Abstract
Many sophisticated recommendation methods have been developed to produce recommendations to the users. Among them, Random Walk with Restart (RWR) is one of the most widely used techniques. However, RWR has a large time complexity of \(O(k(n+m)^3)\) and memory complexity of \((O(n+m)^2)\). The change reducing the computational complexity is of great practical importance. In this paper, we propose an optimized version of random walk with restart, called the Optimized Random Walk with Restart (ORWR) and conduct theoretical and empirical studies on its performance. Mathematical analysis shows that using this technique the time complexity reduces to \(O(nm^2)\) and the memory complexity to O(nm). Experiments on three different recommendation problems using real-world datasets confirms the proposed ORWR method improves both time and memory cost of the recommendation.
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Acknowledgments
The research is supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant to Xin Wang and Behrouz Far, and National Natural Science Foundation of China (No. 61772420).
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Rahimi, S.M., de Oliveira e Silva, R.A., Far, B., Wang, X. (2019). Optimized Random Walk with Restart for Recommendation Systems. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_26
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DOI: https://doi.org/10.1007/978-3-030-18305-9_26
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