基于反事实学习及混淆因子建模的文章个性化推荐

Counterfactual Learning in Article Recommendation with Confounder

如今,互联网推荐系统已经成为了一个热门话题,自动化推荐极大程度上方便了人们的生活,帮助人们从海量的信息当中寻找到最感兴趣的关键信息.互联网上每时每刻都在产生新的文章信息,已有的信息是一个非常庞大的数据集合,这些被记录的大量数据能够帮助统计出用户偏好以及文章内容的受欢迎程度.目前互联网上有许多种类的推荐系统,他们综合考虑了用户特征,文章特征.基于互联网各大社交媒体上的数据,现有的用户个性化推荐系统通过构建特定的模型对用户进行精准推荐.目前,推荐算法主要通过监督学习与在线学习的方法进行构建,但这些方法进行个性化推荐的时候往往忽略了一个问题:历史记录当中的推荐策略往往是部分观测数据,具有分布不平衡的劣势,通过现有的历史记录不能保证算法能够得到无偏的推荐结果,也不能适应线上的环境以及推荐策略变化.本文提出了一种基于反事实学习并考虑系统当中混淆因子的文章个性化推荐.这种方法有更强的理论保证,并且在实验结果当中也显示了比现有方法更加好的算法表现.

Nowadays,the Internet recommendation system has become a hot topic.Automatic recommendation has greatly facilitated people’s life and helped people find the most interesting key information from the massive information.Now news information is generated every moment on the Internet,and the existing information is a very large data set,which can help to count the user preferences and popularity of news content.At present,there are many kinds of recommendation systems on the Internet.They comprehensively consider the characteristics of users and articles to be recommended.Based on the data on various social media on the Internet,they build models and can use these models for accurate personalized user recommendation.The existing recommendation system is usually a supervised learning system which takes a lot of user characteristics into account.These methods often ignore the following issue:the recommendation strategy in the history is often imbalance.Through the existing historical records,we cannot guarantee an unbiased result.So in this study,we propose a kind of personalized recommendation based on counterfactual learning.This method has stronger theoretical guarantee and also shows better algorithm performance than existing methods in the experimental results.