NDLI: A Recommendation Model for Social Resource Sharing Systems Based on Tripartite Graph Clustering

Content Provider	IEEE Xplore Digital Library
Author	Ustunbas, Y. Oguducu, S.G.
Copyright Year	2011
Abstract	The use of folksonomies to recommend web pages and tags assigned to these pages, is an important research direction in web recommendation. In this study, we implement a model that fits tripartite structure of folksonomies and extracts valuable information for generating recommendations. Then we developed two types of recommendation systems that take advantage of this information, web page recommendation and tag recommendation. We compared our recommendation results with the results using bipartite clustering of web pages and tags. The experiments are conducted on the data set obtained from Delicious web site. The results show that this model generates better accuracy results for web page recommendation while extracting more useful information simultaneously which could be an extra to generate different types of recommendations.
Starting Page	378
Ending Page	381
File Size	326970
Page Count	4
File Format	PDF
ISBN	9781457714641
DOI	10.1109/EISIC.2011.56
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-12
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Social resource sharing system Web pages Clustering algorithms Folksonomy Tagging Recommendation model Data models Data mining Recommender systems
Content Type	Text
Resource Type	Article

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A Recommendation Model for Social Resource Sharing Systems Based on Tripartite Graph Clustering