Suvash Sedhain
ABSTRACT
This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRec's compact and efficiently trainable model outperforms state-of-the-art CF techniques (biased matrix factorization, RBM-CF and LLORMA) on the Movielens and Netflix datasets.
- Y. Koren, R. Bell, C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42 (8), 2009. Google Scholar
Digital Library
- J. Lee, S. Kim, G. Lebanon, Y. Singer. Local low-rank matrix approximation. ICML, 2013.Google ScholarDigital Library
- M. Riedmiller, H. Braun. A direct adaptive method for faster backpropagation learning: the RProp algorithm. IEEE International Conference on Neural Networks, 1993.Google ScholarCross Ref
- R. Salakhutdinov, A. Mnih, G. Hinton. Restricted Boltzmann machines for collaborative filtering. ICML, 2007. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, J. Riedl. Item-based collaborative filtering recommendation algorithms. WWW, 2001. Google ScholarDigital Library
Index Terms
- AutoRec: Autoencoders Meet Collaborative Filtering
Recommendations
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Collaborative filtering based on an iterative prediction method to alleviate the sparsity problem
iiWAS '09: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & ServicesCollaborative filtering (CF) is one of the most popular recommender system technologies. It tries to identify users that have relevant interests and preferences by calculating similarities among user profiles. The idea behind this method is that, it may ...
A framework for diversifying recommendation lists by user interest expansion
Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...
Comments