基于双视角纠偏的推荐模型  

Rectifying Dual Bias for Recommendation

作　　者：黄露 倪葎 金澈清 HUANG Lu;NI Lyu;JIN Cheqing(School of Data Science and Engineering,East China Normal University,Shanghai 200062,China)

机构地区：[1]华东师范大学数据科学与工程学院,上海200062

出　　处：《计算机科学》2023年第9期152-159,共8页Computer Science

基　　金：上海市青年科技英才扬帆计划项目(22YF1411300)。

摘　　要：近几年,推荐算法快速增长,但大多数研究都重点关注如何利用机器学习模型更好地拟合历史交互数据。然而,推荐系统中的历史交互数据往往是观察性的,而非实验性数据。观测数据存在多种偏差,其中最典型的是流行度偏差。大多数处理流行度偏差的方法采用去除流行度偏差的策略,但是去偏策略本质上难以提升推荐精准性,这是因为推荐算法所引起的偏差会扩大。因此,同时在训练和推断阶段充分利用流行度偏差的纠偏策略更为可行。文中结合因果图分别从用户和物品两个角度来纠偏,提出了一个双偏去混及调整模型(Double Bias Deconfounding and Adjusting,DBDA)。在训练阶段剥离产生不利影响的流行度偏差,并在推断阶段根据流行度的变化趋势,对用户偏好做出更为精准的预测。在3个大规模公开数据集上进行实验,结果表明,相比目前的最优方法,所提方法在各个评价指标上提升了2.48%~19.70%。In recent years,a large number of recommendation algorithms have emerged,most of which focus on how to construct a machine learning model to give a good fit to historical interaction data.However,historical interaction data always come from observations rather than experiments in recommendation.Various biases exist in observed data,where the popularity bias is a representative one.Most approaches to dealing with popularity bias use the strategy of removing the popularity bias.But it is actually difficult for these approaches to improve the recommendation accuracy due to bias amplification causedby recommendation algorithms.Thus,the strategy of leveraging the popularity bias bothin training and inferencestagesis more applicable.Combined with the causal graph,a double bias deconfounding and adjusting(DBDA)model is proposed to rectify bias from the perspectives of both user and item.In the training stage,the adverse effects of the popularity bias are removed,and in the inference stage,a more accurate prediction of user preferences is made with the aid of the trend of popularity.Experiments are conducted on three large-scale public datasets to verify that the proposed method produces 2.48%~19.70%higher diverse evaluation metrics than the state-of-art method.

关 键 词：推荐系统 协同过滤 因果推断 后门调整 流行度偏差 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]