| Authors: |
Miha Grčar
and Blaž Fortuna
and Dunja Mladenič
and Marko Grobelnik
|
| URL: |
http://db.cs.ualberta.ca/webkdd05/proc/paper25-mladenic.pdf |
| Description: |
SpringerLink - Book Chapter |
| Tags: |
classification
kde
knn
learning
projekt
recommender
seminar
svm
ws07
|
| Abstract: |
We present experimental results of confronting the k-Nearest Neighbor (kNN) algorithm with Support Vector Machine (SVM) in
the collaborative filtering framework using datasets with different properties. While k-Nearest Neighbor is usually used forthe collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algorithm. Sincecollaborative filtering can also be interpreted as a classification/regression task, virtually any supervised learning algorithm(such as SVM) can also be applied. Experiments were performed on two standard, publicly available datasets and, on the otherhand, on a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering. We concludethat the quality of collaborative filtering recommendations is highly dependent on the quality of the data. Furthermore, wecan see that kNN is dominant over SVM on the two standard datasets. On the real-life corporate dataset with high level ofsparsity, kNN fails as it is unable to form reliable neighborhoods. In this case SVM outperforms kNN. |
@article{keyhere,
title = {kNN Versus SVM in the Collaborative Filtering Framework},
author = {Miha Grčar and Blaž Fortuna and Dunja Mladenič and Marko Grobelnik},
journal = {Data Science and Classification},
pages = {251--260},
url = {http://db.cs.ualberta.ca/webkdd05/proc/paper25-mladenic.pdf},
year = {2006},
description = {SpringerLink - Book Chapter},
abstract = {We present experimental results of confronting the k-Nearest Neighbor (kNN) algorithm with Support Vector Machine (SVM) in
the collaborative filtering framework using datasets with different properties. While k-Nearest Neighbor is usually used forthe collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algorithm. Sincecollaborative filtering can also be interpreted as a classification/regression task, virtually any supervised learning algorithm(such as SVM) can also be applied. Experiments were performed on two standard, publicly available datasets and, on the otherhand, on a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering. We concludethat the quality of collaborative filtering recommendations is highly dependent on the quality of the data. Furthermore, wecan see that kNN is dominant over SVM on the two standard datasets. On the real-life corporate dataset with high level ofsparsity, kNN fails as it is unable to form reliable neighborhoods. In this case SVM outperforms kNN.},
doi = {http://dx.doi.org/10.1007/3-540-34416-0_27},
keywords = {classification kde knn learning projekt recommender seminar svm ws07 }
}
::