Hybrid Recommender Systems for Electronic Commerce
Thomas T. Tran, Robin A Cohen · 2000
In electronic commerce applications, prospective buyers may be interested in receiving recommendations to assist with their purchasing decisions. Previous research has described two main models for automated recommender systems - collaborative filtering and the knowledge-based approach. In this paper, we present an architecture for designing a hybrid recommender system that combines these two approaches. We then discuss how such a recommender system can switch between the two methods, depending on the current support for providing good recommendations from the behaviour of other users, required for the collaborative filtering option. We also comment on how the overall design is useful to support recommendations for a variety of product areas and present some directions for future work. Overview One of the tasks in the application area of knowledgebased electronic markets is that of providing recommendations to potential shoppers. In an environment where there is a wide choice for the prospective buyers, an automated system which serves to present a more narrow selection for the buyer would be desirable. Recommender systems are systems which provide recommendations to potential buyers. Two widely used techniques for building recommender systems to date are collaborative filtering and knowledge-based approaches. Collaborative filtering is a real-time personalization technique that leverages similarities between people to make recommendations (Greening 1998). other words, a collaborative filtering recommender system assumes that human preferences are correlated; thus, it predicts preferences and makes recommendations to one user based on the preferences of a group of users. In contrast, a knowledge-based recommender system exploits its knowledge base of the product domain to generate recommendations to a user, by reasoning about what products meet the user’s requirements. In this paper, we analyze the advantages and shortcomings of both techniques and present an architecture for a hybrid recommender system that integrates the two approaches. Such a system will inherit all the strengths from a collaborative filtering recommender system, but will be able to avoid its weaknesses. Although there have been some proposals for designing systems which make use of both the knowledge-base approach and collaborative filtering (Burke 1999), collaborative filtering is used more in a post-processing stage, so that the knowledge-based design predominates. In this paper, we outline some specifications for changing between the collaborative filtering and the knowledge-based styles of recommendation, within a single system. This design strategy will be useful for electronic commerce applications where the number of buyers and the make-up of the community of buyers dictates whether collaborative filtering will be effective or not. Background In this section, we introduce the collaborative filtering and knowledge-based approaches in building recommender systems. We discuss the strengths and weaknesses of each approach as the motivation for the design of a hybrid architecture that combines the two approaches.