NDLI: An dynamic-weighted collaborative filtering approach to address sparsity and adaptivity issues

Content Provider	IEEE Xplore Digital Library
Author	Liang Gu Peng Yang Yongqiang Dong
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing, China (Liang Gu; Peng Yang; Yongqiang Dong)
Abstract	Recommendation systems, as efficient measures to handle the information overload and personalized service problems, have attracted considerable attention in research community. Collaborative filtering is one of the most successful techniques based on the user-item matrix in recommendation systems. Usually the matrix is extremely sparse due to the massive number of users and items. And the sparsity of users and items tends to differ significantly in degree. The feature of the matrix changes with the variation of users/items data and hence, leads to poor scalability of the recommendation method. This paper proposes a dynamic-weighted collaborative filtering approach (DWCF) to address sparsity and adaptivity issues. In this approach, the relationship between the distributions of similar users and items is considered to get better recommendation, i.e., the contributions of the user part and the item part to recommendation results depend on their similarity ratios. Moreover, the effect strength of different parts is controlled by an averaging parameter. Experiments on MovieLens dataset illustrate that the DWCF approach proposed in this paper can obtain good recommendation result given different conditions of data sparsity and perform better than a user-based predictor, an item-based predictor and a conventional hybrid approach.
Starting Page	3044
Ending Page	3050
File Size	1623274
Page Count	7
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900403
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Collaboration Accuracy Sparse matrices Computational modeling Feature extraction Estimation adaptivity recommendation collaborative filtering sparsity
Content Type	Text
Resource Type	Article

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