Gaussian Ranking by Matrix Factorization

By Harald Steck. 2015

31 citations recorded by the source corpus.

The ranking quality at the top of the list is crucial in many real-world applications of recommender systems. In this paper, we present a novel framework that allows for pointwise as well as listwise training with respect to various ranking metrics. This is based on a training objective function where we assume that, for given a user, the recommender system predicts scores for all items that follow approximately a Gaussian distribution. We motivate this assumption from the properties of implicit feedback data. As a model, we use matrix factorization and extend it by non-linear activation functions, as customary in the literature of artificial neural networks. In particular, we use non-linear activation functions derived from our Gaussian assumption. Our preliminary experimental results show that this approach is competitive with state-of-the-art methods with respect to optimizing the Area under the ROC curve, while it is particularly effective in optimizing the head of the ranked list.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research, Recommender Systems

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Citations in the corpus

Variational Autoencoders for Collaborative Filtering (2018, background): “Asymmetric matrix factorization [35] may also be interpreted as an autoencoder, as elaborated in Steck [44].”
Session-based Recommendations with Recurrent Neural Networks (2015, neutral): “Although the task can also be interpreted as a classiﬁcation task, learning-to-rank approaches (Rendle et al., 2009; Shi et al., 2012; Steck, 2015) generally outperform other approaches.”