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Evaluation and user acceptance issues of a Bayesian classifier based TV Recommendation System

By Benedikt Engelbert, Karsten Morisse and Kai-christoph Hamborg

Abstract

Nowadays there is a variety of TV channels and programs. This seems to be an advantage for the TV user, but in most cases the user is overwhelmed and not able to choose the most appropriate content though. Assistive systems are needed to support the user in selecting the most appropriate content regarding the user’s interests. The research group Next Generation PVR faced the task to develop a user supporting Personal Video Recorder (PVR) in the form of a Bayesian classifier based recommendation system. The work on the prototype of the system is almost done. This paper focuses on the evaluation of the given system. We are presenting two types of evaluation scenarios as well as an approach for measuring user acceptance of a TV recommendation system. Within the evaluation, the acceptance will be questioned. In addition, the results of both scenarios and of the user acceptance survey are presented and discussed

Topics: Algorithms, Experimentation, Human Factors Keywords Recommendation System, Television, Evaluation, Bayesian classifier, User Acceptance
Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.416.4164
Provided by: CiteSeerX
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