Collaborative Denoising Auto Encoders for Top N Recommender Systems
Most real world recommender services measure their performance based on the top N results shown to the end users. Thus, advances in top N recommendation have far ranging consequences in practical applications. In this paper, we present a novel method, called Collaborative Denoising Auto Encoder (CDAE) for top N recommendation that utilizes the idea of Denoising Auto Encoders. We demonstrate that the proposed model is a generalization of several well known collaborative filtering models but with more flexible components. Thorough experiments are conducted to understand the performance of CDAE under various component settings. Furthermore, experimental results on several public datasets demonstrate that CDAE consistently outperforms state of the art top N recommendation methods on a variety of common evaluation metrics.
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