Data Sparsity Issues in the Collaborative Filtering Framework

Abstract. With the amount of available information on the Web growing rapidly with each day, the need to automatically filter the information in order to ensure greater user efficiency has emerged. Within the fields of user profiling and Web personalization several popular content filtering techniques have been developed. In this chapter we present one of such techniques ? collaborative filtering. Apart from giving an overview of collaborative filtering approaches, we present the 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 the k-Nearest Neighbor algorithm is usually used for collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algorithm. Since collaborative filtering can also be interpreted as a classification/regression task, virtually any supervised learning algorithm (such as SVM) ca...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Miha Grcar, Dunja Mladenic, Blaz Fortuna, Marko Grobelnik
• Keywords: Collaborative Filtering, Collaborative Filtering Recommendations, Collaborative Filtering Tasks, Data Mining, KDD 2005