Comment-based Multi-View Clustering of Web 2.0 Items∗

Clustering Web 2.0 items (i.e., web resources like videos, images) into semantic groups benefits many applications, such as organiz-ing items, generating meaningful tags and improving web search. In this paper, we systematically investigate how user-generated com-ments 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 qual-ity, 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 Factoriza-tion), which extends NMF for multi-view clustering by jointly fac-torizing 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 [24]. On a Yelp dataset, CoNMF outperforms the best baseline CoSC with a statistically significant performance gain of 7%