一种基于3因素概率图模型的长尾推荐方法

互联网时代,数据呈爆炸式增长,前所未有的数据量远远超过受众的接收和处理能力,因此,从海量复杂数据中有效获取关键性有用信息成为必须解决的问题.面对信息过载问题,人们迫切需要一种高效的信息过滤系统,“推荐系统”应运而生.在现实的推荐场景中,用户给予项目的评分或者选择项目的频次是一个典型的长尾现象.事实上,长尾现象的深入分析,不仅有助于挖掘用户的个性化偏好,更有助于电商场景中相关利益主体的业绩提升.因此,长尾推荐研究日益受到重视.针对长尾推荐的可解释性问题,提出了基于3因素概率图模型的长尾推荐方法.面对长尾推荐过程中推荐系统、用户对“具有可解释性的长尾项目推荐”的现实需求,着眼于概率图模型在因果关系方面的可解释性优势,立足于“新颖性+准确性”综合考量的方法设计目标,建立了基于用户活跃度、项目非流行度和用户-项目偏好水平的3因素概率图推荐方法.实验比较结果表明,具有可解释性优势的3因素概率图推荐方法在保证一定预测精度的前提下具有更好的新颖性推荐效果.

A Method on Long Tail Recommendation Based on Three-Factor Probabilistic Graphical Model

In the Internet era, the explosive rise of information and unprecedented data size has greatly exceeded the receivers’ receiving and handling capability. Accordingly it has become a necessity to capture important and useful information from the massive and complex data. The problem of information overload calls for a more efficient information filtering system and this explains the birth and rise of the recommendation system. In the real recommendation situations, the long tail phenomenon is typically represented, whether in the customers’ rating or their frequency of opting for the items. Actually a deep analysis on the long tail phenomenon helps to promote the e-commerce stakeholders’ performance as well as explore the customers’ preferences, which explains the increasing attention paid on the long tail recommendation. To handle the interpretability of long tail recommendation, a three-factor probabilistic graphical model is proposed. To meet the recommendation system and users’ need for interpretable item recommendation in reality, a three-factor probabilistic graphical recommendation method based on customers’ activity level, the items’unpopularity and customer-item preference level are proposed. In this method, the advantage of probabilistic graphical model in interpreting cause-result relationship is utilized and recommendation accuracy and design novelty in the algorithm can be ensured in this study. The results of experiments indicate that with the advantage of interpretability, the three-factor probabilistic graphical recommendation method can ensure prediction accuracy and perform better in providing novel recommendations.