Velusamy, Sudha and Gopal, Lakshmi and Sridhar, V and Bhatnagar, Shalabh (2007) Fuzzy Clustering Based Ad Recommendation for TV Programs. In: Proceedings of the Fifth European Conference, EuroITV (Published in Interactive TV: A Shared Experience, Eds. P.Cesar, K.Chorianopoulos and J.F.Jensen, LNCS 4471, Springer, 2007), Amsterdam, Netherlands, Amsterdam.
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Advertisements(Ads) are the main revenue earner for Television (TV) broadcasters. As TV reaches a large audience, it acts as the best media for advertisements of products and services. With the emergence of digital TV, it is important for the broadcasters to provide an intelligent service according to the various dimensions like program features, ad features, viewers’ interest and sponsors’ preference. We present an automatic ad recommendation algorithm that selects a set of ads by considering these dimensions and semantically match them with programs. Features of the ad video are captured interms of annotations and they are grouped into number of predefined semantic categories by using a categorization technique. Fuzzy categorical data clustering technique is applied on categorized data for selecting better suited ads for a particular program. Since the same ad can be recommended for more than one program depending upon multiple parameters, fuzzy clustering acts as the best suited method for ad recommendation. The relative fuzzy score called “degree of membership” calculated for each ad indicates the membership of a particular ad to different program clusters. Subjective evaluation of the algorithm is done by 10 different people and rated with a high success score.
|Item Type:||Conference Paper|
|Additional Information:||Copyright of this article belongs to Springer.|
|Keywords:||Ad Recommendation;Personalization;Fuzzy Clustering;Text Categorization.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||17 Oct 2011 06:59|
|Last Modified:||17 Oct 2011 06:59|
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