Member contribution-based group recommender system

被引:105
|
作者
Wang, Wei [1 ]
Zhang, Guangquan [1 ]
Lu, Jie [1 ]
机构
[1] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Decis Syst & E Serv Intelligence Lab, POB 123, Sydney, NSW 2007, Australia
基金
澳大利亚研究理事会;
关键词
Recommender systems; Group recommender systems; Collaborative filtering; Tourism; e-Services; MATRIX FACTORIZATION; TRUST;
D O I
10.1016/j.dss.2016.05.002
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Developing group recommender systems (GRSs) is a vital requirement in many online service systems to provide recommendations in contexts in which a group of users are involved. Unfortunately, GRSs cannot be effectively supported using traditional individual recommendation techniques because it needs new models to reach an agreement to satisfy all the members of this group, given their conflicting preferences. Our goal is to generate recommendations by taking each group member's contribution into account through weighting members according to their degrees of importance. To achieve this goal, we first propose a member contribution score (MCS) model, which employs the separable non-negative matrix factorization technique on a group rating matrix, to analyze the degree of importance of each member. A Manhattan distance-based local average rating (MLA) model is then developed to refine predictions by addressing the fat tail problem. By integrating the MCS and MLA models, a member contribution-based group recommendation (MC-GR) approach is developed. Experiments show that our MC-GR approach achieves a significant improvement in the performance of group recommendations. Lastly, using the MC-GR approach, we develop a group recommender system called GroTo that can effectively recommend activities to web-based tourist groups. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:80 / 93
页数:14
相关论文
共 50 条
  • [31] RetroSynX: A retrosynthetic analysis framework using hybrid reaction templates and group contribution-based thermodynamic models
    Wang, Wenlong
    Liu, Qilei
    Zhang, Lei
    Dong, Yachao
    Du, Jian
    CHEMICAL ENGINEERING SCIENCE, 2022, 248
  • [32] A chat-based group recommender system for tourism
    Thuy Ngoc Nguyen
    Ricci, Francesco
    INFORMATION TECHNOLOGY & TOURISM, 2018, 18 (1-4) : 5 - 28
  • [33] A chat-based group recommender system for tourism
    Thuy Ngoc Nguyen
    Francesco Ricci
    Information Technology & Tourism, 2018, 18 : 5 - 28
  • [34] Hybrid POI group recommender system based on group type in LBSN
    Sojahrood, Zahra Bahari
    Taleai, Mohammad
    Cheng, Hao
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 219
  • [35] A Group Travel Recommender System Based on Group Approximate Constraint Satisfaction
    Kyeong Kim, Jae
    Cheul Kwon, Woo
    Choi, Il Young
    Heo, Hyuk
    Moon, Hyun Sil
    IEEE ACCESS, 2024, 12 : 96113 - 96125
  • [36] Group contribution-based LCA models to enable screening for environmentally benign novel chemicals in CAMD applications
    Baxevanidis, Pantelis
    Papadokonstantakis, Stavros
    Kokossis, Antonis
    Marcoulaki, Effie
    AICHE JOURNAL, 2022, 68 (03)
  • [38] Contributorship in scientific collaborations: The perspective of contribution-based byline orders
    Lu, Chao
    Zhang, Chenwei
    Xiao, Chengrui
    Ding, Ying
    INFORMATION PROCESSING & MANAGEMENT, 2022, 59 (03)
  • [39] Tourism Group Recommender System
    Al-Ajlan, Amani
    Alabdulwahab, Sarah
    Aljeraisy, Lulwa
    Althakafi, Asmaa
    Alhassoun, Rand
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2022, 22 (04): : 637 - 644
  • [40] Near-efficient equilibria in contribution-based competitive grouping
    Gunnthorsdottir, Anna
    Vragov, Roumen
    Seifert, Stefan
    McCabe, Kevin
    JOURNAL OF PUBLIC ECONOMICS, 2010, 94 (11-12) : 987 - 994