Prediction Strategies in a TV Recommender System - Method and Experiments

Predicting the interests of a user in information is an important process in personalized information systems. In this paper, we present a way to create prediction engines that allow prediction techniques to be easily combined into prediction strategies. Prediction strategies choose one or a combination of prediction techniques at the moment a prediction is required, taking into account the most up -to-date knowledge about the current user, other users, the information and the system itself. Results of two experiments show that prediction strategies improve both the accuracy and stability of prediction engines. One of these experiments involves a TV recommender system. This paper describes the method of prediction strategies, how they have been applied in the TV recommender system and results of the experiment in detail. KEYWORDS Personalization, Adaptive Systems, Recommender Systems, User Modeling

• Type: Conference
• Year: 2003
• Where: IADIS
• Authors: Mark van Setten, Mettina Veenstra, Anton Nijholt, Betsy van Dijk
• Keywords: IADIS 2003, IADIS 2008, Prediction, Prediction Strategies, Prediction Techniques