Title: The wisdom of the few: a provable approach

Authors: Xiao-Yu Huang; Xian-Hong Xiang

Addresses: School of Economics and Commerce, South China University of Technology, Guangzhou, China ' Department of Interventional Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

Abstract: In recent years, the wisdom of the few (WOF) model has attracted substantial research interests. The WOF refers to the findings that in some collaborative prediction tasks, e.g., collaborative filtering (CF), with only the ratings from a small set of expert users, it nearly suffices to predict a much larger number of other users' unobserved ratings. In this paper, we propose a WOF algorithm for the CF problem, and prove that under some mild statistical assumptions, the algorithm can predict the users' missing ratings correctly with high probability guarantee. We also conduct CF experiments with the proposed algorithm on real datasets; the results show that our algorithm is competitive with the conventional CF algorithm.

Keywords: collaborative filtering; crowdsourcing; expert systems; wisdom of the crowd.

DOI: 10.1504/IJCSE.2019.10017874

International Journal of Computational Science and Engineering, 2019 Vol.18 No.1, pp.21 - 28

Received: 01 Mar 2017
Accepted: 03 Jul 2017

Published online: 14 Dec 2018 *

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