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User Cold Start Recommendation System Based on Hofstede Cultural Theory
Abstract
The main function of recommendation systems is to help users select satisfactory services from many services. Existing recommendation systems usually need to conduct a questionnaire survey of the user or obtain the user's third-party information in the case of cold start users; this operation often infringes on the user's privacy. This article is aimed at providing accurate recommendations for cold start users without infringement on user privacy. Therefore, in response to this problem, this manuscript per the authors proposes a recommendation algorithm based on Hofstede's cultural dimensions theory. The algorithm uses Hofstede's cultural dimensions theory to establish a connection between two cold start users, thus ensuring the stability of QoS prediction accuracy. Then, the prediction results and the dynamic combination of the matrix factorization algorithm are used to obtain a more accurate prediction. The verification results on the real dataset WS-Dream show that the prediction algorithm proposed in this paper effectively alleviates the user cold start problem.
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