust score of user is generated on the basis of check-in history. In addition to trust, spatial context is anther important aspect of TLoRW to generate location recommendations based on the current location of a user. The proposed algorithm runs on a contextual subgraph rather full graph, relaxing the computing resource requirement. We represent a given LBSN with a undirected graph model. Recommendation scores of the locations are generated as the result of the random walk performed on the trust augmented LBSN subgraph. A comprehensive evaluation of TLoRW is conducted by comparing its recommendation performance against baseline techniques, as well as state-of-the-art trust-aware recommendation approaches in the literature, based on benchmark datasets. The experiments reveal that the trust information incorporated into random-walk-based approach improves the accuracy of the recommended locations @5 by minimum 5%.

Subject Keywords

Heterogeneous graph, Information fusion, Location-based social networks, Random walk, Trust score prediction, Trust-aware recommendation

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85140476955&origin=inward
https://hdl.handle.net/11511/99984

Journal

Expert Systems with Applications

DOI

https://doi.org/10.1016/j.eswa.2022.119048

Collections

Department of Computer Engineering, Article

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Citation Formats

D. Canturk, P. Karagöz, S.-W. Kim, and İ. H. Toroslu, “Trust-aware location recommendation in location-based social networks: A graph-based approach,” Expert Systems with Applications, vol. 213, pp. 0–0, 2023, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85140476955&origin=inward.