An efficient collaborative recommender system based on k-separability

Authors:
Georgios Alexandridis;Georgios Siolas;Andreas Stafylopatis
Affiliations:
Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Year:
2010

Citing 7
Cited 0

Amazon.com Recommendations: Item-to-Item Collaborative Filtering

IEEE Internet Computing
Learning Collaborative Information Filters

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Hybrid Approach to Making Recommendations and Its Application to the Movie Domain

AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
A Projection Pursuit Algorithm for Exploratory Data Analysis

IEEE Transactions on Computers
A new constructive algorithm for architectural and functional adaptation of artificial neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
K-Separability

ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Most recommender systems usually have too many items to recommend to too many users using limited information. This problem is formally known as the sparsity of the ratings' matrix, because this is the structure that holds user preferences. This article outlines a collaborative recommender system, that tries to amend this situation. The system is built around the notion of k-separability combined with a constructive neural network algorithm.