An Efficient Framework of Hybrid Recommendation System based on Multi Mode

Abstract

Recommendation systems have been widely applied in many areas, such as E-commerce, and so on. However, in some complex systems such as missed sparse data, it will be increasingly difficult to build a model for user recommendations. In this research we develop a recommendation system on E-Commerce. This system will be able to adapt and provide the best recommendations for each user dynamically even in sparse environment. The system will be created in a web-based application to display the product recommendations to users. The recommendation system developed is expected to be able to solve cold-start problem when there is no other relevant data to be recommended for the new added product and also the sparsity problem. To overcome this problem, the system will implement multi-mode algorithm that uses more than one search algorithm for the closest characteristics in the recommendation system and can choose one of the best algorithms to use in accordance with the existing data and hybrid-filtering that can use a combination of Collaborative Filtering is to make recommendations based on information equations between users and Content-Based Filtering is to make recommendations based on information representation of a content. Thus the system will be able to provide product recommendations on any state of data on E-Commerce.