Collaborative Filtering Algorithm Based on User Clustering

Zhao Deng; Jin Wang

doi:10.4028/www.scientific.net/AMM.411-414.1044

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 411-414Collaborative Filtering Algorithm Based on User...

Collaborative Filtering Algorithm Based on User Clustering

Abstract:

To overcome the uncertainty of the users neighborhoods in the recommendation algorithm of nearest neighbor, an improved collaborative filtering algorithm based on user clustering is proposed. This improved algorithm filters the users by their features, and the improved cosine similarity algorithm is used for the item similarity computation. Experiments on the MovieLens dataset showed that, compared with Lis collaborative filtering algorithm, the recommendation quality of the improved algorithm is more accurate and the category coverage is larger.

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Periodical:

Applied Mechanics and Materials (Volumes 411-414)

Pages:

1044-1048

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.411-414.1044

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Online since:

September 2013

Authors:

Zhao Deng, Jin Wang

Keywords:

Collaborative Filtering (CF), Recommender System, User Clustering

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