Recommendation in Online Electronic Devices Shopping System using Item-to-Item Collaborative Filtering Method

Abstract

The World Wide Web information grows explosively in the Internet and people encounter problem to pick some correct things from the overwhelming set of choices. The recommender systems help them choose something they actually want or need. Many of the E-commerce Web sites are already using recommender systems to facilitate their buyingprocess. Collecting taste information from many users is important to recommend relevant items to the user. The system intends to recommend most relevant items in the online electronic devices shopping system. In this system Item-to-Item collaborative filtering method makes recommendations about the interest of a user based on history data. Cosine similarity is used to compute the similarity between two item pairs.