Internet Recommendation Systems
比较了内容过滤和协同过滤的推荐方法，提出一个贝叶斯偏好模型，能整合五种信息（个人偏好、他人偏好、专家评价、物品特征和用户特征）来改进推荐，并用电影数据验证了模型在协同过滤可行或不可行时的效果。
Several online firms, including Yahoo!, Amazon.com , and Movie Critic, recommend documents and products to consumers. Typically, the recommendations are based on content and/or collaborative filtering methods. The authors examine the merits of these methods, suggest that preference models used in marketing offer good alternatives, and describe a Bayesian preference model that allows statistical integration of five types of information useful for making recommendations: a person's expressed preferences, preferences of other consumers, expert evaluations, item characteristics, and individual characteristics. The proposed method accounts for not only preference heterogeneity across users but also unobserved product heterogeneity by introducing the interaction of unobserved product attributes with customer characteristics. The authors describe estimation by means of Markov chain Monte Carlo methods and use the model with a large data set to recommend movies either when collaborative filtering methods are viable alternatives or when no recommendations can be made by these methods.