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Context-aware location recommendation by using a random walk-based approach
Date
2016-05-01
Author
Bagci, Hakan
Karagöz, Pınar
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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
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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.