Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 153-158.
• Data Science • Previous Articles Next Articles
ZHAO Hai-yan1, WANG Jing1, CHEN Qing-kui1, CAO Jian2
CLC Number:
[1]CELMAÒ.Music Recommendation and Discovery in the LongTail[J].Ceedings of International Congress on Electron Microscopy Methods Enzymol-89,2008,11(1):7-8. [2]KARIMI R,WISTUBA M,NANOPOULOS A,et al.Factorized Decision Trees for Active Learning in Recommender Systems[C]∥IEEE International Conference on TOOLS with Artificial Intelligence.IEEE Computer Society,2013:404-411. [3]ELAHI M,RICCI F,RUBENS N.Active Learning in Collabora-tive Filtering Recommender Systems[J].Computer Science Review,2016,20(C):29-50. [4]POZO M,CHIKY R,MEZIANE F,et al.Enhancing new user cold-start based on decision trees active learning by using past warm-users predictions[J].International Conference on Computational Collective Intelligence,2017,7(4):137-147. [5]BOUTILIER C,ZEMEL R S,MARLIN B.Active Collaborative Filtering[C]∥19th Conference on Uncertainty in Artifical Intelligence.2012:98-106. [6]HOFMANN T.Latent semantic models for collaborative filte-ring[J].ACM Transactions on Information Systems,2013,22(1):89-115. [7]SETTLES B.Active Learning Literature Survey:ComputerScience Technical Report 1648[R].University of Wisconsin,Madison,2010. [8]CASSEL S,HOWAR F,JONSSON B,et al.Active learning for extended finite state machines[J].Formal Aspects of Computing,2016,28(2):233-263. [9]SHARMA M,BILGIC M.Evidence-based uncertainty sampling for active learning[J].Data Mining & Knowledge Discovery,2016,31(1):1-39. [10]HUANG E C,PAO H K,LEE Y J.Big active learning[C]∥IEEE International Conference on Big Data.IEEE,2018:94-101. [11]NARR A,TRIEBEL R,CREMERS D.Stream-based ActiveLearning for efficient and adaptive classification of 3D objects[C]∥IEEE International Conference on Robotics and Automation.IEEE,2016:227-233. [12]LANG K J,BAUM E B.Query learning can work poorly when a human oracle is used[C]∥IEEE Intl. Joint Conference on Neural Networks.1992:306-322. [13]HOI S C H,JIN R,ZHU J,et al.Semi-supervised SVM batch mode active learning for image retrieval[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2008(CVPR 2008).IEEE,2008:1-7. [14]XU Z,YU K,TRESP V,et al.Representative Sampling for Text Classification Using Support Vector Machines[C]∥European Conference on Ir Research.Springer-Verlag,2003:393-407. [15]JONES S,SHAO L,DU K.Active learning for human action retrieval using query pool selection[J].Neurocomputing,2014,124(2):89-96. [16]SUGIYAMA M,NAKAJIMA S.Pool-based active learning inapproximate linear regression[J].Machine Learning,2009,75(3):249-274. [17]BOUGUELIA M R.An adaptive streaming active learning strategy based on instance weighting[J].Pattern Recognition Letters,2016,70:38-44. [18]BOUGUELIA M R,BELAÏD Y,BELAÏD A.A Stream-Based Semi-supervised Active Learning Approach for Document Classification[C]∥International Conference on Document Analysis and Recognition.IEEE,2013:611-615. [19]ROKACH L,NAAMANI L,SHMILOVICI A.Pessimistic cost-sensitive active learning of decision trees for profit maximizing targeting campaigns[J].Data Mining & Knowledge Discovery,2008,17(2):283-316. [20]KOHRS A,BERNARD M.Improving collaborative filtering for new users by smart object selection[C]∥Proceedings of International Conference on Media Features.2001. [21]HOFMANN T.Collaborative filtering via gaussian probabilistic latent semantic analysis[C]∥ACM SIGIR Conference.2003:259-266. [22]SCHOHN G,COHN D.Less is More:Active Learning withSupport Vector Machines[C]∥Seventeenth International Conference on Machine Learning.Morgan Kaufmann Publishers Inc.,2000:839-846. [23]SETTLES B,CRAVEN M,RAY S.Multiple-Instance ActiveLearning[C]∥Conference on Neural Information Processing Systems.DBLP,2008:1289-1296. [24]TONG S,KOLLER D.Support vector machine active learning with applications to text classification[J].Journal of Machine Learning Research,2001,2(1):999-1006. [25]JAN K P S,CHRISTIAN K I.Active learning with support vector machines[J].Wiley Interdisciplinary Reviews:Data Mining and Knowledge Discovery,2004,4(4):313-326. [26]ROY N,MCCALLUM A.Toward optimal active learningthrough monte carlo estimation of error reduction[C]∥ICML.2001:441-448. [27]GEMAN S,BIENENSTOCK E.Neural networks and the bias/variance dilemma[M].MIT Press,1992:1-58. [28]GUAN C,LIU Q,LV J,et al.Consolidation:Metric+ActiveLearning and Its Applications for Cross-Domain Recommendation[C]∥IEEE/Wic/ACM International Conference on Web Intelligence and Intelligent Agent Technology.IEEE,2016:244-251. [29]SUGIYAMA M.Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error[J].Journal of Machine Learning Research,2006,7(1):141-166. [30]TEIXEIRA I R,CARVALHO F D A T D,RAMALHO G L,et al.ActiveCP:A Method for Speeding up User Preferences Acquisition in Collaborative Filtering Systems[M]∥Advances in Artificial Intelligence.Springer Berlin Heidelberg,2002:237-247. [31]GOLBANDI N,KOREN Y,LEMPEL R.On bootstrapping re-commender systems[C]∥ACM Internatioanl Conference on Information & Knowledge Management.ACM,2010:1805-1808. [32]ZHAO Y,XU C,CAO Y.Research on query-by-committeemethod of active learning and application[C]∥International Conference on Advanced Data Mining and Applications.Sprin-ger-Verlag,2016:985-991. [33]ELAHI M,RICCI F,RUBENS N.Active learning strategies for rating elicitation in collaborative filtering:A system-wide perspective[J].Acm Transactions on Intelligent Systems & Technology,2014,5(1):1-33. [34]ELAHI M,REPSYS V,RICCI F.Rating Elicitation Strategies for Collaborative Filtering[C]∥E-Commerce and Web Techno-logies.2011,85:160-171. [35]RASHID A M,ALBERT I,DAN C,et al.Getting to know you:learning new user preferences in recommender systems[C]∥International Conference on Intelligent User Interfaces.ACM,2002:127-134. [36]PAGANO R,QUADRANA M,ELAHI M,et al.Toward Active Learning in Cross-domain Recommender Systems[J].arXiv:1701.02021,2017. [37]KOREN Y,BELL R.Advances in Collaborative Filtering[M]∥Recommender Systems Handbook.2011:145-186. [38]ELAHI M,BRAUNHOFER M,RICCI F,et al.Personality-Based Active Learning for Collaborative Filtering Recommender Systems[C]∥Congress of the Italian Association for Artificial Intelligence.Cham:Springer,2013:360-371. [39]SEUNG H,OPPER S.Query by committee[J].Proc of the Fith Workshop on Computational Learning Theory,1992,284:287-294. [40]GEIGER D.Current State of Personalized Task Recommenda-tion[M]∥Personalized Task Recommendation in Crowdsourcing Systems.Springer International Publishing,2016:15-29. [41]KRAWCZYK B,WOÒNIAK M.Online query by committee for active learning from drifting data streams[C]∥International Joint Conference on Neural Networks.IEEE,2017:38-49. [42]PICHARA K,SOTO A,ARANEDA A.Detection of Anomalies in Large Datasets Using an Active Learning Scheme Based on Dirichlet Distributions[C]∥Ibero-American Conference on Ai:Advances in Artificial Intelligence.Springer-Verlag,2008:163-172. [43]NGUYEN T T,PHAM H V,VU P M,et al.RecommendingAPI Usages for Mobile Apps with Hidden Markov Model[C]∥IEEE/ACM International Conference on Automated Software Engineering.IEEE,2016:795-800. [44]SHI S,LIU Y,HUANG Y,et al.Active Learning for kNNBased on Bagging Features[C]∥International Conference on Natural Computation.IEEE,2008:61-64. [45]FATHIAN M,HOSEINPOOR Y,MINAEI-BIDGOLI B.Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods[J].Kybernetes,2016,45(5):732-743. [46]ZHAO Y,XU C,CAO Y.Research on query-by-committeemethod of active learning and application[C]∥International Conference on Advanced Data Mining and Applications.Sprin-ger-Verlag,2006:985-991. [47]RASHID A M,KARYPIS G,RIEDL J.Learning preferences of new users in recommender systems:an information theoretic approach[J].ACM Sigkdd Explorations Newsletter,2008,10(2):90-100. [48]CARENINI G,SMITH J,POOLE D.Towards more conversational and collaborative recommender systems[C]∥InternationalConference on Intelligent User Interfaces.ACM,2003:12-18. [49]PENNOCK D M,HORVITZ E,LAWRENCE S,et al.Collaborative Filtering by Personality Diagnosis:A Hybrid Memory-and Model-Based Approach[C]∥Proceedings of IJCAI-99.2013:473-480. [50]SETTLES B,CRAVEN M.An analysis of active learning strategies for sequence labeling tasks[C]∥Conference on Empirical Methods in Natural Language Processing.Association for Computational Linguistics,2008:1070-1079. [51]IKHWANTRI F,LOUVAN S,KURNIAWAN K,et al.Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus[C]∥Proceedings of the 2007 ACM Conference on Recommender Systems.2018:37-49. [52]BRIDGE D,RICCI F.Supporting product selection with query editing recommendations[C]∥ACM Conference on Recommender Systems.ACM,2007:65-72. [53]RICCI F,NGUYEN Q N.Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System[J].IEEE Intelligent Systems,2007,22(3):22-29. [54]MCNEE S M,LAM S K,KONSTAN J A,et al.Interfaces for eliciting new user preferences in recommender systems[C]∥International Conference on User Modeling.Springer-Verlag,2003:178-187. [55]GOLBANDI N,KOREN Y,LEMPEL R.Adaptive bootstrap-ping of recommender systems using decision trees[C]∥ACM International Conference on Web Search and Data Mining.ACM,2011:595-604. [56]KARIMI R,WISTUBA M,NANOPOULOS A,et al.Factorized Decision Trees for Active Learning in Recommender Systems[C]∥IEEE,International Conference on TOOLS with Artificial Intelligence.IEEE Computer Society,2013:404-411. [57]HERLOCKER J L,KONSTAN J A,TERVEEN L G,et al.Evaluating collaborative filtering recommender systems[J].ACM Transactions on Information Systems,2004,22(1):5-53. [58]HARPALE A S,YANG Y.Personalized active learning for collaborative filtering[C]∥International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2008:91-98. [59]SHANI G,GUNAWARDANA A.Evaluating Recommendation Systems[M]∥Recommender Systems Handbook.Springer,2011:257-297. [60]LIU N N,YANG Q.EigenRank:a ranking-oriented approach to collaborative filtering[C]∥International ACM SIGIR Confe-rence on Research and Development in Information Retrieval.ACM,2008:83-90. |
[1] | QIN Qi-qi, ZHANG Yue-qin, WANG Run-ze, ZHANG Ze-hua. Hierarchical Granulation Recommendation Method Based on Knowledge Graph [J]. Computer Science, 2022, 49(8): 64-69. |
[2] | FANG Yi-qiu, ZHANG Zhen-kun, GE Jun-wei. Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning [J]. Computer Science, 2022, 49(8): 70-77. |
[3] | ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169. |
[4] | SHUAI Jian-bo, WANG Jin-ce, HUANG Fei-hu, PENG Jian. Click-Through Rate Prediction Model Based on Neural Architecture Search [J]. Computer Science, 2022, 49(7): 10-17. |
[5] | QI Xiu-xiu, WANG Jia-hao, LI Wen-xiong, ZHOU Fan. Fusion Algorithm for Matrix Completion Prediction Based on Probabilistic Meta-learning [J]. Computer Science, 2022, 49(7): 18-24. |
[6] | CAI Xiao-juan, TAN Wen-an. Improved Collaborative Filtering Algorithm Combining Similarity and Trust [J]. Computer Science, 2022, 49(6A): 238-241. |
[7] | HE Yi-chen, MAO Yi-jun, XIE Xian-fen, GU Wan-rong. Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation [J]. Computer Science, 2022, 49(6A): 272-279. |
[8] | HOU Xia-ye, CHEN Hai-yan, ZHANG Bing, YUAN Li-gang, JIA Yi-zhen. Active Metric Learning Based on Support Vector Machines [J]. Computer Science, 2022, 49(6A): 113-118. |
[9] | XIONG Zhong-min, SHU Gui-wen, GUO Huai-yu. Graph Neural Network Recommendation Model Integrating User Preferences [J]. Computer Science, 2022, 49(6): 165-171. |
[10] | HONG Zhi-li, LAI Jun, CAO Lei, CHEN Xi-liang, XU Zhi-xiong. Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration [J]. Computer Science, 2022, 49(6): 149-157. |
[11] | YU Ai-xin, FENG Xiu-fang, SUN Jing-yu. Social Trust Recommendation Algorithm Combining Item Similarity [J]. Computer Science, 2022, 49(5): 144-151. |
[12] | WANG Mei-ling, LIU Xiao-nan, YIN Mei-juan, QIAO Meng, JING Li-na. Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions [J]. Computer Science, 2022, 49(3): 99-104. |
[13] | CHEN Jin-peng, HU Ha-lei, ZHANG Fan, CAO Yuan, SUN Peng-fei. Convolutional Sequential Recommendation with Temporal Feature and User Preference [J]. Computer Science, 2022, 49(1): 115-120. |
[14] | ZHAN Wan-jiang, HONG Zhi-lin, FANG Lu-ping, WU Zhe-fu, LYU Yue-hua. Collaborative Filtering Recommendation Algorithm Based on Adversarial Learning [J]. Computer Science, 2021, 48(7): 172-177. |
[15] | ZHANG Ren-zhi, ZHU Yan. Malicious User Detection Method for Social Network Based on Active Learning [J]. Computer Science, 2021, 48(6): 332-337. |
|