Collaborative Denoising Auto-Encoders for Top-N Recommender Systems
- 8 February 2016
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 153-162
- https://doi.org/10.1145/2835776.2835837
Abstract
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.Keywords
This publication has 21 references indexed in Scilit:
- Collaborative Deep Learning for Recommender SystemsPublished by Association for Computing Machinery (ACM) ,2015
- Local collaborative rankingPublished by Association for Computing Machinery (ACM) ,2014
- FISMPublished by Association for Computing Machinery (ACM) ,2013
- Factorization Machines with libFMACM Transactions on Intelligent Systems and Technology, 2012
- Generalizing matrix factorization through flexible regression priorsPublished by Association for Computing Machinery (ACM) ,2011
- Collaborative topic modeling for recommending scientific articlesPublished by Association for Computing Machinery (ACM) ,2011
- Collaborative competitive filteringPublished by Association for Computing Machinery (ACM) ,2011
- Factorization meets the neighborhoodPublished by Association for Computing Machinery (ACM) ,2008
- Fast maximum margin matrix factorization for collaborative predictionPublished by Association for Computing Machinery (ACM) ,2005
- Item-based collaborative filtering recommendation algorithmsPublished by Association for Computing Machinery (ACM) ,2001