TY - GEN
T1 - Towards a more comprehensive comparison of collaborative filtering algorithms
AU - González-Caro, Cristina N.
AU - Calderón-Benavides, Maritza L.
AU - De Pérez-Alcázar, José J.
AU - Garcáa-Díaz, Juan C.
AU - Delgado, Joaquin
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.
PY - 2002
Y1 - 2002
N2 - The basic objective of a Collaborative Filtering (CF) algorithm is to suggest items to a particular user based on his/her preferences and users with similar interests. Although, there is an apparently strong demand for CF techniques, and many algorithms have been recently proposed, very few articles comparing these techniques can be found. Our paper is oriented towards the study of a sample of algorithms to representing differents stages in the evolutive process of CF. Experiments were conducted on two datasets with different characteristics, using two protocols and three evaluation metrics for the different algorithms. The results indicate that, in general, the Online-Learning (WMA, MWM) and the Support Vector Machines algorithms have a better performance that the other algorithms, on both datasets. Considering the amount of information, the less sparse such information is, the higher the coverage and accuracy of general models tend to be; however, the behavior under sparse data is closer to what is observed in a real system if we have in mind that users usually rate an amount of records much smaller than the total available.
AB - The basic objective of a Collaborative Filtering (CF) algorithm is to suggest items to a particular user based on his/her preferences and users with similar interests. Although, there is an apparently strong demand for CF techniques, and many algorithms have been recently proposed, very few articles comparing these techniques can be found. Our paper is oriented towards the study of a sample of algorithms to representing differents stages in the evolutive process of CF. Experiments were conducted on two datasets with different characteristics, using two protocols and three evaluation metrics for the different algorithms. The results indicate that, in general, the Online-Learning (WMA, MWM) and the Support Vector Machines algorithms have a better performance that the other algorithms, on both datasets. Considering the amount of information, the less sparse such information is, the higher the coverage and accuracy of general models tend to be; however, the behavior under sparse data is closer to what is observed in a real system if we have in mind that users usually rate an amount of records much smaller than the total available.
KW - Aspect model
KW - Collaborative filtering
KW - Dependency networks
KW - Memory based models
KW - Online learning
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=36248994270&partnerID=8YFLogxK
U2 - 10.1007/3-540-45735-6_22
DO - 10.1007/3-540-45735-6_22
M3 - Libros de Investigación
AN - SCOPUS:36248994270
SN - 3540441581
SN - 9783540441588
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 248
EP - 253
BT - String Processing and Information Retrieval - 9th International Symposium, SPIRE 2002, Proceedings
A2 - Laender, Alberto H. F.
A2 - Oliveira, Arlindo L.
PB - Springer Verlag
T2 - 9th International Symposium on String Processing and Information Retrieval, SPIRE 2002
Y2 - 11 September 2002 through 13 September 2002
ER -