Collaborative Filtering Recommender System

There has been an increase in the number of services available in the internet .Datasets are growing at a fast pace as it is being gathered and generated by a number of devices like smartphones, tablets and various information sensing devices. Traditional data processing methods are ineffective in handling such a huge amount of data related to services within limited time constraints. Most of the present day recommendation systems use structured data. In order to handle the large amount of data relevant to the services and assist a user in selecting a service which is most relevant a collaborative filtering based recommender system is proposed which uses unstructured data. There are three stages in this method. In the first stage porters stemming algorithm is applied ,then clustering is applied on the data, in order to reduce the number of services, in the last stage a filtering approach is used in order to recommend relevant services to the user. As stemming and clustering are applied before filtering recommendations are done at a faster pace. Keywords: stemming, clustering, collaborative filtering, pearson coefficient, recommender system.