Context-aware location recommendation by using a random walk-based approach

Date

2016-05-01

Author

Bagci, Hakan
Karagöz, Pınar

Metadata

Show full item record

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

Item Usage Stats

91
views

0
downloads

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 recommendations. We build a graph model of LBSNs for performing a random walk approach with restart. Random walk is performed to calculate the recommendation probabilities of the nodes. A list of locations are recommended to users after ordering the nodes according to the estimated probabilities. We compare our algorithm, CLoRW, with popularity-based, friend-based and expert-based baselines, user-based collaborative filtering approach and a similar work in the literature. According to experimental results, our algorithm outperforms these approaches in all of the test cases.

Subject Keywords

Location-Based Social Networks, Location Recommendation, Context-Aware Recommendation, Random Walk

URI

https://hdl.handle.net/11511/42327

Journal

KNOWLEDGE AND INFORMATION SYSTEMS

DOI

https://doi.org/10.1007/s10115-015-0857-0

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

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...
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...
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...
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...
Time Preference aware Dynamic Recommendation Enhanced with Location, Social Network and Temporal Information Ozsoy, Makbule Gulcin; Polat, Faruk; Alhajj, Reda (2016-08-21) Social networks and location based social networks have many active users who provide various kind of data, such as where they have been, who their friends are, which items they like more, when they go to a venue. Location, social network and temporal information provided by them can be used by recommendation systems to give more accurate suggestions. Also, recommendation systems can provide dynamic recommendations based on the users' preferences, such that they can give different recommendations for differ...

Citation Formats

H. Bagci and P. Karagöz, “Context-aware location recommendation by using a random walk-based approach,” KNOWLEDGE AND INFORMATION SYSTEMS, pp. 241–260, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42327.