基于自注意力机制和迁移学习的跨领域推荐算法  

Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning

作　　者：方义秋[1] 张震坤 葛君伟[2] FANG Yi-qiu;ZHANG Zhen-kun;GE Jun-wei(School of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;School of Software Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学计算机科学与技术学院,重庆400065 [2]重庆邮电大学软件工程学院,重庆400065

出　　处：《计算机科学》2022年第8期70-77,共8页Computer Science

基　　金：国家自然科学基金(62072066)。

摘　　要：传统的单领域推荐算法受限于用户和项目的稀疏关系,存在用户/项目冷启动的问题,并且,其仅以用户对项目评分进行建模,忽略了评论文本中所蕴含的信息。基于评论文本的跨领域推荐算法在辅助领域提取用户/项目的评论信息来缓解目标领域的数据稀疏问题,以提高推荐的准确率。文中提出了结合自注意力机制和迁移学习的跨领域推荐算法SAMTL(Self-Attention Mechanism and Transfer Learning)。与现有算法不同,SAMTL充分融合了目标领域和辅助领域的知识。首先,引入自注意力机制建模用户的喜好信息;其次,通过交叉映射跨域传输网络实现借助一个领域的信息来提高另一个领域的推荐准确率;最后,在知识融合模块和评分预测模块整合两个域的信息,进行评分预测。在Amazon数据集上的实验表明,与现有的跨领域推荐模型相比,SAMTL的MAE和MSE值更高,在3种不同的跨领域数据集上的MAE值分别提高了8.4%,13.2%和19.4%,MSE值分别提高了6.3%,7.8%和5.6%。通过多项实验验证了自注意力机制和迁移学习的有效性,以及它们在缓解数据稀疏和用户冷启动问题方面的优势。Traditional single-domain recommendation algorithm is limited by the sparse relationship between users and items,and there is a problem of user/item cold start,and only models the item ratings by users,ignoring the information contained in the review text.The cross-domain recommendation algorithm based on review text extracts user/item review information in the auxiliary domain to alleviate data sparseness in the target domain and improve the accuracy of recommendation.This paper proposes a cross-domain recommendation algorithm SAMTL that combines self-attention mechanism and transfer learning.Different from existing algorithms,SAMTL fully integrates the knowledge of the target domain and auxiliary domains.Firstly,the self-attention mechanism is introduced to model user’s preference information.Then,by the cross-mapping cross-domain transmission network,the recommendation accuracy of another domain is improved with the help of information in one domain.Finally,the information of the two domains is integrated in the knowledge fusion and scoring prediction module to perform scoring prediction.Experiments on Amazon data set show that,compared with the existing cross-domain recommendation model,SAMTL has higher MAE and MSE values,and MAE increases by 8.4%,13.2%and 19.4%on three different cross-domain data sets,MSE increases by 6.3%,7.8%and 5.6%respectively.A number of experiments verify the effectiveness of self-attention mechanism and transfer learning,as well as the advantages in alleviating data sparsity and user cold start problems.

关 键 词：推荐系统 跨领域 迁移学习 自注意力 评论文本 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]