HYBRID RECOMMENDER SYSTEMS SURVEY AND EXPERIMENTS Table I. Recommendation technique Ratings from u of Identifv users in U similar to u, and extrapolate from their ratings of i. Content- Features of items in I u's ratings of items in I Generate a classifier based hat fits us rating behavior and use it on i Demographic Demographic Demographic Identify users that information about nformation about u graphically U and their ratings similar to u. and extrapolate from their ratings of i Utility-based Features of items in L. A utility function over Apply the function to items in I that describes the items and determine s rank Features of items in L. A description of Infer a match between of preference. Some of the most important systems using this technique are (Resnick et al., 1994), Ringo/ Firefly (Shardanand Maes, 1995), Tapestry( Goldberg et al., 1992)and Recommender(hill et al, 1995) These systems can be either memory-based, comparing users against each other other mea model-based. in which a model is derived from the historical rating data and used to make predictions(breese et al., 1998). Model-based recommenders have used a variety of learning techniques including neural networks (Jennings Higuchi, 1993), latent semantic indexing ( Foltz, 1990), and Bayesian networks( Condliff et al., 1999) The greatest strength of collaborative techniques is that they are completely inde pendent of any machine-readable representation of the objects being recommended, and work well for complex objects such as music and movies where variations in taste are responsible for much of the variation in preferences. Schafer, Konstan and Riedl (1999)call this ' people-to-people correlation Demographic recommender systems aim to categorize the user based on pe attributes and make recommendations based on demographic classes. An early example of this kind of system was Grundy(rich, 1979) that recommended books based on personal information gathered through an interactive dialogue. The users responses were matched against a library of manually assembled user stereotypes Some more recent recommender systems have also taken this approach. Krulwich (1997), for example, uses demographic groups from marketing research to suggest a range of products and services. a short survey is used to gather the data for user categorization. In other systems, machine learning is used to arrive at a classifier based on demographic data(Pazzani, 1999). The representation of demographic Reproduced with permission of the copyright owner. Further reproduction prohibited without permissionReproduced with permission of the copyright owner. Further reproduction prohibited without permission