2008 OneClassCollaborativeFiltering

• (Pan et al., 2008) ⇒ Rong Pan, Yunhong Zhou, Bin Cao, Nathan N. Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. (2008). “One-Class Collaborative Filtering.” In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining. ISBN:978-0-7695-3502-9 doi:10.1109/ICDM.2008.16

Subject Headings: Weighted Matrix Factorization.

Cited By

• http://scholar.google.com/scholar?q=%222008%22+One-Class+Collaborative+Filtering
• http://dl.acm.org/citation.cfm?id=1510528.1511402&preflayout=flat#citedby

2009

• (Rendle et al., 2009) ⇒ Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. (2009). “BPR: Bayesian Personalized Ranking from Implicit Feedback.” In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. ISBN:978-0-9749039-5-8
  • QUOTE: Both Pan et al., 2008 and Hu et al., 2008 have presented a matrix factorization method for item prediction from implicit feedback. Thus the model class is the same as we described in Section 4.3.1, i.e. [math]\displaystyle{ \hat{X} := WH^t }[/math] with the matrices [math]\displaystyle{ W : |U| \times k }[/math] and [math]\displaystyle{ H : |U| \times k }[/math]. The optimization criterion and learning method differ substantially from our approach. Their method is an adaption of a SVD, which minimizes the square-loss. Their extensions are regularization to prevent overfitting and weights in the error function to increase the impact of positive feedback. In total their optimization criterion is: [math]\displaystyle{ \Sigma_{u \in U} \Sigma_{i \in I} C_{ui}( \lt wa-u, h_i\gt - 1)^2 | \lambda || W ||^2_f + \lambda ||H||^2_f }[/math] where [math]\displaystyle{ c_{ui} }[/math] are not model parameters but apriori given weights for each tuple [math]\displaystyle{ (u,i) }[/math]. … Pan et al. suggest to set [math]\displaystyle{ c_{ui} = 1 }[/math] for positive feedback and choose lower constants for the rest.

Quotes

Abstract

Many applications of collaborative filtering (CF), such as news item recommendation and bookmark recommendation, are most naturally thought of as one-class collaborative filtering (OCCF) problems. In these problems, the training data usually consist simply of binary data reflecting a user's action or inaction, such as page visitation in the case of news item recommendation or webpage bookmarking in the bookmarking scenario. Usually this kind of data are extremely sparse (a small fraction are positive examples), therefore ambiguity arises in the interpretation of the non-positive examples. Negative examples and unlabeled positive examples are mixed together and we are typically unable to distinguish them. For example, we cannot really attribute a user not bookmarking a page to a lack of interest or lack of awareness of the page. Previous research addressing this one-class problem only considered it as a classification task. In this paper, we consider the one-class problem under the CF setting. We propose two frameworks to tackle OCCF. One is based on weighted low rank approximation; the other is based on negative example sampling. The experimental results show that our approaches significantly outperform the baselines.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 OneClassCollaborativeFiltering	Qiang Yang Rong Pan Yunhong Zhou Bin Cao Nathan N. Liu Rajan Lukose Martin Scholz			One-Class Collaborative Filtering				10.1109/ICDM.2008.16		2008