A SVM-based personal recommendation system for TV programs

This paper presents a SVM-based prediction approach for constructing personal recommendation system for TV programs. We have applied support vector machine (SVM) to personal prediction of online Internet electronic program guide (IEPG). Our basic idea is to combine SVM and feedback processing into our system, using user-watched histories as retraining data, to realize personal predictions. We evaluate the precision by experiments with open data. The results show that the proposed polynomial kernel SVM system offers a statistically significant increase in performance compared to other method, and this system demonstrates good dynamically adaptive capability.