E-commerce recommenders: powerful tools for E-business

Abstract

Introduction Gathering product information from large electronic catalogs on E-commerce (EC) sites can be a time-consuming and information-overloading process. User personalization, site content customization based upon a user's preferences and interests, is one mechanism of increasing the browsing efficiency of EC sites. Ideally, by increasing product navigation efficiency, EC sites will increase sales. This article briefly describes the main working objectives and perspectives regarding development of an EC site recommendation system. The article begins with a brief overview of systems. Next, we describe the importance of understanding consumers and their behavior and present a proposal for an agent-based architecture. We conclude with some thoughts about the field. This article is not intended to provide an in-depth explanation of the field, but instead demonstrates how a successful combination of marketing, Knowledge Discovery in Databases (KDD), user modeling, and Human Computer Interaction (HCI) lead to an effective technology in the decision support systems of EC. Recommendation systems suggest products and provide information to consumers to help them decide which items to purchase. Often, it is not possible for humans to make optimal purchasing decisions because there are too many factors involved. Technology can aid decision development by, for example, appropriately chunking information and thus structuring the user's valuation of products and allowing better human analogical reasoning. The recommender in the EC environment acts as a specialized seller for the customer. The recommenders mainly rely on user interfaces, techniques of marketing and large amounts of information about others customers and products to offer the right item to the right customer. The recommenders are the fundamental elements in sustaining usability and site confidence. EC recommenders are gradually becoming powerful tools for EC business. We classify the large number of recommenders [12,13] by the kind of information they use and by the way the recommendation system handles that information: 1. Collaborative-Social-filtering systems generate recommendations by aggregating consumer preferences. These systems group users based on similarity in behavioral or social patterns. The statistical analysis of data extraction or data mining and knowledge discovery in databases (KDD) techniques (monitoring the behavior of a user over the system, ratings over the products, purchase historical, etc.) builds the recommendation by analogies with many other users. Similarities between users are computed using the user-to-user correlation. This technique finds a set of "nearest neighbors" for each user in order to identify similar liking. Some collaborative filtering systems include Ringo [14] or …