A Neural Autoregressive Approach to Collaborative Filtering
This addresses the problem of recommendation systems for users by providing an incremental improvement over existing methods.
The paper tackled collaborative filtering tasks by proposing CF-NADE, a neural autoregressive architecture that outperformed all previous state-of-the-art methods on MovieLens 1M, MovieLens 10M, and Netflix datasets, with performance improving further by adding more hidden layers.
This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribution Estimator (NADE). We first describe the basic CF-NADE model for CF tasks. Then we propose to improve the model by sharing parameters between different ratings. A factored version of CF-NADE is also proposed for better scalability. Furthermore, we take the ordinal nature of the preferences into consideration and propose an ordinal cost to optimize CF-NADE, which shows superior performance. Finally, CF-NADE can be extended to a deep model, with only moderately increased computational complexity. Experimental results show that CF-NADE with a single hidden layer beats all previous state-of-the-art methods on MovieLens 1M, MovieLens 10M, and Netflix datasets, and adding more hidden layers can further improve the performance.