基于Setwise排序的深度输入感知因子分解机  

Deep input-aware factorization machine based on Setwise ranking

作　　者：刘通 周宁宁[1] LIU Tong;ZHOU Ning-ning(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]南京邮电大学计算机学院,江苏南京210023

出　　处：《计算机工程与科学》2023年第10期1891-1900,共10页Computer Engineering & Science

摘　　要：SetRank是一种新颖的Setwise贝叶斯协同排序模型,能够以更贴合实际场景的方式对隐式反馈的数据进行建模。然而,SetRank中只考虑了协同信息,缺乏对内容信息的有效利用。为解决上述问题,首先提出了一种基于Setwise排序的因子分解机模型SRFMs,借鉴SetRank中的Setwise排序解决隐式反馈中的负样本缺失问题,选择因子分解机作为预测器来建模内容信息,从优化项目排序的角度出发建模用户偏好;接着,为改进标准FM中固定特征表示以及缺少高阶特征交互的缺点,受IFM启发,提出将输入感知与深度神经网络相结合,融入SRFMs从而构建出SR-DIFM模型;最后,在2个真实数据集上进行了实验,结果表明,所提出的模型在解决隐式反馈场景下推荐问题的同时,可以更好地利用用户和项目的内容信息,从而获得更高的推荐准确率,在HR、NDCG和mAP等客观指标上都优于其他模型。SetRank is a novel Setwise Bayesian collaborative ranking model that can model implicit feedback data more closely to real-world scenarios.However,SetRank only considers collaborative information and lacks effective use of content information.In order to solve the above problems,a factorization machine model based on Setwise ranking,named SRFMs,is proposed.Drawing on the Setwise ranking proposed in SetRank to solve the problem of missing negative samples in implicit feedback,a factorization machine is chosen as a predictor to model content information,and model user preferences from the perspective of optimal item ranking.Furthermore,to improve the fixed feature representation and the lack of higher-order feature interactions in standard FM,inspired by IFM,SR-DIFM model is constructed to incorporate the SRFMs by combining input-aware networks with neural networks.Experimental results on two real-world datasets demonstrate that the proposed model outperforms the state-of-art model in terms of evaluation metrics including HR,NDCG and mAP,and can improve the accuracy of recommendations which make better use of user and item content information while solving the recommendation problem under implicit feedback.

关 键 词：隐式反馈 内容信息 排序学习 因子分解机 神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]