Feature Aware Factorised Collaborative Filtering
In the area of electronic commerce, recommender systems have become more and more popular. The quality of recommendations depends on the quality of the preference model extracted by the recommender system. Recently, latent factor models based on probabilistic matrix factorisation have gained great attention in both industry and academia, owing to their superior accuracy over traditional recommender systems. Although latent factor models are very efficient, the latency of the features captured in these models impedes explaining the learnt model to the users. A lack of understanding of the latent features makes it difficult to decide on the optimal number of features to give as input to these models. Therefore, the model accuracy degrades when less relevant features are introduced into the model. To tackle this problem, in this paper we propose an extension to the basic matrix factorisation, so that the model takes into account the relevancy of the features beside their values. We test the accuracy of the proposed method on two benchmark datasets. The experiments show that the proposed method makes remarkable improvements over the basic method and some of the state of the art latent factor models.
Australasian Conference on Artificial Intelligence