kNN Versus SVM in the Collaborative Filtering Framework
Miha Grcar, Blaz Fortuna and Dunja Mladenić
In: Workshop on Knowledge Discovery in the Web, 21 Aug 2005, Chicago, USA.

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 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.

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