A Survey on Collaborating Techniques and Qos Based Recommendation System

The immense growth in internet technologies leads to increase in size of the web service repository. Normally, all the service requestor expects very qualitative resultant web service for their request. These requestors may not have previous knowledge about their requesting domain. So it is difficult for them to filter out the relevant web service from huge pool of data. Moreover, the resulting of irrelevant services for the user request will affect the user satisfaction. Recommender system is being widely used to recommend products or items to consumer. This system can also be used to recommend a service or a list of service to service requestor. Collaborative filtering technique (CF) is one the efficient recommending system that recommends the service based on the past users experiences or ratings on that service. The past users are the nearest neighbors to the requestors. Traditional CF does the user-based and item-based similarity computation between the users and items for recommendation. They do not take into account nonfunctional components (QOS parameters) of the service which greatly have impact on performance. This paper is a review about CF technique and need of QOS parameter for the recommendation system to improve the performance.