Integrating Knowledge-based and Collaborative-filtering Recommender Systems

Knowledge-based and collaborative-filtering recommender systems facilitate electronic commerce by helping users find appropriate products from large catalogs. This paper discusses the strengths and weaknesses of both techniques and introduces the possibility of a hybrid recommender system that combines the two approaches. An approach is suggested in which knowledge-based techniques are used to bootstrap the collaborative filtering engine while its data pool is small, and the collaborative filter is used as a postfilter for the knowledge-based recommender. Collaborative Filtering Collaborative filtering recommender system are a widelyaccepted technique in electronic commerce. (See [Resnick & Varian, 1997] and other articles in that special issue. A recent survey is found in [Maes, Guttman & Moukas, 1999]. See also [Goldberg et al. 1992] and [Resnick, et al. 1994].) These systems aggregate data about customers’ purchasing habits or preferences and make recommendations to other users based on similarity in overall patterns. For example, in the Ringo music recommender system (Shardanand & Maes, 1995), users who had expressed their musical preferences by rating various artists and albums could get suggestions of other groups and recordings that others with similar preferences also liked. As a collaborative filtering system collects more ratings from more users, the probability increases that someone in the system will be a good match for any given new user. This beneficial property also has its downside, however. A collaborative filtering system must be initialized with a large amount of data, because a system with a small base of ratings is unlikely to be very useful. Further, the accuracy of the system is very sensitive to the number of rated items that can be associated with a given user (Shardanand & Maes, 1995). These factors contribute to a “ramp-up” problem: until there is a large number of users whose habits are known, the system cannot be useful for most users, and until a sufficient number of rated items has been collected, the system cannot be useful for a particular user. Another problem with collaborative filtering systems might be called the “banana” problem. Bananas are a frequently-purchased item in most American grocery stores, and the odds are high that any given market basket will contain bananas. A naive recommender system working from market basket data will always recommend bananas, simply because they are highly correlated with everything. Because the system has no notion of what foods ought to go together, it cannot screen out such suggestions. These drawbacks are not significant for some large ecommerce sites, such as Amazon.com, with a very large customer base, and a large and diverse product line that lends itself to multi-item purchases. A more difficult challenge is presented for a product such as an automobile that is bought much less frequently and one at a time. For an automobile, a home loan or any other infrequentlypurchased item, the system will not be able to use marketbasket or purchase history to make recommendations. A recommender system would never be able to say “people who bought a Geo Metro also bought a Ford Escort,” because that is not how people buy cars. Knowledge-based recommender systems What a recommender system for a car or other similar product must do is get information about users’ preferences: Why are they buying a car? Is comfort or fuel economy more important? Based on such information, the system can pursue a knowledge-based approach to generating a recommendation, by reasoning about what products meet the user’s requirements. The PersonalLogic recommender system offers a dialog that effectively walks the 1 The need to maintain user-identified logs of preferences and purchases also raises privacy concerns for collaborative filtering