Surfing the Digital Wave

Generating Personalised TV Listings
using Collaborative, Case-Based Recommendation


Barry Smyth & Paul Cotter

Department of Computer Science
University College Dublin
Belfield, Dublin 4, Ireland
{Barry.Smyth, Paul.Cotter}@ucd.ie



Abstract. In the future digital TV will offer an unprecedented level of
programme choice. We are told that this will lead to dramatic increases in
viewer satisfaction as all viewing tastes are catered for all of the time. However,
the reality may be somewhat different. We have not yet developed the tools to
deal with this increased level of choice (for example, conventional TV guides
will be virtually useless), and viewers will face a significant and frustrating
information overload problem. This paper describes a solution in the form of
the PTV system. PTV employs user profiling and information filtering
techniques to generate web-based TV viewing guides that are personalised for
the viewing preferences of individual users. The paper explains how PTV
constructs graded user profiles to drive a hybrid recommendation technique,
combining case-based and collaborative information filtering methods. The
results of an extensive empirical study to evaluate the quality of PTVs case-based 
and collaborative filtering strategies are also described.
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