HYBRID RECOMMENDER SYSTEMS SURVEY AND EXPERIMENTS 335 it may be a more detailed representation of the user's needs(Towle Quinn, 2000) The Entree system(described below )and several other recent systems(for example, Schmitt Bergmann, 1999) employ techniques from case-based reasoning for knowledge-based recommendation. Schafer, Konstan and Riedl call knowledge- based recommendation the 'Editor's choice method The knowledge used by a knowledge-based recommender can also take many forms. Google uses information about the links between web pages to infer popularity and authoritative value ( Brin Page, 1998). Entree uses knowledge of cuisines to infer similarity between restaurants. Utility-based approaches calcu late a utility value for objects to be recommended, and in principle, such calculations could be based on functional knowledge. However, existing systems do not use such inference, requiring users to do their own mapping between their needs and the fea ures of products, either in the form of preference functions for each feature in the case of tete-a- tete or answers to a detailed questionnaire in the case of Persona logic 2. Comparing recommendation techniques All recommendation techniques have strengths and weaknesses discussed below and summarized in Table II. Perhaps the best known is the ramp-up'problem(Konstan et al., 1998). This term actually refers to two distinct but related problems New User: Because recommendations follow from a comparison between the target ser and other users based solely on the accumulation of ratings, a user with few ratings becomes difficult to categorize. Table IL. TradofIs between recommendation techniques A. Can identify cross-genre I New user ramp-up probler filtering J. New item ramp-up problem B. Domain knowledge not K " Gray sheep problem L. Quality d C. Adaptive: qua over time M. Stability vs, plasticity problem D. Implicit feedback sufficient Content-based(CN) B, C, D I, L, M Demographic(DM) A, B, C L, K, L M N. Must gather demograp information O. User must input ut P. Suggestion ability static G. Can include non-product features Knowledge-based E F G ap Q Knowledge engineering required user needs to products Reproduced with permission of the copyright owner. Further reproduction prohibited without permissionReproduced with permission of the copyright owner. Further reproduction prohibited without permission