00000cab a22000003a 4500 UP-99796217608500937 Buklod 20231007233935.0 m |o d | ta 080901s xx d | ||r ||||| || (iLib)UPD-00048495485 DENGII eng Hofmann, Thomas Latent semantic models for collaborative filtering. vol. 22, no. 1 pp. 89 - 115 Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, that is, a database of available user preferences. In this article, we describe a new family of model-based algorithms designed for this task. These algorithms rely on a statistical modelling technique that introduces latent class variables in a mixture model setting to discover user communities and prototypical interest profiles. We investigate several variations to deal with discrete and continuous response variables as well as with different objective functions. The main advantages of this technique over standard memory-based methods are higher accuracy, constant time prediction, and an explicit and compact model representation. The latter can also be used to mine for user communitites. The experimental evaluation shows that substantial improvements in accucracy over existing methods and published results can be obtained. Information systems. Information storage and retrieval. Information search and retrieval. Information filtering. Computing methodologies. Pattern recognition. Clustering. Algorithms. ACM transactions on information systems. 22, 1 (2004). FO UPD DENG-II Article