TRUST-AWARE LOCATION RECOMMENDATION IN LOCATION-BASED SOCIAL NETWORKS

2021-8-9
Cantürk, Deniz
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 performance through integrated trust information. In both of the algorithms, the elements of LBSN and their relationships (user-user, user-location) are represented by using a graph model. For trust modeling, we develop a method to generate trust scores of LBSN users. With the developed method, the global trust score of a user is predicted with respect to the check-in history. The trust model is integrated into the LBSN graph model to be used within the proposed location recommendation algorithms. The first algorithm, TLoRW, generates location recommendations based on the user’s current location by exploiting the friendships, experts, and trusted users traversing the region of user’s spatial context through a random walk based algorithm. This region is constructed as the subgraph of the user according to the current location. In the second recommendation algorithm, SgWalk, we consider user subgraph as a heterogeneous information network and propose a novel HIN embedding technique. The location recommendation is generated by the proximity between users and locations based on their corresponding node embedding. SgWalk is differentiated from the previous node embedding techniques relying on meta-path or bi-partite graphs by utilizing the user subgraphs generated based on spatial context. By this way, it is aimed to capture the relationship between the entities with respect to the spatial context. The recommendation performance of TLoRW and SgWalk is analyzed through extensive experiments conducted on benchmark datasets by evaluating the accuracy in top-k location recommendations. The experiments reveal that trust information has a significant effect on improving the location recommendation performance. The performance evaluation results show a substantial improvement compared to baseline techniques and the state-of-the-art trust-aware recommendation and heterogeneous graph embedding techniques in the literature.

Suggestions

Trust-aware location recommendation in location-based social networks: A graph-based approach
Canturk, Deniz; Karagöz, Pınar; Kim, Sang-Wook; Toroslu, İsmail Hakkı (2023-03-01)
© 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 sco...
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...
Developing recommendation techniques for location based social networks using random walk
Bağcı, Hakan; Karagöz, Pınar; Department of Computer Engineering (2015)
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 thesis, we propose three recommendation algorithms for location-based social networks. These are random walk based context-aware location (CLoRW), activity (RWCAR) and friend (RWCFR) recommendation algorithms. All the algorithms consider the current context (i.e. curre...
Group oriented trust-aware location recommendation for location-based social networks
Teoman, Huseyin Alper; Karagöz, Pınar (2022-04-25)
© 2022 ACM.With the increasing popularity of social networks and online communities, group recommendation systems arise in order to support users to interact with those having similar interests, and to provide recommendations for joint activities, such as eating out as a group or seeing a movie with friends. However, the techniques and approaches to provide recommendations to groups are limited, as most of the available studies focus on individual recommendations. In this study, we address the problem of re...
Citation Formats
D. Cantürk, “TRUST-AWARE LOCATION RECOMMENDATION IN LOCATION-BASED SOCIAL NETWORKS,” Ph.D. - Doctoral Program, Middle East Technical University, 2021.