Collaborative Filtering Recommendation Algorithm Integrated into Co-Rating Impact Factor

Xu Yang Wang; Heng Liu

doi:10.4028/www.scientific.net/AMR.926-930.3004

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 926-930Collaborative Filtering Recommendation Algorithm...

Collaborative Filtering Recommendation Algorithm Integrated into Co-Rating Impact Factor

Abstract:

The sparsity rating data is one of the main challenges of recommendation system. For this problem, we presented a collaborative filtering recommendation algorithm integrated into co-ratings impact factor. The method reduced the sparsity of rating matrix by filling the original rating matrix. It made the full use of rating information and took the impact on similarity of co-ratings between users into consideration when looking for the nearest neighbor so that the similarities were accurately computed. Experimental results showed that the proposed algorithm, to some extent, improved the recommendation accuracy.

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Periodical:

Advanced Materials Research (Volumes 926-930)

Pages:

3004-3007

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.926-930.3004

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Online since:

May 2014

Authors:

Xu Yang Wang*, Heng Liu

Keywords:

Collaborative Filtering (CF), Co-Ratings, Impact Factor, Recommendation, Sparsity

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