Please wait a minute...
Advanced Search
数据分析与知识发现  2020, Vol. 4 Issue (12): 120-135     https://doi.org/10.11925/infotech.2096-3467.2020.0264
     研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于多属性评分隐表征学习的群组推荐算法*
张纯金1,郭盛辉2,纪淑娟2(),杨伟2,伊磊2
1山东科技大学网络安全与信息化办公室 青岛 266590
2山东省智慧矿山信息技术重点实验室(山东科技大学) 青岛 266590
Group Recommendation Algorithms Based on Implicit Representation Learning of Multi-attribute Ratings
Zhang Chunjin1,Guo Shenghui2,Ji Shujuan2(),Yang Wei2,Yi Lei2
1Network Security and Information Office, Shandong University of Science and Technology, Qingdao 266590, China
2Shandong Provincial Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao 266590, China
全文: PDF (2008 KB)   HTML ( 5
输出: BibTeX | EndNote (RIS)      
摘要 

【目的】 克服个体用户表征学习受个体用户评分稀疏性影响严重的问题,提高推荐算法的准确率和反应速度。【方法】 提出一种基于神经网络的多属性评分隐表征学习方法,并应用该方法从用户群组和项目两个维度学习多属性评分的隐表征,最后分别通过用户群组偏好匹配和项目吸引力计算实现两个群组推荐。【结果】 基于TripAdvisor数据集的实验结果表明:本文算法的准确率、时间性能优于典型的多属性推荐算法和群组推荐算法;准确率略差于个体推荐算法,但在线和离线运行时间较个性化推荐算法分别至少降低30%和50%;用户群组的隐表征学习相比项目的隐表征学习对推荐性能的提高作用更明显。【局限】 由于真实群组数据难以获取,仅基于某种聚类算法生成虚拟群组,因此群组较理想化。虚拟群组的偏好比真实群组的偏好可能更易聚合。【结论】 基于神经网络学习群组用户的隐表征(即聚合群组用户的偏好)和项目的隐表征,可以有效提高群组推荐算法和多属性推荐算法的准确率和召回率,效果非常接近最新的个性化推荐算法。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
张纯金
郭盛辉
纪淑娟
杨伟
伊磊
关键词 群组推荐算法多属性评分隐表征学习评分矩阵神经网络    
Abstract

[Objective] This paper addresses the issues facing user representation learning due to the sparsity of their ratings, aiming to improve the performance of recommendation algorithm. [Methods] We proposed a neural network-based method to learn the implicit representation of multi-attribute ratings from user groups and individual items. Then, we conducted two group-oriented recommendations by matching their learned representations with preferences as well as calculating the attraction of each item. [Results] We examined our method with TripAdvisor data set and found the accuracy and time performance of the proposed algorithms were better than the typical multi-attribute ones and group ones. Compared to the personalized recommendation algorithm, the accuracies of our algorithms were slightly worse, but their online and offline running time was reduced by more than 30% and 50%, repectively. The recommendation results from user group based algorithm outperformed the item based one. [Limitations] We generated virtual groups based on clustering algorithm and their preferences were aggregated more effecitvely than the real world ones. [Conclusions] The proposed algorithms effectively improve the recommendation results.

Key wordsGroup Reccommendation Algorithms    Multi-attribute Ratings    Implicit Representation Learning    Rating Matrix    Nerual Network
收稿日期: 2020-03-20      出版日期: 2020-12-25
ZTFLH:  TP393  
基金资助:*青岛社会科学规划研究项目“大数据背景下跨境电商中产品信息挖掘与推荐研究”(QDSKL1801138);国家自然科学基金项目“面向大数据流的信用攻击群体及关键人物发现方法研究”(71772107);国家自然科学基金项目“复杂属性网络的多视角表示学习关键技术研究”(62072288)
通讯作者: 纪淑娟     E-mail: jsjsuzie@sina.com
引用本文:   
张纯金,郭盛辉,纪淑娟,杨伟,伊磊. 基于多属性评分隐表征学习的群组推荐算法*[J]. 数据分析与知识发现, 2020, 4(12): 120-135.
Zhang Chunjin,Guo Shenghui,Ji Shujuan,Yang Wei,Yi Lei. Group Recommendation Algorithms Based on Implicit Representation Learning of Multi-attribute Ratings. Data Analysis and Knowledge Discovery, 2020, 4(12): 120-135.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0264      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I12/120
Fig.1  本文研究与实验框架
Fig.2  NNIRL模型
Fig.3  GMURec算法框架
Fig.4  GMIRec算法框架
属性 原始数据 预处理后数据
用户 536 952 20 443
项目 3 945 1 755
数据 796 958 262 300
稀疏度 99.96% 99.3%
Table 1  初步数据预处理
Fig.5  不同数目群组下的轮廓系数
参数 出现位置
梯度下降算法中的学习率α 0.15 公式(6)和公式(7)
梯度下降算法中误差阈值和迭代次数 0.01和100 000 梯度下降算法
调整因子σ 0.80 公式(9)和公式(11)
Table 2  参数设置
Fig.6  GMURec算法中神经网络结构的marcoF1
Fig.7  GMIRec算法中神经网络结构的marcoF1
算法名称 简称 面向个体用户 面向群体用户 单属性 多属性
基于神经网络的群组推荐算法 ATTGRec
基于最小痛苦策略的群组推荐算法 LP
PromoRec算法 PromoRec
基于Pearson相似度的协同过滤算法 PCCCF
基于一种混合相似度的协同过滤算法 HyCF
本文算法 GMURec、GMIRec
Table 3  对比算法
Fig.8  对比算法的准确率
Fig.9  对比算法的召回率
Fig.10  对比算法的运行时间
[1] 朱成纯, 张谧 . 基于活动的社交网络中的群组推荐算法设计[J]. 计算机系统应用, 2017,26(9):103-108.
[1] ( Zhu Chengchun, Zhang Mi . Predicting User Preferences for Groups in Event-Based Social Networks[J]. Computer Systems & Applications, 2017,26(9):103-108.)
[2] 李珊, 姚叶慧, 厉浩 , 等. 基于ISA联合聚类的组推荐算法研究[J]. 数据分析与知识发现, 2019,3(8):77-87.
[2] ( Li Shan, Yao Yehui, Li Hao , et al. ISA Biclustering Algorithm for Group Recommendation[J]. Data Analysis and Knowledge Discovery, 2019,3(8):77-87.)
[3] Sarwar B M, Karypis G, Konstan J , et al. Recommender Systems for Large-Scale E-Commerce: Scalable Neighborhood Formation Using Clustering[J]. Communications, 2002,50(12):158-167.
[4] Lu J, Shambour Q, Xu Y S , et al. A Web-Based Personalized Business Partner Recommendation System Using Fuzzy Semantic Techniques[J]. Computational Intelligence, 2013,29(1):37-69.
[5] Ortega F, Hernando A, Bobadilla J , et al. Recommending Items to Group of Users Using Matrix Factorization Based Collaborative Filtering[J]. Information Sciences, 2016,345(C):313-324.
[6] 黄国言, 李有超, 高建培 , 等. 基于项目属性的用户聚类协同过滤推荐算法[J]. 计算机工程与设计, 2010,31(5):1038-1041.
[6] ( Huang Guoyan, Li Youchao, Gao Jianpei , et al. Collaborative Filtering Recommendation Algorithm Based on User Clustering of Item Attributes[J]. Computer Engineering and Design, 2010,31(5):1038-1041.)
[7] 陈克寒, 韩盼盼, 吴健 . 基于用户聚类的异构社交网络推荐算法[J]. 计算机学报, 2013,36(2):349-359.
[7] ( Chen Kehan, Han Panpan, Wu Jian . User Clustering Based Social Network Recommendation[J]. Chinese Journal of Computers, 2013,36(2):349-359.)
[8] 王晓军 . 推荐系统中分布式混合协同过滤方法[J]. 北京邮电大学学报, 2016,39(2):25-29.
[8] ( Wang Xiaojun . A Distributed Hybrid Collaborative Filtering Method in Recommender Systems[J]. Journal of Beijing University of Posts and Telecommunications, 2016,39(2):25-29.)
[9] 黄贤英, 李沁东, 熊李媛 . 结合拓扑势用户聚类的协同过滤推荐算法[J]. 计算机工程与设计, 2018,39(1):90-95.
[9] ( Huang Xianying, Li Qindong, Xiong Liyuan . Collaborative Filtering Recommendation Algorithm with Topology Potential Combined User Clustering[J]. Computer Engineering and Design, 2018,39(1):90-95.)
[10] 王兴茂, 张兴明, 吴毅涛 , 等. 基于启发式聚类模型和类别相似度的协同过滤推荐算法[J]. 电子学报, 2016,44(7):1708-1713.
[10] ( Wang Xingmao, Zhang Xingming, Wu Yitao , et al. A Collaborative Recommendation Algorithm Based on Heuristic Clustering Model and Category Similarity[J]. Acta Electronica Sinica, 2016,44(7):1708-1713.)
[11] 张峻玮, 杨洲 . 一种基于改进的层次聚类的协同过滤用户推荐算法研究[J]. 计算机科学, 2014,41(12):176-178.
[11] ( Zhang Junwei, Yang Zhou . Collaborative Filtering Recommendation Algorithm Based on Improved User Clustering[J]. Computer Science, 2014,41(12):176-178.)
[12] 李华, 张宇, 孙俊华 . 基于用户模糊聚类的协同过滤推荐研究[J]. 计算机科学, 2012,39(12):83-86.
[12] ( Li Hua, Zhang Yu, Sun Junhua . Research on Collaborative Filtering Recommendation Based on User Fuzzy Clustering[J]. Computer Science, 2012,39(12):83-86.)
[13] 李贵, 陈召新, 李征宇 , 等. 基于谱聚类群组发现的协同过滤推荐算法[J]. 计算机科学, 2014,41(11A):354-358.
[13] ( Li Gui, Chen Zhaoxin, Li Zhengyu , et al. Collaborative Filtering Recommendation Algorithm Based on Spectral Clustering Subgroups Discovering[J]. Computer Science, 2014,41(11A):354-358.)
[14] Zheng N, Bao H. Flickr Group Recommendation Based on User-Generated Tags and Social Relations via Topic Model [C]//Proceedings of the 10th International Symposium on Neural Networks. Springer, 2013: 514-523.
[15] 陈婷, 朱青, 周梦溪 , 等. 社交网络环境下基于信任的推荐算法[J]. 软件学报, 2017,28(3):721-731.
[15] ( Chen Ting, Zhu Qing, Zhou Mengxi , et al. Trust-Based Recommendation Algorithm in Social Network[J]. Journal of Software, 2017,28(3):721-731.)
[16] Quijano-Sanchez L, Recio-Garcia J A, Diaz-Agudo B. Personality and Social Trust in Group Recommendations [C]//Proceedings of the 22nd IEEE International Conference on Tools with Artificial Intelligence. IEEE Computer Society, 2010: 121-126.
[17] Lai C H, Liu D R, Lin C S . Novel Personal and Group-Based Trust Models in Collaborative Filtering for Document Recommendation[J]. Information Sciences, 2013,239:31-49.
[18] Kagita V R, Pujari A K, Padmanabhan V . Virtual User Approach for Group Recommender Systems Using Precedence Relations[J]. Information Sciences, 2015,294:15-30.
[19] Ma Y K, Ji S J, Liang Y Q, et al. A Hybrid Recommendation List Aggregation Algorithm for Group Recommendation [C]//Proceedings of the 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). ACM, 2015: 405-408
[20] Chao D L, Balthrop J, Forrest S. Adaptive Radio: Achieving Consensus Using Negative Preferences [C]//Proceedings of 2005 International ACM SIGGROUP Conference on Supporting Group Work. ACM, 2005: 120-123.
[21] Masthoff J . Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers[J]. User Modeling and User-Adapted Interaction, 2004,14(1):37-85.
[22] Van Deventer O, De Wit J, Vanattenhoven J, et al. Group Recommendation in an Hybrid Broadcast Broadband Television Context [C]// Proceedings of the 21st Conference on User Modeling, Adaptation, and Personalization. 2013.
[23] McCarthy K, Salamó M, Coyle L, et al. CATS: A Synchronous Approach to Collaborative Group Recommendation [C]//Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference. 2006: 86-91.
[24] Ntoutsi I, Stefanidis K, Norvag K, et al. gRecs: A Group Recommendation System Based on User Clustering [C]//Proceedings of the 17th International Conference on Database Systems for Advanced Applications. Springer-Verlag, 2012: 299-303.
[25] Quijano-Sanchez L Recio-Garcia J A Diaz-Agudo B . An Architecture and Functional Description to Integrate Social Behaviour Knowledge into Group Recommender Systems[J]. Applied Intelligence, 2014,40(4):732-748.
[26] McCarthy J F. Pocket RestraurantFinder a Situated Recommender System for Groups [C]//Proceedings of the Workshop on Mobile Ad-Hoc Communication at the 2002 ACM Conference on Human Factors in Computer Systems. 2002: 1-10.
[27] 刘荣荣 . 考虑时间情境的群体推荐算法研究[J]. 武汉理工大学学报(信息与管理工程版), 2016,38(1):93-96.
[27] ( Liu Rongrong . Group Recommendation Algorithm Research Considering Time Context[J]. Journal of Wuhan University of Technology (Information & Management Engineering), 2016,38(1):93-96.)
[28] 郭均鹏, 赵梦楠 . 面向在线社区用户的群体推荐算法研究[J]. 计算机应用研究, 2014,31(3):696-699.
[28] ( Guo Junpeng, Zhao Mengnan . Group Recommendation Algorithm for Online Community Users[J]. Application Research of Computers, 2014,31(3):696-699.)
[29] Roy S B, Lakshmanan L V S, Liu R , et al. From Group Recommendations to Group Formation [C]//Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. 2015: 1603-1616.
[30] 赵梦楠 . 基于SNA的群体推荐系统的研究[J]. 信息技术, 2000(2):199-202.
[30] ( Zhao Mengnan . Research on SNA-based Group Recommendation System[J]. Information Technology, 2000 ( 2):199-202.)
[31] 梁昌勇, 冷亚军, 王勇胜 , 等. 电子商务推荐系统中群体用户推荐问题研究[J]. 中国管理科学, 2013,21(3):153-158.
[31] ( Liang Changyong, Leng Yajun, Wang Yongsheng , et al. Research on Group User Recommendation in E-Commerce Recommendation System[J]. Chinese of Management Science, 2013,21(3):153-158.)
[32] Shang S, Hui P, Kulkarni S R, et al. Wisdom of the Crowd: Incorporating Social Influence in Recommendation Models [C]//Proceedings of the 17th International Conference on Parallel & Distributed Systems. IEEE, 2012: 835-840.
[33] 李汶华, 熊晓栋, 郭均鹏 . 一种基于案例推理和协商的群体推荐算法[J]. 系统工程, 2013,31(11):93-98.
[33] ( Li Wenhua, Xiong Xiaodong, Guo Junpeng . A Group Recommendation Algorithm Based on CBR and Negotiation[J]. Systems Engineering, 2013,31(11):93-98.)
[34] Sacharidis D. Top-N Group Recommendations with Fairness [C]//Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 2019: 1663-1670.
[35] Stratigi M, Nummenmaa J, Pitoura E, et al. Fair Sequential Group Recommendations [C]//Proceedings of the 35th Annual ACM Symposium on Applied Computing. 2020: 1443-1452.
[36] Cao D, He X N, Miao L H, et al. Attentive Group Recommendation [C]//Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. 2018: 645-654.
[37] Zhu Q, Zhou M X, Liang J F, et al. Efficient Promotion Algorithm by Exploring Group Preference in Recommendation [C]//Proceedings of the 2016 IEEE International Conference on Web Services. IEEE, 2016: 268-275.
[38] Bilge A, Kaleli C. A Multi-Criteria Item-Based Collaborative Filtering Framework [C]//Proceedings of the 11th International Joint Conference on Computer Science and Software Engineering (JCSSE). 2014: 18-22.
[39] Adomavicius G, Kwon Y O . New Recommendation Techniques for Multicriteria Rating Systems[J]. IEEE Intelligent Systems, 2007,22(3):48-55.
[40] Nilashi M, Esfahani M D, Roudbaraki M Z , et al. A Multi-Criteria Collaborative Filtering Recommender System Using Clustering and Regression Techniques[J]. Journal of Soft Computing and Decision Support Systems, 2016,3(5):24-30.
[41] Majumder G S, Dwivedi P, Kant V. Matrix Factorization and Regression-Based Approach for Multi-Criteria Recommender System [C]//Proceedings of the International Conference on Information & Communication Technology for Intelligent Systems. 2017: 103-110.
[42] 陈冬林, 聂规划 . 基于商品属性隐性评分的协同过滤算法研究[J]. 计算机应用研究, 2006,26(4):966-968.
[42] ( Chen Donglin, Nie Guihua . Research on Collaborative Filtering Algorithm Based on Item’s Attribute Implicit Rating[J]. Journal of Computer Applications, 2006,26(4):966-968.)
[43] Zhang J, Peng Q K, Sun S Q , et al. Collaborative Filtering Recommendation Algorithm Based on User Preference Derived from Item Domain Features[J]. Physica A: Statistical Mechanics and Its Applications, 2014,396:66-76.
[44] Jannach D, Karakaya Z, Gedikli F. Accuracy Improvements for Multi-criteria Recommender Systems [C]//Proceedings of the 13th ACM Conference on Electronic Commerce. ACM, 2012: 674-689.
[45] Jannach D, Zanker M, Fuchs M . Leveraging Multi-criteria Customer Feedback for Satisfaction Analysis and Improved Recommendations[J]. Information Technology and Tourism, 2014,14(2):119-149.
[46] Zheng Y. Criteria Chains: A Novel Multi-Criteria Recommendation Approach [C]//Proceedings of the 22nd International Conference on Intelligent User Interfaces. ACM, 2017: 29-33.
[47] 覃正, 李岱峰 . 一种基于资源多属性分类的群组推荐模型[J]. 统计与决策, 2010 ( 8):153-155.
[47] ( Qin Zheng, Li Daifeng . A Group Recommendation Model Based on Multi-attribute Classification of Resources[J]. Statistics and Decision, 2010 ( 8):153-155.)
[48] Garcia I, Sebastia L, Onaindia E, et al. A Group Recommender System for Tourist Activities [C]//Proceedings of the 10th International Conference on E-Commerce and Web Technologies. 2009: 26-37.
[49] Geng L. Neuron Adaptive and Neural Network Based on Gradient Descent Searching Algorithm for Diagonalization of Relative Gain Sensitivity Matrix Decouple Control for MIMO System [C]//Proceedings of 2018 IEEE International Conference on Networking, Sensing and Control. IEEE, 2008: 368-373.
[50] Harrington P. 机器学习实战[M]. 李锐, 李鹏, 曲亚东, 等译. 北京: 人民邮电出版社, 2013: 15-31.
[50] ( Harrington P. Machine Learning in Action [M]. Translated by Li Rui, Li Peng, Qu Yadong, et al. Beijing: Posts and Telecom Press, 2013: 15-31.)
[51] 范永全, 杜亚军 . 基于加权相似度的用户协同过滤方法[J]. 计算机工程与应用, 2016,52(22):222-225.
[51] ( Fan Yongquan, Du Yajun . Improved User-based Collaborative Filtering Method Based on Weighted Similarity[J]. Computer Engineering and Applications, 2016,52(22):222-225.)
[52] Sai L N, Shreya M S, Subudhi A A , et al. Optimal K-Means Clustering Method Using Silhouette Coefficient[J]. International Journal of Applied Research on Information Technology and Computing, 2017,8(3):335-344.
[53] 袁正午, 陈然 . 基于多层次混合相似度的协同过滤推荐算法[J]. 计算机应用, 2018,38(3):633-638.
[53] ( Yuan Zhengwu, Chen Ran . Collaborative Filtering Recommendation Algorithm Based on Multi-Level Hybrid Similarity[J]. Journal of Computer Applications, 2018,38(3):633-638.)
[54] Chao C X, Qu S N, Du T . Research of Collaborative Filtering Recommendation Algorithm for Short Text[J]. Journal of Computer & Communications, 2014,2(14):59-66.
[55] 丁晟春, 王小英, 刘梦露 . 基于本体和加权朴素贝叶斯的网络舆情主题分类[J]. 现代情报, 2018,38(8):12-17, 34.
[55] ( Ding Shengchun, Wang Xiaoying, Liu J . Topic Classification of Network Public Opinion Based on Ontology and Weighted Naive Bayes [J]. Modern Information, 2018,38(8):12-17, 34.)
[56] Wang Y, Deng J Z, Gao J , et al. A Hybrid User Similarity Model for Collaborative Filtering[J]. Information Sciences, 2017,418(C):102-118.
[57] Tran L V, Pham T A N, Tay Y, et al. Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation [C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2019: 255-264.
[58] Delic A, Ricci F, Neidhardt J, et al. Preference Networks and Non-Linear Preferences in Group Recommendations [C]//Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence. 2019: 403-407.
[1] 尹浩然,曹金璇,曹鲁喆,王国栋. 扩充语义维度的BiGRU-AM突发事件要素识别研究*[J]. 数据分析与知识发现, 2020, 4(9): 91-99.
[2] 邱尔丽,何鸿魏,易成岐,李慧颖. 基于字符级CNN技术的公共政策网民支持度研究 *[J]. 数据分析与知识发现, 2020, 4(7): 28-37.
[3] 王思迪,胡广伟,杨巳煜,施云. 基于文本分类的政府网站信箱自动转递方法研究*[J]. 数据分析与知识发现, 2020, 4(6): 51-59.
[4] 刘伟江,魏海,运天鹤. 基于卷积神经网络的客户信用评估模型研究*[J]. 数据分析与知识发现, 2020, 4(6): 80-90.
[5] 王末,崔运鹏,陈丽,李欢. 基于深度学习的学术论文语步结构分类方法研究*[J]. 数据分析与知识发现, 2020, 4(6): 60-68.
[6] 闫春,刘璐. 基于改进SOM神经网络模型与RFM模型的非寿险客户细分研究*[J]. 数据分析与知识发现, 2020, 4(4): 83-90.
[7] 苏传东,黄孝喜,王荣波,谌志群,毛君钰,朱嘉莹,潘宇豪. 基于词嵌入融合和循环神经网络的中英文隐喻识别*[J]. 数据分析与知识发现, 2020, 4(4): 91-99.
[8] 徐月梅,刘韫文,蔡连侨. 基于深度融合特征的政务微博转发规模预测模型*[J]. 数据分析与知识发现, 2020, 4(2/3): 18-28.
[9] 向菲,谢耀谈. 基于混合采样与迁移学习的患者评论识别模型*[J]. 数据分析与知识发现, 2020, 4(2/3): 39-47.
[10] 倪维健,郭浩宇,刘彤,曾庆田. 基于多头自注意力神经网络的购物篮推荐方法*[J]. 数据分析与知识发现, 2020, 4(2/3): 68-77.
[11] 冯文刚,姜兆菲璠. 基于民航旅客分级分类方法的差异化安检和旅客风险演化研究*[J]. 数据分析与知识发现, 2020, 4(12): 105-119.
[12] 彭郴,吕学强,孙宁,张乐,姜肇财,宋黎. 基于CNN的消费品缺陷领域词典构建方法研究*[J]. 数据分析与知识发现, 2020, 4(11): 112-120.
[13] 陶玥,余丽,张润杰. 科技文献中短语级主题抽取的主动学习方法研究*[J]. 数据分析与知识发现, 2020, 4(10): 134-143.
[14] 聂维民,陈永洲,马静. 融合多粒度信息的文本向量表示模型 *[J]. 数据分析与知识发现, 2019, 3(9): 45-52.
[15] 邵云飞,刘东苏. 基于类别特征扩展的短文本分类方法研究 *[J]. 数据分析与知识发现, 2019, 3(9): 60-67.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn