报告题目：Perceptive users for online social systems: the Netflix case

摘要：Identifying perceptive users and user perceptibility is of importance for understanding the user collective behavior for user-object bipartite networks. Investigating the Netflix data set (containing 37,755,925 ratings delivered by 218,319 users on 7,803 movies during 2,241 days), we track the ratings given to the 13 objects which are nominated for the Oscar awards before and after the award-nomination time. The distribution of the time difference between the rating and the award-nomination time show that there exists a few users concern the award-nominated movies before the award-nomination time. In this paper, we present a parameter-free method to identify the user perceptibility, which is defined as the capability that a user can identify high-quality objects before they actually be widely approved (award- nominated). Besides the empirical results that high perceptibility users have larger degree, stronger correlation of rating series and higher reputation, we investigate the behavior patterns of the perceptive users from the burstiness and memory of rating durations, as well as user preference. The experimental results indicate that high perceptibility users prefer to rate less popular objects and the rating durations of high perceptibility users show lower burstiness and higher memory effects. Furthermore, the results of predicting high perceptibility users by means of machine learning algorithms show that the burstiness and memory coefficients along with user preference can improve the prediction performance in identifying the high perceptibility users based on user behavior patterns. This work provides a further understanding on the collective behavior patterns and perceptive users.

报告人：刘晓露

刘晓露，山东财经大学。博士毕业于上海理工大学，主要研究方向为复杂网络与在线用户行为分析。博士后期间主持一项教育部人文社科青年基金与一项博士后面上项目。

时间：2019年9月10日 14:00

地点：中心校区知新楼B座1044报告厅

邀请人：王光辉 数学学院教授