Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
A Fair Peer Selection Algorithm for an Ecommerce-Oriented Distributed Recommender System
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
The effect of sparsity on collaborative filtering metrics
ADC '09 Proceedings of the Twentieth Australasian Conference on Australasian Database - Volume 92
e-learning experience using recommender systems
Proceedings of the 42nd ACM technical symposium on Computer science education
Expert Systems with Applications: An International Journal
A collaborative filtering similarity measure based on singularities
Information Processing and Management: an International Journal
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Collaborative filtering recommenders utilize a database of user preferences to make personal product suggestions, and have achieved widespread successes in various e-commerce applications nowadays. Inverse User Frequency is one of most well known approaches to improve the accuracy of the standard collaborative filtering recommender[1]. In this paper, we propose a Statistical Attribute Distance method that uses the similarity in statistics of users' ratings to calculate the user correlation instead of using the statistics of users that rate for similar items. Form our experiment results we suggest the Statistical Attribute Distance outperforms Inverse User Frequency in recommendation accuracy and scalability.