Recommender systems are now an integral part of many e-commerce websites, providing people relevant products they should consider purchasing. To date, many types of recommender systems have been proposed, with major categories belonging to item-based, user-based (collaborative) or knowledge-based algorithms. In this paper, we present a hybrid system that combines a knowledge based (KB) recommendation approach with a learning component that constantly assesses and updates the system's recommendations based on a collaborative and item based components. This combination facilitated creating a commercial system that was originally deployed as a KB system with only limited user data, but grew into a progressively more accurate system by using accumulated user data to augment the KB weights through item based and collaborative elements. This paper details the algorithms used to create the hybrid recommender, and details its initial pilot in recommending alternative products in an online shopping environment.
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