A predictive approach for improving the sales of products in e-commerce
Z. A. Usmani, Shraddha Manchekar, Tahreem Malim, Ayman Mir · 2017
With the advent of e-commerce, there are hundreds of websites being deployed and each site offers millions of products. This means that a substantial amount of information is being provided by these websites which cause the problem of information overload and in turn results in reduced customer satisfaction and interest. Recommender systems are designed to overcome such problems. They are intelligent systems that implement knowledge-based discovery techniques to accurately predict products that fit the customer profile. This paper discusses various recommendation techniques based on the web data mining such as Classification, Collaborative Filtering, Association rule mining and Sequence rule mining, discusses the problems faced by the recommender systems and then introduces the combination of the above-discussed recommendation techniques to overcome the problems faced and improve the sales.