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 usu- ally used for the collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algo- rithm. Since collaborative filtering can also be interpreted as a classification/regression task, virtually any supervised learning algorithm (such as SVM) can also be applied. Ex- periments were performed on two standard, publicly avail- able datasets and, on the other hand, on a real-life corporate dataset that does not fit the profile of ideal data for collab- orative filtering. We conclude that the quality of collabo- rative filtering recommendations is highly dependent on the quality of the data. Furthermore, we can see that kNN is dominant over SVM on the two standard datasets. On the real-life corporate dataset with high level of sparsity, kNN fails as it is unable to form reliable neighborhoods. In this case SVM outperfroms kNN.
CITATION STYLE
Grčar, M., Fortuna, B., Mladenič, D., & Grobelnik, M. (2006). kNN Versus SVM in the Collaborative Filtering Framework. In Data Science and Classification (pp. 251–260). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-34416-0_27
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