The Recommendation of Digital TV Program Based on Rankboost Algorithm

A Rankboost algorithm using for recommendation of digital TV program is proposed. The program feature model expressed by time, channel, main category and subcategory are founded. The rank function is calculated based on Rankboost algorithm and real user TV watching records, its AUC (Area under the curve) of ROC (Receiver operator characteristic) is the highest comparing with those got by simple statistic and Bayesian approach, the experimental recommendation results which are calculated using the above rank function conform user real actions well.