A Survey Paper on different Clustering techniques for Collaborative Filtering for services recommendation
Collaborative filtering (CF) is a technique commonly used to build personalized recommendations on the Web. Some popular websites that make use of the collaborative filtering technology include Amazon, Netflix, iTunes, IMDB. Data sparseness is the most important issue in CF. Data sparseness causes the system difficulty in determining the nearest neighbours of the target user accurately. Clustering can solve the problem of data sparseness. Grouping a set of physical or abstract objects into classes of similar objects, this process is called as clustering. This paper presents the methods to generate recommendations using clusteringbased collaborative filtering approach. Keywords Clustering, Collaborative filtering, Nearest Neighbours, Data sparseness, Personalised recommendations.