TY - GEN
T1 - A model-based approach to collaborative filtering by neural networks
AU - Nachev, Anatoli
AU - Ganchev, Ivan
AU - Rowland, Jacqueline
PY - 2005
Y1 - 2005
N2 - Most recommender systems use collaborative filtering to predict new items of interest for a user. In this paper we present a model-based approach to collaborative filtering by using unsupervised self-organising ART2 neural networks which deploys two of the main advantages of the ART model - stability and plasticity when the system works in poorly defined domains and planning of network resources is difficult or even impossible We report empirical results that show the impact of ART2 NN parameters on recognition stability, appropriate category granularity, classification accuracy, and response time.
AB - Most recommender systems use collaborative filtering to predict new items of interest for a user. In this paper we present a model-based approach to collaborative filtering by using unsupervised self-organising ART2 neural networks which deploys two of the main advantages of the ART model - stability and plasticity when the system works in poorly defined domains and planning of network resources is difficult or even impossible We report empirical results that show the impact of ART2 NN parameters on recognition stability, appropriate category granularity, classification accuracy, and response time.
KW - Adaptive resonance theory
KW - ART2
KW - Collaborative filtering
KW - Neural networks
UR - https://www.scopus.com/pages/publications/60749087601
M3 - Conference Publication
AN - SCOPUS:60749087601
SN - 9781932415667
SN - 193241567X
SN - 9781932415674
T3 - Proceedings of the 2005 International Conference on Artificial Intelligence, ICAI'05
SP - 846
EP - 852
BT - Proceedings of the 2005 International Conference on Artificial Intelligence, ICAI'05
T2 - 2005 International Conference on Artificial Intelligence, ICAI'05
Y2 - 27 June 2005 through 30 June 2005
ER -