HybridSVD: When Collaborative Information is Not Enough
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We propose a hybrid algorithm for top-n recommendation task that allows to incorporate both user and item side information within the standard collaborative filtering approach. The algorithm extends PureSVD -- one of the state-of-the-art latent factor models -- by exploiting a generalized formulation of the singular value decomposition. This allows to inherit key advantages of the classical algorithm such as highly efficient Lanczos-based optimization procedure, minimal parameter tuning during a model selection phase and a quick folding-in computation to generate recommendations instantly even in a highly dynamic online environment. Within the generalized formulation itself we provide an efficient scheme for side information fusion which avoids undesirable computational overhead and addresses the scalability question. Evaluation of the model is performed in both standard and cold-start scenarios using the datasets with different sparsity levels. We demonstrate in which cases our approach outperforms conventional methods and also provide some intuition on when it may give no significant improvement.
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