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With the change in internet, way of using it is also changing. Internet not only just provide a way of interaction through operating system but also involving in different fields like artificial intelligence, machine learning etc. Social networking site is mostly used and popular platform of internet. It creates connectivity among people with similar features. Internet service in social networking is making it essential in life of people. Keeping in mind about user nature and interest makes it easy to recommend similar characteristic friend. It also provides a way of enhancing business, promoting products, getting current new, updates etc. It helps to be in touch with our contacts by recommending them similar characteristics of user. Here, a hybrid recommendation model has been proposed and developed to explore the similarity between users based on lifestyle basis. It is the combination of K-mean Clustering and Similarity Weight Calculation to explore the more precise and absolute results. The complete solution is developed using Java technology and evaluated on basis of precision, recall and fscore.

Hybrid Friend Recommendation Approach based on Clustering and Similarity Index