ROBIN BURKE as relevance feedback enable a search engine to refine its representation of the users query, and represent a simple form of recommendation. The next-generation search engine Google blurs this distinction, incorporatingauthoritativeness'criteria into its ranking(defined recursively as the sum of the authoritativeness of pages linking to a given page) in order to return more useful results (Brin Page, 1998). One common thread in recommender systems research is the need to combine recommendation techniques to achieve peak performance. All of the known rec ommendation techniques have strengths and weaknesses, and many researchers have chosen to combine techniques in different ways. This article surveys the different ecommendation techniques being researched- analyzing them in terms of the data that supports the recommendations and the algorithms that operate on that data and examines the range of hybridization techniques that have been proposed. This analysis points to a number of possible hy brids that have yet to be explored. Finally, we discuss how adding a hybrid with colla borative filtering improved the perform ance of our knowledge-based recommender system Entree. In addition, we show that semantic ratings made available by the knowledge-based portion of the system provide an additional boost to the hy brids performance 1.1. RECOMMENDATION TECHNIQUES Recommendation techniques have a number of possible classifications(Resnick Varian, 1997; Schafer, Konstan riedl, 1999; Terveen Hill, 2001). Of interest in this discussion is not the type of interface or the properties of the users interaction with the recommender, but rather the sources of data on which recommendation is based and the use to which that data is put. Specifically, recommender systems have (i background data, the information that the system has before the recommendation process begins, (ii) input data, the information that user must communicate to the system in order to generate a recommendation, and (iii) an algorithm that combines background and input data to arrive at its suggestions On this basis, we can dis- tinguish five different recommendation techniques as shown in Table I. Assume that I is the set of items over which recommendations might be made, U is the set of users whose preferences are known, u is the user for whom recommendations need to be generated, and i is some item for which we would like to predict u's preference Collaborative recommendation is probably the most familiar, most widely implemented and most mature of the technologies. Collaborative recommender sys- tems aggregate ratings or recommendations of objects, recognize commonalities between users on the basis of their ratings, and generate new recommendations based on inter-user comparisons. A typical user profile in a collaborative system consists of vector of items and their ratings, continuously augmented as the user interacts with the system over time. Some systems used time-based discounting of ratings to ccount for drift in user interests(Billsus Pazzani, 2000; Schwab et al., 2001) In some cases, ratings may be binary(like/dislike)or real-valued indicating degree UrlhtTp://www.google.com Reproduced with permission of the copyright owner. Further reproduction prohibited without permissionReproduced with permission of the copyright owner. Further reproduction prohibited without permission