计算机科学 ›› 2019, Vol. 46 ›› Issue (8): 28-34.doi: 10.11896/j.issn.1002-137X.2019.08.005
邓存彬1,2, 虞慧群1, 范贵生1
DENG Cun-bin1,2, YU Hui-qun1, FAN Gui-sheng1
摘要： 在信息爆炸的时代,推荐系统在减轻信息过载方面发挥了巨大的作用。目前,推荐系统普遍使用传统的协同过滤算法学习用户商品行为矩阵中的隐向量,但是其存在数据稀疏性和冷启动问题,同时未考虑用户的兴趣偏好以及商品的受欢迎程度会随时间发生改变,这极大地限制了推荐的准确性。已有学者利用深度学习模型学习辅助信息的特征来扩充协同过滤算法的特征,取得了一定的成果,但并未充分有效地解决全部问题。以电影推荐为研究对象,提出了融合动态协同过滤和深度学习的推荐算法。首先,利用动态协同过滤算法融入时间特征;然后,利用深度学习模型来学习用户和电影特征信息,以形成高维潜在空间的用户特征和电影特征的隐向量;最后,将其融入到动态协同过滤算法中。以MovieLens为实验数据集对电影的评分进行预测,实验结果表明所提算法提高了电影评分预测的准确性。
中图分类号:
| [1]SUN H,HAN Z.An improved collaborative filtering algorithm for popular items of fusion items[J].Miniature Microcomputer Systems,2018,39(4):638-643.(in Chinese) 孙红,韩震.融合物品热门因子的协同过滤改进算法[J].小型微型计算机系统,2018,39(4):638-643. [2]WENG X L,WANG Z J.Research progress of collaborative filtering recommendation algorithm[J].Computer Engineering and Applications,2018,54(1):25-31.(in Chinese) 翁小兰,王志坚.协同过滤推荐算法研究进展[J].计算机工程与应用,2018,54(1):25-31. [3]XU R,ZHANG W.A recommendation system scoring prediction framework based on Adaboost algorithm[J].Journal of ComputerSystems,2017,26(8):107-113.(in Chinese) 徐日,张谧.基于Adaboost算法的推荐系统评分预测框架[J].计算机系统应用,2017,26(8):107-113. [4]PORTEOUS I,ASUNCION A,WELLING M.Bayesian matrix factorization with side information and dirichlet process mixtures [C]∥Twenty-Fourth AAAI Conference on Artificial Intelligence.AAAI Press,2010:563-568. [5]HUANG L W,JIANG B T,LU S Y,et al.A Survey of Recommendation Systems Based on Deep Learning [J].Chinese Journal of Computers,2018,41(7):191-219.(in Chinese) 黄立威,江碧涛,吕守业,等.基于深度学习的推荐系统研究综述[J].计算机学报,2018,41(7):191-219. [6]ZHU Y,LI H,LIAO Y,et al.What to do next:modeling user behaviors by time-lstm [C]∥Twenty-Sixth International Joint Conference on Artificial Intelligence.2017:3602-3608. [7]ZHENG L,NOROOZI V,YU P S.Joint deep modeling of users and items using reviews for recommendation[C]∥Proceedings of the Tenth ACM International Conference on Web Search and Data Mining.ACM,2017:425-434. [8]CHENG H T,KOC L,HARMSEN J,et al.Wide & deep lear- ning for recommender systems[C]∥Proceedings of the 1st Workshop on Deep Learning for Recommender Systems.ACM,2016:7-10. [9]QU Y,CAI H,REN K,et al.Product-based neural networks for user response prediction[C]∥2016 IEEE 16th International Conference on Data Mining (ICDM).IEEE,2016:1149-1154. [10]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. [11]ZHAO W,WANG W,YE J,et al.Leveraging long and short- term information in content-aware movie recommendation[J].arXiv:1712.09059,2017. [12]KIM D,PARK C,OH J,et al.Convolutional matrix factorization for document context-aware recommendation[C]∥Proceedings of the 10th ACM Conference on Recommender Systems.ACM,2016:233-240. [13]WEI J,HE J,CHEN K,et al.Collaborative filtering and deep learning based recommendation system for cold start items[J].Expert Systems with Applications,2017,69:29-39. [14]WANG H,WANG N,YEUNG D Y.Collaborative deep learning for recommender systems[C]∥Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2015:1235-1244. [15]KOREN Y.Collaborative filtering with temporal dynamics[C]∥ Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining.ACM,2009:447-456. [16]HARPER F M,KONSTAN J A.The movieLens datasets[J].Acm Transactions on Interactive Intelligent Systems,2016,5(4):1-19. [17]KAWALE J,KAWALE J,FU Y.Deep collaborative filtering via marginalized denoising auto-encoder [C]∥ACM International on Conference on Information and Knowledge Management.ACM,2015:811-820. [18]KIM D,PARK C,OH J,et al.Deep hybrid recommender systems via exploiting document context and statistics of items [J].Information Sciences,2017,417(C):72-87. |
| [1] | 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 |
| [2] | 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 |
| [3] | 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 |
| [4] | 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 |
| [5] | 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 |
| [6] | 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 |
| [7] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
| [8] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
| [9] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
| [10] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
| [11] | 周慧, 施皓晨, 屠要峰, 黄圣君. 基于主动采样的深度鲁棒神经网络学习 Robust Deep Neural Network Learning Based on Active Sampling 计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044 |
| [12] | 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫. 小样本雷达辐射源识别的深度学习方法综述 Survey of Deep Learning for Radar Emitter Identification Based on Small Sample 计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138 |
| [13] | 祝文韬, 兰先超, 罗唤霖, 岳彬, 汪洋. 改进Faster R-CNN的光学遥感飞机目标检测 Remote Sensing Aircraft Target Detection Based on Improved Faster R-CNN 计算机科学, 2022, 49(6A): 378-383. https://doi.org/10.11896/jsjkx.210300121 |
| [14] | 王建明, 陈响育, 杨自忠, 史晨阳, 张宇航, 钱正坤. 不同数据增强方法对模型识别精度的影响 Influence of Different Data Augmentation Methods on Model Recognition Accuracy 计算机科学, 2022, 49(6A): 418-423. https://doi.org/10.11896/jsjkx.210700210 |
| [15] | 毛典辉, 黄晖煜, 赵爽. 符合监管合规性的自动合成新闻检测方法研究 Study on Automatic Synthetic News Detection Method Complying with Regulatory Compliance 计算机科学, 2022, 49(6A): 523-530. https://doi.org/10.11896/jsjkx.210300083 |
|
||
首页