Hybrid Recommender System A B2C expert system

Abstract

Today, the concept of recommender systems have become a fundamental application in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. . This has helped users in finding items that they would like to buy or consider based on huge amounts of data present on the websites. Recommender Systems have evolved to fulfill the natural dual need of buyers and sellers by automating the generation of recommendations based on data analysis. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. These recommendations techniques are divided so based on varying inputs to the system, such as items popular on the company’s Website; user characteristics such as geographical location or product preference or other personal information; or past buying behavior of top customers. Since each of these techniques has its own pros and cons, thus so as to improve performance level and reduce the chances of flaws, these methods are generally combined and used which are known as hybrid recommenders. As my final year project, I also intend to implement one such model of a hybrid recommender system which will combine the knowledge based and collaborative filtering techniques for a shopping website where in any customer according to his needs, taste and physical appearance shall get a filtered list of available options.