What Combinations of Contents is Driving Popularity in IPTV-based Social Networks?
IPTV-based Social Networks are gaining popularity with TV programs coming over IP connection and internet like applications available on home TV. One such application is rating TV programs over some predefined genres. In this paper, we suggest an approach for building a recommender system to be used by content distributors, publishers, and motion pictures producers-directors to decide on what combinations of contents may drive popularity or unpopularity. This may be used then for creating a proper mixture of media contents which can drive high popularity. This may also be used for the purpose of catering customized contents for group of users whose taste is similar and thus combinations of contents driving popularity for a certain group is also similar. We use a novel approach for this formulation utilizing fuzzy decision trees. Computational experiments performed over real-world program review database shows that the proposed approach is very efficient towards understanding of the content combinations.
KeywordsFuzzy Rule Recommender System Certainty Factor Fuzzy Classification Fuzzy Decision Tree
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