NDLI: A Semantic Category Recommendation System Exploiting LDA Clustering Algorithm and Social Folksonomy

Content Provider	IEEE Xplore Digital Library
Author	Hyung-Rak Jo Kyung-Wook Park Jae-Ik Kim Dong-Ho Lee
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Hanyang Univ., Seoul, South Korea (Hyung-Rak Jo; Kyung-Wook Park; Jae-Ik Kim; Dong-Ho Lee)
Abstract	According to a widespread use of the internet, the amount of data generated from various Social Network Services (SNSs) is increasing day by day. Thus, it has become necessary to categorize data for users to efficiently access to the desired information. However, most of the web sites do not provide the categorizing service. Even a few sites that offer the categorizing service do not support user-oriented automatic category generation with a high-quality performance. In addition, there are several limitations in an analysis of large amounts of data because of a high dimension of vectors when clustering data sets for the category generation. This paper proposes a system that provides users with a service for recommending categories by utilizing social folksonomy with clustered data. Further, a method to reduce the dimension of vectors by removing meaningless words in the contents is introduced.
Starting Page	644
Ending Page	645
File Size	162188
Page Count	2
File Format	PDF
ISSN	07303157
e-ISBN	9781467365642
DOI	10.1109/COMPSAC.2015.84
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-01
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Sports equipment Games Complexity theory Dictionaries Pipelines Semantics latent topics social folksonomy automatic category generation dimension reduction tag cluster
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Software

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