Computer Science ›› 2019, Vol. 46 ›› Issue (8): 78-83.doi: 10.11896/j.issn.1002-137X.2019.08.012
• Big Data & Data Science • Previous Articles Next Articles
ZHANG Yan-hong1, ZHANG Chun-guang2, ZHOU Xiang-zhen3, WANG Yi-ou4
CLC Number:
[1]ZHUO Y,YOU J L,WANG J L,et al.Measurement and Re- commendation System Oriented to Online Video Service in Sea Service[J].Computer Engineering,2018,44(4):28-34,40.(in Chinese) 卓煜,尤佳莉,王劲林,等.海服务中面向在线视频服务的测量与推荐系统[J].计算机工程,2018,44(4):28-34,40. [2]WANG X,NIE X,WANG X,et al.A new recommender system framework for TV video[C]∥International Conference on Information Science & Technology.IEEE,2016:147-152. [3]SHI M Z,WU G D,ZHANG Q,et al.Research on the Long Tail Distribution Recommendation of the Multi-topic and RBM[J].Journal of Chinese Computer Systems,2018,39(2):304-309.(in Chinese) 史明哲,吴国栋,张倩,等.多主题受限玻尔兹曼机的长尾分布推荐研究[J].小型微型计算机系统,2018,39(2):304-309. [4]DEZ J,MARTNEZ-REGO D,ALONSO-BETANZOS A,et al.Optimizing novelty and diversity in recommendations[J].Progress in Artificial Intelligence,2018,1(3):1-9. [5]BENHAMOU F.Fair use and fair competition for digitized cultural goods:the case of eBooks[J].Journal of Cultural Econo-mics,2015,39(2):123-131. [6]SHEUGH L,ALIZADEH S H.A novel 2D-Graph clustering method based on trust and similarity measures to enhance accuracy and coverage in recommender systems[J].Information Scien-ces,2018,432(1):210-230. [7]WANG S.A recommendation algorithm based on aggregate diversity enhancement[J].Computer Engineering and Science,2016,38(1):183-187.(in Chinese) 王森.一种基于整体多样性增强的推荐算法[J].计算机工程与科学,2016,38(1):183-187. [8]REN C,PING Z,HUA Z.A new Collaborative Filtering technique to improve recommendation diversity[C]∥IEEE International Conference on Computer & Communications.2017. [9]DI NOIA T,OSTUNI V C,ROSATI J,et al.An analysis of users’ propensity toward diversity in recommendations[C]∥Proceedings of the 8th ACM Conference on Recommender systems.ACM,2014:285-288. [10]HE M,XIAO R,LIU W S,et al.Collaborative Filtering Recommendation Algorithm Combing Category Information and User Interests[J].Computer Science,2017,44(8):236-241.(in Chinese) 何明,肖润,刘伟世,等.融合类别信息和用户兴趣度的协同过滤推荐算法[J].计算机科学,2017,44(8):236-241. [11]WEN J H,YUAN P L,ZENG J,et al.Research on Collaborative Filtering Recommendation Algorithm Based on Topic of Tags[J].Computer Engineering,2017,43(1):247-252.(in Chinese) 文俊浩,袁培雷,曾骏,等.基于标签主题的协同过滤推荐算法研究[J].计算机工程,2017,43(1):247-252. [12]BRAUNHOFER M,ELAHI M,RICCI F.Alleviating the new user problem in collaborative filtering by exploiting personality information[J].User Modeling and User-Adapted Interaction,2016,26(2/3):221-255. [13]REN C,PING Z,HUA Z.A new Collaborative Filtering technique to improve recommendation diversity[C]∥IEEE International Conference on Computer & Communications.2017. [14]GOGNA A,MAJUMDAR A.Balancing accuracy and diversity in recommendations using matrix completion framework[J].Knowledge-Based Systems,2017,125(1):83-95. [15]CARBONELL J,GOLDSTEIN J.The use of MMR,diversity-based reranking for reordering documents and producing summaries[C]∥International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,1998:335-336. [16]ULLAH M Z,SHAJALAL M,CHY A N,et al.Query Subtopic Mining Exploiting Word Embedding for Search Result Diversification[C]∥Asia Information Retrieval Symposium.Cham:Springer,2016:308-314. [17]RAN J,LEJEUNE M A.Risk-budgeting multi-portfolio optimization with portfolio and marginal risk constraints[J].Annals of Operations Research,2018,262(2):1-32. [18]OSTUNI V C,NOIA T D,SCIASCIO E D,et al.Top-N recommendations from implicit feedback leveraging linked open data[C]∥ACM Conference on Recommender Systems.ACM,2013:85-92. [19]CLARKE C L A,KOLLA M,CORMACK G V,et al.Novelty and diversity in information retrieval evaluation[C]∥International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2008:659-666. [20]NIKOLAKOPOULOS A N,KALANTZIS V,GALLOPOULOS E,et al.Factored Proximity Models for Top-N Recommendations[C]∥IEEE International Conference on Big Knowledge.2017. [21]CASTELLS P.Improving sales diversity by recommending users to items[C]∥ACM Conference on Recommender Systems.ACM,2014:145-152. [22]ZHANG Z,FAN X Y,GUO Y T,et al.Dynamic Summarization Update Method Based on Topic Signature[J].Computer Engineering,2018,44(6):169-175.(in Chinese) 张祯,樊兴悦,郭禹田,等.基于Topic Signature的动态文摘更新方法[J].计算机工程,2018,44(6):169-175. [23]YU X S,SUN S.Research on Personalized Recommendation Model Based on Network Users’ Information Behavior.Journal of Chongqing University of Technology(Natural Science),2013,27(1):47-50.(in Chinese) 余肖生,孙珊.基于网络用户信息行为的个性化推荐模型.重庆理工大学学报(自然科学),2013,27(1):47-50. [24]WANG Y,WAN X Y,TAO Y Z,et al.Collaborative filtering recommendation algorithm based on K-medoids item clustering.Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2017,29(4):521-526.(in Chinese) 王永,万潇逸,陶娅芝,等.基于K-medoids项目聚类的协同过滤推荐算法.重庆邮电大学学报(自然科学版),2017,29(4):521-526. |
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[2] | LI Dao-quan, WU Xing-cheng and GUO Rui-min. Electronic Commerce Transaction Trust Model Based on Two Layers Nodes and Objective Risk [J]. Computer Science, 2016, 43(5): 117-121. |
[3] | TIAN Wei,XU Jing,PENG Yu-qing. Research on CF Algorithm Based on Probabilistic Analysis of Discrete Rating Vector [J]. Computer Science, 2010, 37(5): 181-183. |
[4] | . [J]. Computer Science, 2008, 35(6): 289-292. |
[5] | . [J]. Computer Science, 2008, 35(2): 109-114. |
[6] | . [J]. Computer Science, 2008, 35(1): 144-146. |
[7] | . [J]. Computer Science, 2006, 33(3): 87-88. |
[8] | SUN Peng (Departement of Electronic Serises,Peking University,Beijing 102600). [J]. Computer Science, 2006, 33(1): 137-140. |
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