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

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Abstract

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.

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Authors and Affiliations

  1. Computer Engineering Department, Middle East Technical University, Ankara, Turkey

    Hakan Bagci & Pinar Karagoz

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  1. Hakan Bagci
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  2. Pinar Karagoz
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Correspondence to Hakan Bagci.

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Bagci, H., Karagoz, P. Context-aware location recommendation by using a random walk-based approach. Knowl Inf Syst 47, 241–260 (2016). https://doi.org/10.1007/s10115-015-0857-0

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