计算机科学 ›› 2019, Vol. 46 ›› Issue (8): 111-115.doi: 10.11896/j.issn.1002-137X.2019.08.018
郭旭1, 朱敬华1,2
GUO Xu1, ZHU Jing-hua1,2
摘要: 随着互联网应用的蓬勃发展,推荐系统作为解决信息过载的有效手段,成为了工业界与学术界的研究热点。面向用户隐式反馈的传统推荐算法主要基于协同过滤和排序学习等方法,但这些方法未充分利用用户行为中的隐式反馈特征。文中提出了一种基于神经网络的用户向量化表示模型,其能够充分利用用户的异构的隐式反馈行为特征。同时,借鉴机器翻译中的self-attention机制,设计了一种神经注意力推荐模型,其融合用户向量化表示和用户-项目交互的动态时序特征以提高推荐系统的性能。在公开数据集上进行对比实验,通过召回率、准确率、NDCG 3个指标评价推荐性能。结果表明,与其他面向隐式反馈的推荐模型相比,所提推荐模型具有更好的推荐性能,并且对用户行为特征具有很好的泛化能力。
中图分类号:
[1]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[M]∥Advances in Neural Information Processing Systems.Bertin:Springer,2017:5998-6008. [2]SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]∥Procee-dings of the 10th International Conference on World Wide Web.ACM,2001:285-295. [3]OSTUNI V C,DI NOIA T,DI SCIASCIO E,et al.Top-n recommendations from implicit feedback leveraging linked open data[C]∥Proceedings of the 7th ACM Conference on Recommender Systems.ACM,2013:85-92. [4]ZIMDARS A,CHICKERING D M,MEEK C.Using temporal data for making recommendations[C]∥Proceedings of the Se-venteenth Conference on Uncertainty in artificial intelligence.Morgan Kaufmann Publishers Inc.,2001:580-588. [5]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback[C]∥Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence.AUAI Press,2009:452-461. [6]SEDHAIN S,MENON A K,SANNER S,et al.Autorec:Autoencoders meet collaborative filtering[C]∥Proceedings of the 24th International Conference on World Wide Web.ACM,2015:111-112. [7]ELKAHKY A M,SONG Y,HE X.A multi-view deep learning approach for cross domain user modeling in recommendation systems[C]∥Proceedings of the 24th International Conference on World Wide Web.International World Wide Web Confe-rences Steering Committee,2015:278-288. [8]HE X,LIAO L,ZHANG H,et al.Neural collaborative filtering[C]∥Proceedings of the 26th International Conference on World Wide Web.International World Wide Web Conferences Steering Committee,2017:173-182. [9]HIDASI B,KARATZOGLOU A,BALTRUNAS L,et al.Session-based recommendations with recurrent neural networks[J].arXiv:1511.06939,2015. [10]GONG Y,ZHANG Q.Hashtag Recommendation Using Attention-Based Convolutional Neural Network[C]∥Proceedings of International Joint Conference on Artificial Intelligence.AAAI Press,2016:2782-2788. [11]LI Y,LIU T,JIANG J,et al.Hashtag recommendation with topical attention-based LSTM[C]∥Proceedings of the 26th International Conference on Computational Linguistics.ACL,2016:3019-3029. [12]XIAO J,YE H,HE X,et al.Attentional factorization machines:Learning the weight of feature interactions via attention networks[J].arXiv:1708.04617,2017. |
[1] | 程章桃, 钟婷, 张晟铭, 周帆. 基于图学习的推荐系统研究综述 Survey of Recommender Systems Based on Graph Learning 计算机科学, 2022, 49(9): 1-13. https://doi.org/10.11896/jsjkx.210900072 |
[2] | 王冠宇, 钟婷, 冯宇, 周帆. 基于矢量量化编码的协同过滤推荐方法 Collaborative Filtering Recommendation Method Based on Vector Quantization Coding 计算机科学, 2022, 49(9): 48-54. https://doi.org/10.11896/jsjkx.210700109 |
[3] | 周芳泉, 成卫青. 基于全局增强图神经网络的序列推荐 Sequence Recommendation Based on Global Enhanced Graph Neural Network 计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085 |
[4] | 戴禹, 许林峰. 基于文本行匹配的跨图文本阅读方法 Cross-image Text Reading Method Based on Text Line Matching 计算机科学, 2022, 49(9): 139-145. https://doi.org/10.11896/jsjkx.220600032 |
[5] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
[6] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
[7] | 熊丽琴, 曹雷, 赖俊, 陈希亮. 基于值分解的多智能体深度强化学习综述 Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization 计算机科学, 2022, 49(9): 172-182. https://doi.org/10.11896/jsjkx.210800112 |
[8] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
[9] | 宁晗阳, 马苗, 杨波, 刘士昌. 密码学智能化研究进展与分析 Research Progress and Analysis on Intelligent Cryptology 计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053 |
[10] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
[11] | 汪鸣, 彭舰, 黄飞虎. 基于多时间尺度时空图网络的交通流量预测模型 Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction 计算机科学, 2022, 49(8): 40-48. https://doi.org/10.11896/jsjkx.220100188 |
[12] | 李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023 |
[13] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
[14] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
[15] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
|