NDLI: A Personalized Recommender Integrating Item-Based and User-Based Collaborative Filtering

Content Provider	IEEE Xplore Digital Library
Author	XiaoYan Shi HongWu Ye SongJie Gong
Copyright Year	2008
Description	Author affiliation: Zhejiang Bus. Technol. Inst., Ningbo (XiaoYan Shi; HongWu Ye; SongJie Gong)
Abstract	Recommender systems employ prediction algorithms to provide users with items that match their interests. The collaborative filtering (CF) is the most popular system and the two of the most famous techniques in CF are the user-based CF (UBCF) and item-based CF (IBCF). Nevertheless each of them takes only one-directional information from the user-item ratings matrix to generate recommendations. In other words, the UBCF utilizes user similarities and the IBCF tries to make a prediction by utilizing item similarities. It means that methods may use only half of the total information from the given data set. For completing the missing part of usable information, this paper proposes a CF algorithm integrating the UBCF and IBCF, which takes both vertical and horizontal information in the user-item matrix. It produces perdition using IBCF to form a dense user-item matrix and then recommends using UBCF based on the dense matrix. The experimental results on MovieLens dataset show that the proposed algorithm outperformed in terms of prediction accuracy and robustness to data sparseness.
Sponsorship	Eng. Technol. Press, Hong Kong
Starting Page	264
Ending Page	267
File Size	452437
Page Count	4
File Format	PDF
ISBN	9780769535609
DOI	10.1109/ISBIM.2008.191
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	user-based collaborative filtering sparsity Information filtering Electronic mail Information management personalized recommender Seminars item-based collaborative filtering Accuracy Databases Collaboration Textile technology Filtering algorithms Information filters
Content Type	Text
Resource Type	Article

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