NDLI: Improving the accuracy of Tagging Recommender System by Using Classification

Content Provider	IEEE Xplore Digital Library
Author	Jian Song Liang He Xin Lin
Copyright Year	2010
Description	Author affiliation: Department of Computer Science and Technology, East China Normal University, Shanghai, China (Jian Song; Liang He; Xin Lin)
Abstract	Collaborative tagging system has become more and more popular and recently achieved widespread success due to flexibility and conceptual comprehensibility of tagging systems. Recommender system has the access to adopt tagging systems to achieve better performance. In this paper we consider that the items can be categorized into different classifications in which users show different interests. Here we adopt a two-step recommender method called TRSUC (Tagging Recommender Systems by Using Classification) which can be described as Inner-Class Recommender or Global Recommender in which we use tag as the intermediary entity between user and item. The experiment using MovieLens as dataset shows that we acquire better results than the recommender algorithms without classifying the items.
Starting Page	387
Ending Page	391
File Size	2986769
Page Count	5
File Format	PDF
ISBN	9781424454273
ISSN	17389445
e-ISBN	9781424454280
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-02-07
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	Electronics and Telecommunications Research Institute
Subject Keyword	Tagging Recommender systems Filtering algorithms Collaboration Motion pictures Collaborative work Helium Merchandise Information resources Information retrieval Classification Collaborative Tagging Recommender System
Content Type	Text
Resource Type	Article

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