Abstract
Aiming at the problem that traditional similarity calculation method is not enough to capture the similarity relationship between users, a distributed collaborative filtering recommendation algorithm based on DHT is proposed for active users in distributed recommendation system to locate nearest neighbors. The algorithm searches the user’s similar neighbor information according to the “fuzzy critical value” generated by the user’s extreme score, so as to improve the search efficiency of the user similar neighbors. According to the distribution of user information, the calculation method of user neighbor similarity is improved. The similarity calculation is weighted, and the setting of the weights takes into account the two factors: the similarity degree between users and the inverse user information frequency.
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Acknowledgements
We thank the anonymous reviewers and the editors for the valuable feedback on earlier versions of this paper. This paper is supported by the National Statistical Science Research Project of China, under Grant Number 2015LY43.
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Wang, T., Wang, M. Distributed collaborative filtering recommendation algorithm based on DHT. Cluster Comput 22 (Suppl 2), 2931–2941 (2019). https://doi.org/10.1007/s10586-018-1699-9
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DOI: https://doi.org/10.1007/s10586-018-1699-9