Hybrid Approach For Efficient Diversification on Cloud Stored Large Dataset

With enormous amount of data being stored and searched on web, quick and efficient search of relevant data has become a challenge. Recently Diversification has been proposed as an approach to facilitate user's to search large data set without having to look through all relevant results. !mong different diversification approach Gossip Based Recommendation has become popular because it provides Scalability, automaticity, dynamicity and decentralized control. Here each user has the flexibility to construct cluster of relevant datasets which will be further used in the processing of queries. Due to user involvement the chances of redundancy amongst users increase considerably. In this paper we introduced the usefulness score which will be an indication of the data relevance. This usefulness score is then merged with the distance based access method of Space Partitioning and Probing to further minimize the number of accessed objects. So the objective is to improve performance by accessing only small number of objects which guarantees the quality result in terms of relevance and diversification.