Overcoming neighborhood based collaborative filtering in the online shopping for the user recommendation

Nowadays due to the information overload the individual users did not obtaining their own relevant products which they are specified. So the recommendations to the individual users can reduce the load to the user whenever they are buying the products. However, certain algorithms where introduced to improve the quality of the aggregate diversity concept. By improving this concept the aggregate diversity of the certain products can be obtained. In this paper I have explored the aggregate diversity concepts which are obtained from the items that are individually ranked and displayed. This will be more effective in the application such as E-Commerce and E-Bay. In the proposed approach efficient recommendations are obtained to the user by which the aggregate diversity is achieved with the required products.