NDLI: An Evaluation of Structure Based Similarity Indexes for Collaborative Filtering

Content Provider	IEEE Xplore Digital Library
Author	Yong Chen Ming-sheng Shang
Copyright Year	2010
Abstract	Recommender systems are significant for web based e-commerce, and collaborative filtering(CF) algorithms are proved to be the most successful recommendation techniques. For CF algorithms, the similarity index is the heart, and many indexes have been proposed. Recently, with the development of complex networks, the structure based similarity indexes have become a hot topic. In this paper, we implemented six structure based local similarity indexes, i.e., \emph{salton index},\emph{jaccard index}, \emph{ adamic-adar index},\emph{common neighbors},\emph{s{\o}rensen index}, and \emph{RA index}, compared with two common used benchmark indexes, i.e., \emph{cosine index} and \emph{Pearson correlation coefficient}, using seven evaluation metrics, i.e., \emph{mean absolute error}, \emph{rooted mean square error}, \emph{Precise}, \emph{Recall}, \emph{F1},\emph{diversity and novelty } and \emph{half-life utility}. Experimental results demonstrate that the structural based similarity indexes outperform the benchmark indexes.
Starting Page	474
Ending Page	477
File Size	159558
Page Count	4
File Format	PDF
ISBN	9781424453979
e-ISBN	9781424453986
DOI	10.1109/WKDD.2010.53
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-01-09
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering collaborative filtering evaluation metrics Data engineering International collaboration Information filtering Data mining Computer science recommender systems similarity index Filtering algorithms Collaborative work Information filters Recommender systems
Content Type	Text
Resource Type	Article

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