Affiliation:
1. Chongqing University of Posts and Telecommunications, China
2. Deakin University, Australia
Abstract
The Neighbourhood-based collaborative filtering (CF) algorithm has been widely used in recommender systems. To enhance the adaptability to the sparse data, a CF with new similarity measure and prediction method is proposed. The new similarity measure is designed based on the Hellinger distance of item labels, which overcomes the problem of depending on common-rated items (co-rated items). In the proposed prediction method, we present a new strategy to solve the problem that the neighbour users do not rate the target item, that is, the most similar item rated by the neighbour user is used to replace the target item. The proposed prediction method can significantly improve the utilisation of neighbours and obviously increase the accuracy of prediction. The experimental results on two benchmark datasets both confirm that the proposed algorithm can effectively alleviate the sparse data problem and improve the recommendation results.
Funder
the MEO Layout Fundation of Humanities and Social Sciences
the Foundation of Guangxi Key Laboratory of Cryptography and Information Security
National Natural Science Foundation of China
Subject
Library and Information Sciences,Information Systems
Cited by
2 articles.
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