NDLI: A Hybrid User and Item-Based Collaborative Filtering with Smoothing on Sparse Data

Content Provider	IEEE Xplore Digital Library
Author	Rong Hu Yansheng Lu
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan (Rong Hu; Yansheng Lu)
Abstract	Collaborative filtering, the most successful recommender system technology to date, helps people make choices based on the opinions of other people. Existing collaborative filtering methods, mainly user-based and item-based methods, predict new ratings by aggregating rating information from either similar users or items. However, a large amount of ratings of similar items or similar users may be unavailable because of the sparse characteristic inherent to the rating data. For this reason, we present a Hybrid Predictive Algorithm with Smoothing (HSPA). HSPA uses item-based methods to provide the basis for data smoothing and builds the predictive model based on both users' aspects and items' aspects in order to ensure robust to data sparsity and predictive accuracy. Moreover, HSPA utilizes the user clusters to achieve high scalability. Experimental results from real datasets show that HSPA effectively contributes to the improvement of prediction on sparse data
Sponsorship	VRSJ (Japan)
Starting Page	184
Ending Page	189
File Size	258861
Page Count	6
File Format	PDF
ISBN	076952754X
DOI	10.1109/ICAT.2006.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Smoothing methods Accuracy Collaboration Clustering algorithms Predictive models Information filters Prediction algorithms Robustness Information filtering Recommender systems
Content Type	Text
Resource Type	Article

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