基于反向延长增强的对抗生成网络推荐算法  

Generative adversarial network recommendation based on reverse extension enhancement

作　　者：张文龙 孙福振[1] 吴相帅 李鹏程 王绍卿[1] Zhang Wenlong;Sun Fuzhen;Wu Xiangshuai;Li Pengcheng;Wang Shaoqing(School of Computer Science&Technology,Shandong University of Technology,Zibo Shandong 255049,China)

机构地区：[1]山东理工大学计算机科学与技术学院,山东淄博255049

出　　处：《计算机应用研究》2024年第7期2033-2038,共6页Application Research of Computers

基　　金：国家自然科学基金项目(61841602);山东省自然科学基金项目(ZR2020MF147)。

摘　　要：针对现有序列推荐模型因数据稀疏性严重难以达到最优性能的问题,提出了一种基于反向延长增强的生成对抗网络推荐算法。该方法通过对交互序列进行延长增强来获取高质量的训练数据,以缓解数据稀疏性带来的模型训练不充分的问题。首先,使用伪先验项将项目序列进行反向延长,深化项目序列特征;其次,延长增强的对象由短序列更改为所有用户序列,充分挖掘长序列中富含的上下文信息,缓解了增广序列中伪先验项占比过大而带来的噪声问题;最后,使用共享项目嵌入的生成对抗网络,通过判别器与生成器联合训练以提高模型推荐性能。在三个公开数据集上的实验结果表明,所提模型的命中率(HR@N)和归一化折损累计增益(NDCG@N)相较于最优基线ELECRec平均提升30%,验证了反向延长增强对挖掘序列特征和缓解数据稀疏性的有效性。Addressing the challenge of suboptimal performance in existing sequential recommendation models due to severe data sparsity,this paper proposed a generative adversarial network recommendation algorithm based on reverse extension enhancement.The approach extended and enhanced interaction sequences to obtain high-quality training data,mitigating the issue of insufficient model training caused by data sparsity.Firstly,it extended the project sequences backwardly using pseudo-prior terms to deepen the features of the project sequences.Secondly,it shifted the target of extension enhancement from short sequences to all user sequences,thoroughly exploring contextual information embedded in long sequences and alleviating noise issues arising from an excessively large proportion of pseudo-prior terms in augmented sequences.Finally,it employed a generative adversa-rial network with shared project embeddings,and jointly trained the discriminator and generator to enhance the model’s recommendation performance.Experimental results on three public datasets demonstrate an average improvement of 30%in hit rate(HR@N)and normalized discounted cumulative gain(NDCG@N)compared to the optimal baseline ELECRec,confirming the effectiveness of reverse extension enhancement in mining sequence features and alleviating data sparsity.

关 键 词：推荐系统 反向延长增强 生成对抗网络 序列推荐 自注意力网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]