[1]鹿祥志,孙福振,王绍卿,等.基于用户记忆矩阵的长序列推荐算法[J].智能系统学报,2023,18(3):517-524.[doi:10.11992/tis.202110003]
LU Xiangzhi,SUN Fuzhen,WANG Shaoqing,et al.Long sequence recommendation algorithm based on user memory matrix[J].CAAI Transactions on Intelligent Systems,2023,18(3):517-524.[doi:10.11992/tis.202110003]
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LU Xiangzhi,SUN Fuzhen,WANG Shaoqing,et al.Long sequence recommendation algorithm based on user memory matrix[J].CAAI Transactions on Intelligent Systems,2023,18(3):517-524.[doi:10.11992/tis.202110003]
基于用户记忆矩阵的长序列推荐算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第3期
页码:
517-524
栏目:
学术论文—智能系统
出版日期:
2023-07-05
- Title:
- Long sequence recommendation algorithm based on user memory matrix
- Keywords:
- memory network; hierarchy; long-term interest; short-term interest; long short-term memory network; gated recurrent unit; long sequence recommendation; session recommendations
- 分类号:
- TP309.2
- 摘要:
- 传统的循环神经网络，如长短期记忆网络和门控循环单元，记忆能力有限而且记忆数据的存取不够灵活，对较长序列的特征捕捉有着先天的不足。记忆网络具有存储长时记忆的特点，而且对于记忆数据的存取更加灵活多变，因此本文在基于会话的推荐算法中引入了记忆网络。本文设计了一个层次化的推荐模型，模型分为2层。第1层为会话级的GRU模型，此模型用来刻画当前会话的序列特征，从而预测下一个项目。第2层为用户级的记忆网络模型，这个模型用来刻画用户长期兴趣的变化。本文提出的模型能有效地捕捉到用户的短期和长期兴趣，进而提升推荐的性能。公开数据集上的实验证明，在会话个数为10相对于会话个数为5的性能提升对比中，本文所提带有用户记忆矩阵的分层网络算法在召回率和平均倒数排名的提升度上相对于分层门控循环单元都有4%的增加。
- Abstract:
- Traditional recurrent neural networks, such as long-term and short-term memory networks (LSTM) and gated recurrent unit (GRU), have limited memory capacity and inflexible access to memory data, which have inherent shortcomings in capturing features of longer sequences. The memory network has the characteristics of storing long-term memory, and the access to memory data is more flexible and changeable. Therefore, this paper introduces the memory network in the session-based recommendation algorithm. In this paper, we design a hierarchical recommendation model, which is divided into two layers. The first layer is the session-level GRU model, which is used to characterize the sequence of current session and predict the next item. The second layer is the user-level memory network model, which is used to describe the changes in users’ long-term interests. The model proposed in this paper can effectively capture the short-term and long-term interests of users and thus improve the performance of recommendations. The experiments on public data sets demonstrate that the proposed hierarchical network with user memory (HNUM) algorithm has 4% increase in both recall rate and mean inverse ranking improvement relative to hierarchical gated recurrent unit (HGRU) for a performance improvement comparison of 10 sessions versus 5 sessions.
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[1]王作为,徐征,张汝波,等.记忆神经网络在机器人导航领域的应用与研究进展[J].智能系统学报,2020,15(5):835.[doi:10.11992/tis.202002020]
WANG Zuowei,XU Zheng,ZHANG Rubo,et al.Research progress and application of memory neural network in robot navigation[J].CAAI Transactions on Intelligent Systems,2020,15():835.[doi:10.11992/tis.202002020]
备注/Memo
收稿日期:2021-10-05。
基金项目:国家自然科学基金项目(61841602)；山东省自然科学基金项目(ZR2020MF147).
作者简介:鹿祥志,硕士研究生,主要研究方向为推荐系统;孙福振,副教授,博士,主要研究方向为数据挖掘、智能信息处理。发表学术论文30余篇;王绍卿,副教授,博士,主要研究方向为推荐系统、数据挖掘
通讯作者:孙福振.E-mail:sunfuzhen@sdut.edu.cn
更新日期/Last Update:
1900-01-01