融合注意力与深度因子分解机的时间上下文推荐模型  

Temporal Context Recommendation Model Integrating Attention and DeepFM

作　　者：刘亦欣 王家伟[1] 李自力 LIU Yi-xin;WANG Jia-wei;LI Zi-li(School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

机构地区：[1]重庆交通大学信息科学与工程学院,重庆400074

出　　处：《计算机与现代化》2021年第11期22-27,共6页Computer and Modernization

摘　　要：对于许多在线电商,预测用户购买商品的可能性至关重要。由于用户与商品的交互通常是高维且稀疏的,所以深度因子分解机算法(DeepFM)将因子分解机算法(FM)与深度神经网络(DNN)结合在一起,用FM处理低阶特征组合,用DNN处理高阶特征组合,通过并行的方式组合这2种方法,很好地解决了高维稀疏的问题。但是,它忽略了用户购买商品的先后性问题,也就是时间上下文信息。针对这一缺陷,本文提出一种融合注意力(Attention)与DeepFM的时间上下文推荐模型(DeepAFM),更好地利用用户与商品交互的时间上下文信息,相比较于未加入时间上下文信息的DeepFM模型,AUC提升了1.84%。对比验证结果表明,DeepAFM模型具有更优越的性能。For many online E-commerce companies,it is very important to predict the possibility of consumers purchasing goods.Because the interaction between users and commodities is usually high-dimensional and sparse,the deep factor decomposer algorithm(DeepFM)combines the factor decomposer algorithm(FM)with the deep neural network(DNN),uses FM to deal with low-order feature combination,uses DNN to deal with high-order feature combination,and combines the two methods in parallel,which solves the problem of high-dimensional sparse.However,it ignores the order of purchase,that is,time context information.Aiming at this defect,this paper proposes a time context recommendation system(DeepAFM)which integrates attention and DeepFM,which makes better use of the time context information of user and commodity interaction.Compared with DeepFM model without time context information,the AUC is increased by 1.84%.The results of the comparison and verification show that DeepAFM model has better performance.

关 键 词：推荐系统 时间上下文 注意力机制 深度因子分解机 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]