research-article

Comment-based multi-view clustering of web 2.0 items

Authors:

Xiao ChenAuthors Info & Claims

WWW '14: Proceedings of the 23rd international conference on World wide web

Pages 771 - 782

https://doi.org/10.1145/2566486.2567975

Published: 07 April 2014 Publication History

Abstract

Clustering Web 2.0 items (i.e., web resources like videos, images) into semantic groups benefits many applications, such as organizing items, generating meaningful tags and improving web search. In this paper, we systematically investigate how user-generated comments can be used to improve the clustering of Web 2.0 items. In our preliminary study of Last.fm, we find that the two data sources extracted from user comments -- the textual comments and the commenting users -- provide complementary evidence to the items' intrinsic features. These sources have varying levels of quality, but we importantly we find that incorporating all three sources improves clustering. To accommodate such quality imbalance, we invoke multi-view clustering, in which each data source represents a view, aiming to best leverage the utility of different views.

To combine multiple views under a principled framework, we propose CoNMF (Co-regularized Non-negative Matrix Factorization), which extends NMF for multi-view clustering by jointly factorizing the multiple matrices through co-regularization. Under our CoNMF framework, we devise two paradigms -- pair-wise CoNMF and cluster-wise CoNMF -- and propose iterative algorithms for their joint factorization. Experimental results on Last.fm and Yelp datasets demonstrate the effectiveness of our solution. In Last.fm, CoNMF betters k-means with a statistically significant F1 increase of 14%, while achieving comparable performance with the state-of-the-art multi-view clustering method CoSC (Co-regularized Spectral Clustering). On a Yelp dataset, CoNMF outperforms the best baseline CoSC with a statistically significant performance gain of 7%.

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Index Terms

Comment-based multi-view clustering of web 2.0 items
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Other conferences

WWW '14: Proceedings of the 23rd international conference on World wide web

April 2014

926 pages

ISBN:9781450327442

DOI:10.1145/2566486

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Copyright © 2014 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 07 April 2014

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National Research Foundation-Prime Minister's office, Republic of Singapore

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WWW '14

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IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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WWW '14 Paper Acceptance Rate 84 of 645 submissions, 13%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Wang ZZhu YLi YQiang JYuan YZhang C(2024)Asymmetric Short-Text Clustering via PromptNew Generation Computing10.1007/s00354-024-00244-742:4(599-615)Online publication date: 19-Feb-2024
https://doi.org/10.1007/s00354-024-00244-7
Fang ULi MLi JGao LJia TZhang Y(2023)A Comprehensive Survey on Multi-View ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327031135:12(12350-12368)Online publication date: 1-Dec-2023
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