Bringing order to the Web: automatically categorizing search results
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
ACM SIGKDD Explorations Newsletter
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Squeezer: an efficient algorithm for clustering categorical data
Journal of Computer Science and Technology
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Q2C@UST: our winning solution to query classification in KDDCUP 2005
ACM SIGKDD Explorations Newsletter
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
Receiver-side semantic reasoning for digital TV personalization in the absence of return channels
Multimedia Tools and Applications
| Hi-index | 0.00 |
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.