How to Develop Online Recommendation Systems that Deliver Superior Business Performance

Several “e-movement” crusaders have discovered that customers spend significant amounts of time researching products they seek before purchasing. In a bid to assist customers in these efforts, and conserve precious time, these organizations offer users suggestions of products they may be interested in. This serves the dual purpose of not just attracting browsers but converting them into buyers. For instance, an online bookstore may know that a customer has interest in mobile technology based on previous site visits and suggest relevant titles to purchase. An uninitiated user may be impressed by such suggestions. Suggestions (or “recommendations” as they are popularly known) predict likes and dislikes of users. To offer meaningful recommendations to site visitors, these companies need to store huge amounts of data pertaining to different user profiles and their corresponding interests. This eventually culminates in information overload, or difficulty in understanding and making informed decisions. One solution to combating this issue is what is known as a recommendation system.