NDLI: A Collaborative Filtering Recommendation Algorithm Based on SVD Smoothing

Content Provider	IEEE Xplore Digital Library
Author	YiBo Ren SongJie Gong
Copyright Year	2009
Abstract	Recommender system is one of the most important technologies in electronic commerce. And the collaborative filtering is almost the popular approach used in the recommender systems. With the development of electronic commerce systems, the magnitudes of users and items grow rapidly, resulted in the extreme sparsity of user rating data set. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. Sparsity of users’ ratings is the major reason causing the poor quality. To address this issue, a collaborative filtering recommendation algorithm based on singular value decomposition (SVD) smoothing is presented. This approach predicts item ratings that users have not rated by the employ of SVD technology, and then uses Pearson correlation similarity measurement to find the target users’ neighbors, lastly produces the recommendations. The collaborative filtering recommendation algorithm based on SVD smoothing can alleviate the sparsity problems of the user item rating dataset, and can provide better recommendation than traditional collaborative filtering algorithms.
Starting Page	530
Ending Page	532
File Size	317657
Page Count	3
File Format	PDF
ISBN	9780769538594
DOI	10.1109/IITA.2009.491
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	collaborative filtering Smoothing methods sparsity Information filtering Electronic commerce Matrix decomposition recommender system Collaboration Filtering algorithms Information filters Collaborative work Recommender systems singular value decomposition Singular value decomposition
Content Type	Text
Resource Type	Article

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