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

Metadata

Show full item record

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

Item Usage Stats

191
views

0
downloads

© 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

Suggestions

OpenMETU
Core

TRUST-AWARE LOCATION RECOMMENDATION IN LOCATION-BASED SOCIAL NETWORKS Cantürk, Deniz; Karagöz, Pınar; Department of Computer Engineering (2021-8-9) Users can share their location with other social network users through location-embedded information in LBSNs (Location-Based Social Network). LBSNs contain useful resources, such as user check-in activities, for building a personalized recommender system. Trust in social networks is another important concept that has been integrated into a recommendation system in various settings. In this thesis, we propose two novel techniques for location recommendation, TLoRW and SgWalk, to improve recommendation perfo...
Context-aware location recommendation by using a random walk-based approach Bagci, Hakan; Karagöz, Pınar (2016-05-01) The location-based social networks (LBSN) enable users to check in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a context-aware location recommendation system for LBSNs using a random walk approach. Our proposed approach considers the current context (i.e., current social relations, personal preferences and current location) of the user to provide personalized ...
Context aware friend recommendation for location based social networks using random walk Bağcı, Hakan; Karagöz, Pınar (null; 2016-04-10) The location-based social networks (LBSN) facilitate users to check-in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a random walk based context-aware friend recommendation algorithm (RWCFR). RWCFR considers the current context (i.e. current social relations, personal preferences and current location) of the user to provide personalized recommendations. Our LBSN...
Random Walk Based Context-Aware Activity Recommendation for Location Based Social Networks Bagci, Hakan; Karagöz, Pınar (2015-10-21) The pervasiveness of location-acquisition technologies enable location-based social networks (LBSN) to become increasingly popular in recent years. Users are able to check-in their current location and share information with other users through these networks. LBSN check-in data can be used for the benefit of users by providing personalized recommendations. There are several location recommendation algorithms that employ LBSN data in the literature. However, there are few number of proposed activity recomme...
Highly personalized information delivery to mobile clients Ozen, B; Kilic, O; Altinel, M; Doğaç, Asuman (Springer Science and Business Media LLC, 2004-11-01) The inherent limitations of mobile devices necessitate information to be delivered to mobile clients to be highly personalized according to their profiles. This information may be coming from a variety of resources like Web servers, company intranets, email servers. A critical issue for such systems is scalability, that is, the performance of the system should be in acceptable limits when the number of users increases dramatically. Another important issue is being able to express highly personalized informa...

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.