Personal Content Recommender Based on a Hierarchical User Model for the Selection of TV Programmes

摘要

In this paper we present our approach to user modeling for a personalized selection of multimedia content tested on a corpus of TV programmes. The idea of this approach is to classify content (TV programmes) based on the calculation of similarities between the description of content and the user model for each description attribute. Calculated similarities are then combined into a classification decision using the Support Vector Machines. The basis for the calculation of similarities is a hierarchical structure of the user model, overlaid upon a taxonomy of TV programme genres. Preliminary results show that it works well with a varying quality of content descriptions including incomplete genre classification and arbitrary number of description attributes. The evaluation of the system performance was based on content described using the TV-Anytime standard, but the approach can be adapted for search of other types of content with multi-attribute descriptions.