ChumkiBasu HaymHirsh WilliamCohen Recommendation as classification: using social and content-based information in recommendation American Association for Artificial Intelligence 714–720 1998 DanielBillsus MichaelJ.Pazzani Learning Collaborative Information Filters Morgan Kaufmann Publishers Inc. 46–54 1998 XiaobinFu JayBudzik KristianJ.Hammond Mining Navigation History for Recommendation. ACM Press 106–112 2000 MihaGrčar BlažFortuna DunjaMladenič MarkoGrobelnik kNN Versus SVM in the Collaborative Filtering Framework Data Science and Classification 251–260 2006 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. WeeSunLee Collaborative Learning and Recommender Systems Morgan Kaufmann Publishers Inc. 314–321 2001 LarsSchmidt-Thieme Compound Classification Models for Recommender Systems. IEEE Computer Society 378-385 2005