A Club CF Approach for Big Data Applications

Abstract

In the recent days the web domain is augmented with new types of services, with the increase in service and cloud computing. As a result new forms of web content collecting/designing is done based on the numerous openly available web services online. These services are utilized in many ways by different domains and with the exponential growth of these web services users are experiencing difficulties in finding and utilizing a best matching service for their mashups. A collaborative filtering approach is going to filter and recognize the similar services under same cluster and followed by those evaluations recommendations are made.
Recommender systems are now popular both commercially and in the research community, where many approaches have been suggested for providing recommendations. In many cases a system designer that wishes to employ a recommendation system must choose between a set of candidate approaches. A first step towards selecting an appropriate algorithm is to decide which properties of the application to focus upon when making this choice. Indeed, recommendation systems have a variety of properties that may affect user experience, such as accuracy, robustness, scalability, and so forth. In this paper the system discuss how to compare recommenders based on a set of properties that are relevant for the application.
Recommender systems can now be found in many modern applications that expose the user to huge collections of items. Such systems typically provide the user with a list of recommended items they might prefer, or predict how much they might prefer each item. These systems help users to decide on appropriate items, and ease the task of finding preferred items in the collection.