David – A Novel Approach to an Industrial Recommender System

Abstract

A conceptual approach to a recommender system that is industrial oriented and optimized for business-to-business. With particular needs, industrial datasets seek assertiveness and contextualization to capitalize on recommender systems. Muscle memory must be implanted on business users, enabling them to harvest the benefits of such technologies. These types of users, which differ from standard ones, are fuzzy and vague in their choices, avoiding explicit forms of feedback. This paper addresses state-of-the-art of such industrial systems, its users and explores a potential solution.