Online Video Advertising Based on Fine-Grained Video Tags

With the development of the Internet, it has been a trend of manual tagging, labeling and sharing videos. Rational use of these swarm intelligence will help to improve the effectiveness of video advertising. The method presented in this paper first collects the fine-grained user video tags, and generates the video hotspots by the video timeline-weighted method. Then, based on the idea of the classification matching, the description of the video hotspots can be used to select the advertising. At last, the time points that the popular attention has dropped by the biggest level are found to put advertising. Experiments show that, among the mega-scale video set, the content correlation between the hotspot and the advertisements selected by this method can reach 85%. The probability that the users close ads windows is less than 10%. Compared with the ads system that has been widely adopted so far, the average broadcast time of the new method can be increased by 21.5%, the click-through rate is improved from 0.65% to 0.73%.