Comment-based multi-view clustering of web 2.0 items
By Xiangnan He, Min-Yen Kan, Peichu Xie, Xiao Chen. 2014
108 citations recorded by the source corpus.
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%.
Subfields: Computer Vision and Pattern Recognition, Information Systems, Statistical and Nonlinear Physics
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Citations in the corpus
- Neural Collaborative Filtering (2017, methodology): “To build a multi-media recommender system, we need to develop effective methods to learn from multi-view and multi-modal data [13, 40].”
- Fast Matrix Factorization for Online Recommendation with Implicit Feedback (2016, methodology): “To make our method more applicable to real-world settings, we plan to encode side information such as user social contexts [8] and reviews [9] by extending eALS to more generic models, such as collective factorization [11] and Factorization machines [26].”
- Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks (2017, background): “Another promising direction is to develop FM variants for semi-supervised and multi-view learning, for example by incorporating the widely used graph Laplacian [He et al. , 2017a; Wang et al. , 2016] and co-regularization designs [He et al. , 2014; Yang et al. , 2015].”
- Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (2014, unknown): “Copyright 2014 ACM 978-1-4503-2257-7/14/07 ...$15.00. http://dx.doi.org/10.1145/2600428.2609579.”
- TriRank: Review-aware Explainable Recommendation by Modeling Aspects (2015, background): “Recent research [10, 11, 17, 19] have largely modeled reviews at the word level, distilling them into latent topics to combine with the latent factor model.”
- Unifying Virtual and Physical Worlds (2017, background): “…early fusion [Blaschko and Lampert 2008; Chaudhuri et al. 2009] and joint learning [Bruno and Marchand-Maillet 2009; Gao et al. 2013a; Kumar and Daum´e 2011; Kumar et al. 2011; Liu et al. 2012; Zhang et al. 2015; Zeng et al. 2006; Boden et al. 2012; Cai et al. 2013; He et al. 2014; Li et al. 2014].”