Trust-aware location recommendation in location-based social networks: A graph-based approach. (1st March 2023) - Canturk, Deniz; Karagoz, Pinar; Kim, Sang-Wook; Toroslu, Ismail Hakki
Abstract: With 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%. Highlights: A novel trust based location recommendation framework is proposed. Generated global trust score per user on the basis of the check-in behavior. Developed a latent trust model to leverage the objectivity and consistency features. Incorporated trustworthy user nodes into LBSN graph to obtain trust-aware model. Social connections, trustworthiness, and spatial-context employed together in our model.