Knowledge-Based Recommender Systems (original) (raw)

1. Introduction Recommender systems provide advice to users about items they might wish to purchase or examine. Recommendations made by such systems can help users navigate through large information spaces of product descriptions, news articles or other items. As on-line information and e-commerce burgeon, recommender systems are an increasingly important tool. A recent survey of recommender systems is found in (Maes, Guttman & Moukas, 1999). See also (Goldberg et al. 1992), (Resnick, et al. 1994), and (Resnick & Varian, 1997) and accompanying articles. The most well known type of recommender system is the collaborative-or socialfiltering type. These systems aggregate data about customers' purchasing habits or preferences, and make recommendations to other users based on similarity in overall purchasing patterns. For example, in the Ringo music recommender system (Shardanand & Maes, 1995), users express their musical preferences by rating various artists and albums, and get suggestions of groups and recordings that others with similar preferences also liked. Content-based recommender systems are classifier systems derived from machine learning research. For example, the NewsDude news filtering system is a recommender system that suggests news stories the user might like to read (Billsus & Pazzani, 1999). These systems use supervised machine learning to induce a classifier that can discriminate between items likely to be of interest to the user and those likely to be uninteresting. A third type of recommender system is one that uses knowledge about users and products to pursue a knowledge-based approach to generating a recommendation, reasoning about what products meet the user's requirements. The PersonalLogic recommender system offers a dialog that effectively walks the user down a discrimination tree of product features. 1 Others have adapted quantitative decision support tools for this task (Bhargava, Sridhar & Herrick, 1999). The class of systems that we will concentrate on in this paper draws from research in case-based reasoning or CBR (Hammond, 1989; Kolodner, 1993; Riesbeck & Schank, 1989). The restaurant recommender Entree (Burke, Hammond & Cooper, 1996; Burke, Hammond & Young, 1997) makes its recommendations by finding restaurants in a new city similar to restaurants the user knows and likes. 2 The system allows users to navigate by stating their preferences with respect to a given restaurant, thereby refining their search criteria. Each of these approaches has its strengths and weaknesses. As a collaborative filtering system collects more ratings from more users, the probability increases that