A top-N recommendation model Based on feedback learning to rank and online learning

Currently, the research on recommend system has drawn intensive attention from many researchers. However, it is still a concerned focus to make accurate recommendations in a short time period. Recently, some researchers have proposed a formal model based on learning to rank for Top-N recommendation. Aiming at improving the effect of Top-N recommendation and solving the problem of timeliness, we analyze and summarize out some factors that affect the timeliness of recommendation, and propose a ranking model of recent feedback which combined with online learning. Experimental results show that after the second ranking of users' recent feedback and basic recommendation output, the recommendation accuracy is improved effectively. Furthermore, we also explores the application of online learning algorithm based on the ranking model of recent feedback. The simulation experiments show that the model can still perform an accurate recommendation after being updated online in real time.