Improving On-line Genre-based Viewer Profiling

Veloso, Bruno and Malheiro, B. and Burguillo, Juan Carlos and Foss, Jeremy (2017) Improving On-line Genre-based Viewer Profiling. In: TVX2017 Workshop on In-Programme Personalisation for Broadcast (IPP4B), June 2017, Hilversum, The Netherlands.

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Typically, recommendation algorithms are unable to make recommendations for new users due to the inherent lack of information, i.e., the cold start problem. To overcome this problem, this work addresses builds new viewer profiles by combining general and personal feature-based profiles using both the frequency and the rating of each feature. For each newly arrived viewer, we create a dynamic profile by combining the corresponding demographic stereotypical profile with the individual profile and, then, as the number of the viewergenerated events increases, we gradually fade the general component and strengthen the individual component. Specifically, we combine the genre frequency & rating of the viewer personal and demographic stereotype profiles. This novel viewer profiling algorithm was evaluated with the MovieLens 100k and 1M data sets, using content-based and collaborative stream mining recommendation techniques. When compared with the standard average user stereotype, the results with the demographic stereotypes show a significant improvement in terms of classification accuracy, identical prediction accuracy and an increase in run time.

Item Type: Conference or Workshop Item (Paper)
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Oana-Andreea Dumitrascu
Date Deposited: 10 Jul 2017 10:52
Last Modified: 22 Mar 2023 12:01

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