Review of group recommendation analysis and research

Yunchang WU; Baisong LIU; Yangyang WANG; Chenjie FEI

doi:10.11959/j.issn.1000-0801.2018306

您当前的位置：

首页 >

文章列表页 >

Review of group recommendation analysis and research

summarize | 更新时间：2024-06-05

- Review of group recommendation analysis and research
- Telecommunications Science Vol. 34, Issue 12, Pages: 71-83(2018)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2018306
  CLC： TP391
- Published Online：2018-12，
  
  Published：20 December 2018
- 稿件说明：
移动端阅览
Yunchang WU, Baisong LIU, Yangyang WANG, et al. Review of group recommendation analysis and research[J]. Telecommunications science, 2018, 34(12): 71-83.
DOI：

Yunchang WU, Baisong LIU, Yangyang WANG, et al. Review of group recommendation analysis and research[J]. Telecommunications science, 2018, 34(12): 71-83. DOI： 10.11959/j.issn.1000-0801.2018306.

摘要

随着大数据时代的到来，推荐系统的应用领域也愈发广泛，组推荐系统的推荐服务对象由单一用户扩展为群组成员，正成为推荐系统领域的研究热点之一。组推荐系统需要考虑所有群体成员的偏好，将各成员的偏好融合，缓解群组成员之间的偏好冲突，使推荐结果尽可能满足所有群组成员。主要对最近的组推荐的研究进展进行综述，分别对群组分类、群组发现、群组预测推荐的前沿进行总结，并概括了群组推荐的影响因素。最后，对组推荐的研究点及其展望分别进行阐述。

Abstract

With the advent of age of big data

the application fields of recommendation systems have become increasingly widespread.The recommendation service object of the group recommendation systems are expanded from a single user to a group of members

and becoming one of the research hotspots in the recommendation system fields.The group recommendation system needs to consider the preferences of all group members

and to fuse the preferences of the members

and to alleviate the conflicts of preferences among them

so that the recommendation results satisfy all group members as much as possible.The recent research progress of the group recommendation was mainly reviewed.The frontiers of group classification

group detection and group prediction recommendation were summarized.And main factors of the group recommendations were generalized.Finally

the research points of group recommendation and its prospects were separately elaborated.

关键词

Keywords

references

ZHOU X , CHEN L , ZHANG Y , et al . Enhancing online video recommendation using social user interactions [J ] . VLDB Journal , 2017 , 26 ( 1 ): 1 - 20 .

陈建 . 基于情境感知的广播电视群组推荐方法研究 [D ] . 合肥:中国科学技术大学 , 2017 .

CHEN J . Research on the group recommendation method based on context-aware for broadcast television [D ] . Hefei:University of Science and Technology of China , 2017 .

XU J C . Family shopping recommendation system using userprofile and behavior data [J ] . arXiv:1708.07289 , 2017 .

PESSEMIER T D , DHONDT J , MARTENS L . Hybrid group recommendations for a travel service [J ] . Multimedia Tools ＆Applications , 2017 , 76 ( 2 ): 1 - 25 .

林晓勇 , 俞洋 , 糜正琨 . 一种基于用户行为预测的群移动性管理模型 [J ] . 电信科学 , 2017 , 33 ( 8 ): 94 - 99 .

LIN X Y , YU Y , MI Z K . A model of group mobility management based on user behavior prediction [J ] . Telecommunications Science , 2017 , 33 ( 8 ): 94 - 99 .

XIE T , ZHENG Q , ZHANG W . A behavioral sequence analyzing framework for grouping students in an e-learning system [J ] . Knowledge-Based Systems , 2016 ( 111 ): 36 - 50 .

LAI C H . Applying knowledge flow mining to group recommendation methods for task-based groups [J ] . Journal of the Association for Information Science ＆ Technology , 2015 , 66 ( 3 ): 545 - 563 .

ZHANG C , ZHANG H , WANG J . Personalized restaurant recommendation method combining group correlations and customer preferences [J ] . Information Sciences , 2018 ( 454-455 ): 128 - 143 .

张玉洁 , 杜雨露 , 孟祥武 . 组推荐系统及其应用研究 [J ] . 计算机学报 , 2016 , 39 ( 4 ): 745 - 764 .

ZHANG Y J , DU Y L , MENG X W . Research on group recommendation system and their application [J ] . Chinese Journal of Computers , 2016 , 39 ( 4 ): 745 - 764 .

CONTRERAS D , SALAMÃ M , PASCUAL J . A Web-based environment to support online and collaborative group recommendation scenarios [J ] . Applied Artificial Intelligence , 2015 , 29 ( 5 ): 480 - 499 .

KIM N , LEE J H . Performance analysis of group recommendation systems in TV domains [J ] . International Journal of Fuzzy Logic ＆ Intelligent Systems , 2015 , 15 ( 1 ): 45 - 52 .

QUINTARELLI E , RABOSIO E , TANCA L . Recommending new items to ephemeral groups using contextual user influence [C ] // The 10th ACM Conference on Recommender Systems,September 15-19,2016,Boston,USA . New York:ACM Press , 2016 : 285 - 292 .

HU L , CAO J , XU G , et al . Deep modeling of group preferences for group-based recommendation [C ] // Twenty-Eighth AAAI Conference on Artificial Intelligence,July 27-31,2014,Québec,Canada . Palo Alto:AAAI Press , 2014 : 1861 - 1867 .

RONEN I , GUY I , KRAVI E , et al . Recommending social media content to community owners [C ] // International ACM SIGIR Conference on Research ＆ Development in Information Retrieval,July 17-21,2014,Pisa,Italy . New York:ACM Press , 2014 : 243 - 252 .

YUAN Q , CONG G , LIN C Y . COM:a generative model for group recommendation [C ] // ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,August 24-27,2014,New York,USA . New York:ACM Press , 2014 : 163 - 172 .

NTOUTSI E , STEFANIDIS K , KJETIL G , et al . Fast group recommendations by applying user clustering [M ] // Conceptual Modeling . Heidelberg:Springer , 2012 : 126 - 140 .

CHRISTENSEN I A , SCHIAFFINO S . Social influence in group recommender systems [J ] . Online Information Review , 2014 , 38 ( 4 ):5.

BOK K , LIM J , YANG H , et al . Social group recommendation based on dynamic profiles and collaborative filtering [J ] . Neurocomputing , 2016 , 209 ( C ): 3 - 13 .

王海艳 , 董茂伟 . 基于动态卷积概率矩阵分解的潜在群组推荐 [J ] . 计算机研究与发展 , 2017 , 54 ( 8 ): 1853 - 1863 .

WANG H Y , DONG M W . Latent group recommendation based on dynamic probabilistic martrix factorization model integrated with CNN [J ] . Journal of Computer Reseach and Development , 2017 , 54 ( 8 ): 1853 - 1863 .

VALLIYAMMAI C , PRASANNAVENKATESH R , VENNILA C , et al . An intelligent personalized recommendation for travel group planning based on reviews [C ] // Eighth International Conference on Advanced Computing,December 19-22,2016,Macao,China . Piscataway:IEEE Press , 2016 : 67 - 71 .

HUANG S , MA J , CHENG P , et al . A hybrid multigroup coclustering recommendation framework based on information fusion [J ] . ACM Transactions on Intelligent Systems ＆ Technology , 2015 , 6 ( 2 ): 1 - 22 .

KHAZAEI E , ALIMOHAMMADI A . An automatic user grouping model for a group recommender system in location-based social networks [J ] . International Journal of Geo-Information , 2018 , 7 ( 2 ):67.

BORATTO L , CARTA S . Using collaborative filtering to overcome the curse of dimensionality when clustering users in a group recommender system [C ] // International Conference on Enterprise Information Systems,October 12-14,2014,Thessaloniki,Greece . Heidelberg:Springer , 2014 : 564 - 572 .

SHI J , WU B , LIN X . A Latent group model for group recommendation [C ] // IEEE International Conference on Mobile Services.Jun 27-July 2,2015,New York,USA . Piscataway:IEEE Press , 2015 : 233 - 238 .

ZHANG C , ZHOU J , XIE W . A users clustering algorithm for group recommendation [C ] // 2016 4th International Conference on Applied Computing and Information Technology/3rd International Conference on Computational Science/Intelligence and Applied Informatics/1st International Conference on Big Data,Cloud Computing,Data Science ＆ Engineering (ACIT-CSIIBCD),December 12-14,2016,Las Vegas,USA . Piscataway:IEEE Press , 2016 : 352 - 356 .

GUO C , LI B , TIAN X . Flickr group recommendation using rich social media information [J ] . Neurocomputing , 2016 ( 204 ): 8 - 16 .

苑辰 . 基于重叠社区发现的群组推荐算法研究 [D ] . 北京:北京邮电大学 , 2014 .

YUAN C . Research on group recommendation algorithms based on overlapping community discovery [D ] . Beijing:Beijing University of Posts and Telecommunications , 2014 .

KAŠŠÁK O , KOMPAN M , BIELIKOVÁ M . Personalized hybrid recommendation for group of users:Top N multimedia recommender [J ] . Information Processing ＆ Management , 2015 , 52 ( 3 ): 459 - 477 .

MOVAGHAR A , MOVAGHAR A , MOVAGHAR A , et al . Centrality-based group formation in group recommender systems [C ] // International Conference on World Wide Web Companion,April 3-7,2017,Perth,Australia.[S.l.:s.n] . 2017 : 1187 - 1196 .

BORATTO L , CARTA S , FENU G . Discovery and representation of the preferences of automatically detected groups:exploiting the link between group modeling and clustering [J ] . Future Generation Computer Systems , 2016 , 64 ( 2 ): 165 - 174 .

KHOSHKANGINI R , PINI M S , ROSSI F . A self-adaptive context-aware group recommender system [M ] // AI*IA 2016 Advances in Artificial Intelligence . Heidelberg:Springer , 2016 .

MASTHOFF J , . Group recommender systems:aggregation,satisfaction and group attributes [M ] // Recommender Systems Handbook . New York:Springer US , 2015 .

CHAO D L , BALTHROP J , FORREST S . Adaptive radio:achieving consensus using negative preferences [C ] // International ACM SIGGROUP Conference on Supporting Group Work,November 6-9,2005,Florida,USA . New York:ACM Press , 2005 : 120 - 123 .

AGARWAL A , CHAKRABORTY M , CHOWDARY C R . Does order matter? Effect of order in group recommendation [J ] . Expert Systems with Applications , 2017 ( 82 ): 115 - 127 .

VILLAVICENCIO C , SCHIAFFINO S,DIAZ-PACE J A , et al . A MAS approach for group recommendation based on negotiation techniques [M ] // Advances in Practical Applications of Scalable Multi-agent Systems . Heidelberg:Springer , 2016 .

BERNIER C , BRUN A , AGHASARYAN A , et al . Topology of communities for the collaborative recommendations to groups [R ] . 2010 .

SEO Y D , KIM Y G , LEE E , et al . An enhanced aggregation method considering deviations for a group recommendation [J ] . Expert Systems with Applications , 2018 ( 93 ): 299 - 312 .

JI K , CHEN Z , SUN R , et al . GIST:a generative model with individual and subgroup-based topics for group recommendation [J ] . Expert Systems with Applications , 2018 ( 94 ): 81 - 93 .

WANG W , ZHANG G , LU J . Member contribution-based group recommender system [J ] . Decision Support Systems , 2016 ( 87 ): 80 - 93 .

DELIC A , NEIDHARDT J , NGUYEN T N , et al . Observing group decision making processes [C ] // ACM Conference on Recommender Systems,September 15-19,2016,Boston,USA . New York:ACM Press , 2016 : 147 - 150 .

唐福喜 , 刘克剑 , 冯玲 , 等 . 基于用户交互行为的群组推荐偏好融合策略 [J ] . 西华大学学报:自然科学版 , 2016 , 35 ( 3 ): 51 - 56 .

TANG F X , LIU K J , FENG L , et al . Research on the integration strategy of group recommendation based on user’s interactive behaviors [J ] . Journal of Xihua University (Natural Scinece) , 2016 , 35 ( 3 ): 51 - 56 .

CASTRO J , LU J , ZHANG G , et al . Opinion dynamics-based group recommender systems [J ] . IEEE Transactions on Systems Man ＆ Cybernetics Systems , 2017 : 1 - 13 .

MÁRQUEZ J O Á , ZIEGLER J . Preference elicitation and negotiation in a group recommender system [M ] // Human Computer Interaction-Interact 2015 . Heidelberg:Springer , 2015 : 20 - 37 .

GUO J , ZHU Y , LI A , et al . A social influence approach for group user modeling in group recommendation systems [J ] . IEEE Intelligent Systems , 2016 , 31 ( 5 ): 40 - 48 .

GUO Z , TANG C , TANG H , et al . A novel group recommendation mechanism from the perspective of preference distribution [J ] . IEEE Access , 2018 ( 6 ): 5865 - 5878 .

KAGITA V R , PUJARI A K , PADMANABHAN V . Virtual user approach for group recommender systems using precedence relations [M ] . Amsterdam : Elsevier Science Inc.Press , 2015 .

ZHAO H , LIU Q , GE Y , et al . Group preference aggregation:a Nash equilibrium approach [C ] // IEEE International Conference on Data Mining,November 18-21,2017,New Orleans,USA . Piscataway:IEEE Press , 2017 : 679 - 688 .

胡川 , 孟祥武 , 张玉洁 , 等 . 一种改进的偏好融合组推荐方法 [J ] . 软件学报 , 2018 ( 10 ): 1 - 21 .

HU C , MENG X W , ZHANG Y J , et al . An enhanced group recommendation method based on preference aggregation [J ] . Journal of Software , 2018 ( 10 ): 1 - 21 .

SACHARIDIS D , . Group recommendations by learning rating behavior [C ] // Conference on User Modeling,Adaptation and Personalization,July 4-7,2017,Prague,Czech Republic . New York:ACM Press , 2017 : 174 - 182 .

PESSEMIER T D , DOOMS S , MARTENS L . Comparison of group recommendation algorithms [J ] . Multimedia Tools ＆ Applications , 2014 , 72 ( 3 ): 2497 - 2541 .

FENG S , CAO J . Improving group recommendations via detecting comprehensive correlative information [J ] . Multimedia Tools ＆ Applications , 2015 , 1 ( 76 ): 1355 - 1377 .

KIM H N , SADDIK A E . A stochastic approach to group recommendations in social media systems [J ] . Information Systems , 2015 ( 50 ): 76 - 93 .

WANG J , LIU Z , ZHAO H . Topic oriented user influence analysis in social networks [C ] // IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology,December 6-9,2015,Singapore . Piscataway:IEEE Press , 2015 : 123 - 126 .

WANG J , ZHAO H , LIU Z , et al . Exploring user influence for topical group recommendation [J ] . Chinese Journal of Electronics , 2017 , 26 ( 1 ): 106 - 111 .

CHRISTENSEN I , SCHIAFFINO S , ARMENTANO M . Social group recommendation in the tourism domain [J ] . Journal of Intelligent Information Systems , 2016 , 47 ( 2 ): 1 - 23 .

PESSEMIER T D , DHONDT J , MARTENS L . Hybrid group recommendations for a travel service [J ] . Multimedia Tools ＆Applications , 2017 , 2 ( 76 ): 2787 - 2811 .

JHAMB Y , FANG Y . A dual-perspective latent factor model for group-aware social event recommendation [J ] . Information Processing ＆ Management , 2017 , 53 ( 3 ): 559 - 576 .

ZHANG C , GARTRELL M , MINKA T P , et al . GroupBox:a generative model for group recommendation:technical report MSR-TR–2015-61 [R ] . 2015 .

CABRERIZO F J , CHICLANA F , AL-HMOUZ R , et al . Fuzzydecision making and consensus:challenges [J ] . Journal of Intelligent ＆ Fuzzy Systems , 2015 , 29 ( 3 ).

LIAO H , XU Z , ZENG X J , et al . An enhanced consensus reaching process in group decision making with intuitionistic fuzzy preference relations [J ] . Information Sciences , 2015 , 329 ( C ): 274 - 286 .

GONZÁLEZ-ARTEAGA T , ROCÍO D A C , CHICLANA F . A new measure of consensus with reciprocal preference relations [J ] . Knowledge-Based Systems , 2016 ( 107 ): 104 - 116 .

SUN L , WANG X , WANG Z , et al . Social-aware video recommendation for online social groups [J ] . IEEE Transactions on Multimedia , 2017 , 19 ( 3 ): 609 - 618 .

BARILE F , MASTHOFF J , ROSSI S . The adaptation of an individual’s satisfaction to group context:the role of ties strength and conflicts [C ] // Conference on User Modeling,Adaptation and Personalization,July 4-7,2017,Prague,Czech Republic . New York:ACM Press , 2017 : 357 - 358 .

HONG M , JUNG J J . My movie history:social recommender system by discovering social affinities among users [M ] . Bristol : Taylor ＆ Francis,Inc., , 2016 , 47 ( 1-2 ): 88 - 110 .

SALEHI-ABARI A , BOUTILIER C . Preference-oriented social networks:group recommendation and inference [C ] // ACM Conference on Recommender Systems,September 16-20,2015,Vienna,Austria . New York:ACM Press , 2015 : 35 - 42 .

GAO L , WU J , QIAO Z , et al . Collaborative social group influence for event recommendation [C ] // 25th ACM International on Conference on Information and Knowledge Management,October 24-28,2016,Indianapolis,USA . New York:ACM Press , 2016 : 1941 - 1944 .

CHU J . Social network analysis based approach to group decision making problem with fuzzy preference relations [J ] . Journal of Intelligent ＆ Fuzzy Systems , 2016 , 3 ( 31 ): 1271 - 1285 .

LIU Y , LIANG C , CHICLANA F , et al . A trust induced recommendation mechanism for reaching consensus in group decision making [J ] . Knowledge-Based Systems , 2017 , 119 ( C ): 221 - 231 .

AMER-YAHIA S , OMIDVAR-TEHRANI B , ROY S B , et al . Group recommendation with temporal affinities [C ] // EDBT,March 23-27,2015,Brussels,Belgium.[S.l.:s.n . ] , 2015 : 159 - 168 .

HONG M , JUNG J J , CAMACHO D . GRSAT:a novel method on group recommendation by social affinity and trustworthiness [J ] . Journal of Cybernetics , 2017 , 48 ( 3 ): 140 - 161 .

GARCIA I , SEBASTIA L . A negotiation framework for heterogeneous group recommendation [J ] . Expert Systems with Applications , 2014 , 41 ( 4 ): 1245 - 1261 .

LI D , LV Q , SHANG L , et al . Efficient privacy-preserving content recommendation for online social communities [J ] . Neurocomputing , 2017 ( 219 ): 440 - 454 .

CHECCO A , BIANCHI G , LEITH D J . BLC:private factorization recommenders via automatic group learning [J ] . ACM Transactions on Privacy and Security , 2017 , 2 ( 20 ).

AHMED K W , MOURI I J , ZAMAN R , et al . A privacy preserving personalized group recommendation framework [C ] // International Conference on Advanced Computing,July 18-21,2016,Toulouse,France . Piscataway:IEEE Press , 2016 : 594 - 598 .

HE Y , ZHANG K , WANG H , et al . Impact factor-based group recommendation scheme with privacy preservation in MSN [C ] // IEEE International Conference on Communications,May 21-25,2017,Paris,France . Piscataway:IEEE Press , 2017 : 1 - 6 .

BORATTO L , CARTA S , FENU G . Analysis of the properties that affect the accuracy of a group recommender system [C ] // The 17th IEEE International Conference on Computer and Information Technology,August 21-23,2017,Helsinki,Finland . Piscataway:IEEE Press , 2017 : 102 - 107 .

ROY S B , THIRUMURUGANATHAN S , AMER-YAHIA S . et al Exploiting group recommendation functions for flexible preferences [C ] // International Conference on Data Engineering,November 14-17,2015,Atlantic City,USA . Piscataway:IEEE Press , 2015 : 412 - 423 .

WU J , CHICLANA F , FUJITA H , et al . A visual interaction consensus model for social network group decision making with trust propagation [J ] . Knowledge-Based Systems , 2017 , 122 ( C ): 39 - 50 .

WANG J , JIANG Y , SUN J , et al . Group recommendation based on a bidirectional tensor factorization model [J ] . World Wide Web-Internet ＆ Web Information Systems , 2018 ( 21 ): 961 - 984 .

QUIJANO-SANCHEZ L , SAUER C , RECIO-GARCIA J A , et al . Make it personal:a social explanation system applied to group recommendations [J ] . Expert Systems with Applications , 2017 ( 76 ): 36 - 48 .

LIN X , ZHANG M , ZHANG Y F , et al . Fairness-aware group recommendation with pareto-efficiency [C ] // 11th ACM Conference on Recommender Systems,August 27-31,2017,Como,Italy . New York:ACM Press , 2017 : 107 - 115 .

QI S , MAMOULIS N , PITOURA E , et al . Recommending packages with validity constraints to groups of users [J ] . Knowledge ＆ Information Systems , 2018 , 54 ( 2 ): 345 - 374 .

SERBOS D , QI S , MAMOULIS N , et al . Fairness in package-to-group recommendations [C ] // International World Wide Web,April 3-7,2017,Perth,Western Australia.[S.l.:s.n] . 2017 : 371 - 379 .

JORGE C , RACIEL Y , LUIS M . A fuzzy approach for naturalnoise management in group recommender systems [J ] . Expert Systems with Applications , 2018 ( 94 ): 237 - 249 .

NGUYEN T T , PHAN T C . Diversifying group recommendation [J ] . IEEE Access , 2018 , 3 ( 6 ): 2169 - 3536 .

Views

910

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰