An efficient ad recommendation system for TV programs
With broadcast Television (TV) going digital, the number of channels and the programs aired have increased tremendously. Millions of audiences of various categories such as adults, children, youth and families watch these programs. Advertisements (ads) aired during these programs are targeted to reach these varied audiences and are the main revenue earners for TV broadcasters. While TV broadcasters have the task of scheduling hundreds of ads during the various ad breaks of programs, it is important that the ads shown during any ad break have a good impact on the viewers. An intelligent ad recommendation system that takes into account various factors such as ad/program content, viewers' interests, sponsors' preferences, program timing, program popularity and the available ad slot that help increasing the ad revenue would be useful for sponsors and broadcasters. We present in this paper a single end-to-end ad recommender system that considers all of these factors and recommends a set of well scheduled and sequenced ads that are the best suited for a given TV ad break. The proposed recommendation system captures the features of the ad video in terms of annotations derived from MPEG-7 descriptions and these annotation keywords are systematically grouped into a number of pre-defined semantic categories by using a categorization technique. A fuzzy categorical data clustering technique is then applied on the categorized data for grouping the best suited ads for a set of pre-defined program classes such as News, Sports, Cartoons etc. The program classes considered are selected to match with the TV program genres proposed in the TV-anytime standard. Since the same ad can be recommended to more than one program depending upon multiple parameters, fuzzy clustering acts as a well suited (and perhaps also the best suited) technique for ad recommendation. The relative fuzzy score called "degree of membership" calculated for each ad is an indicator of the number of program clusters to which the given ad belongs to. The clustered ads are then scheduled using an algorithm that takes into consideration parameters such as program popularity, program timing and available ad slots, to provide the best possible package for sponsors to show their ads. The scheduled set of ads if played randomly during an ad break might make viewers (sponsors) unhappy, for instance, when similar (competing) product ads get played consecutively. Hence, the system employs sequencing algorithm that takes into account the pre- and post-ad sequences in order to better order the scheduled set of ads in any ad break. We show that our proposed recommendation system provides an effective way of recommending the right ads for broadcast TV programs. We also demonstrate that this strategy does indeed help sponsors to attract viewers' attention while playing their ads during ad breaks of TV programs. The proposed ad recommendation system is compared and evaluated subjectively with the current ad display system, by ten different people, and is rated with a high success score.
recommender systems fuzzy clustering broadcast tv ads personalization text categorization ad scheduling ad sequencing