Collaborative Denoising Auto-Encoders for Top-N Recommender Systems

Yao Wu, Christopher DuBois, Alice X. Zheng, Martin Ester
2016 Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM '16  
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
more » ... 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.
doi:10.1145/2835776.2835837 dblp:conf/wsdm/WuDZE16 fatcat:6c42mglclngplieo3zwqch4lza