Group recommendation algorithm based on adaptive aggregation
Authors: 
Yonggui Wang, Wenbo YangAuthors Info & Claims
Pages 260 - 264
Abstract
Group recommendation needs to consider the preferences of all members in a group, and recommend items to the group by aggregating member preferences. In the group preference aggregation stage, most existing aggregation methods mainly focus on aggregating members' scores on items according to scoring characteristics, ignoring the rationality and interpretability of members' weights in aggregation functions. To solve this problem, this paper proposes an adaptive aggregation model (AJ) based on item type. In order to verify the effectiveness of the proposed method, a comparative experiment is carried out on MovieLens, and the experimental result show that the proposed method has a good recommendation effect.
References
[1]
WANG Y G, Lin J M, He J Y. Group recommendation method integrating leader influence and implicit trust [J]. Computer Engineering and Application, 2022, 58 (09): 98-106.
[2]
XU X M, MEI H Y, YU H, LI X H. Research review of group recommendation methods based on preference fusion [J]. Small Microcomputer System, 2020, 41 (12): 2500-2508.
[3]
Ardissono L, Goy A, Petrone G, Tailoring the recommendation of tourist information to heterogeneous user groups[C]//Hypermedia: Openness, Structural Awareness, and Adaptivity: International Workshops OHS-7, SC-3, and AH-3 Aarhus, Denmark, August 14–18, 2001 Revised Papers 3. Springer Berlin Heidelberg, 2002: 280-295.
[4]
McCarthy K, Salamó M, Coyle L, Cats: A synchronous approach to collaborative group recommendation[C]//Florida Artificial Intelligence Research Society Conference (FLAIRS). 2006: 86-91.
[5]
Liu X, Tian Y, Ye M, Exploring personal impact for group recommendation[C]//Proceedings of the 21st ACM international conference on Information and knowledge management. 2012: 674-683.
[6]
Quijano-Sánchez L, Díaz-Agudo B, Recio-García J A. Development of a group recommender application in a social network[J]. Knowledge-Based Systems, 2014, 71: 72-85.
[7]
De Pessemier T, Dooms S, Martens L. Comparison of group recommendation algorithms[J]. Multimedia tools and applications, 2014, 72: 2497-2541.
[8]
ZHAO H Y, CHENG R Y, CHEN Q K, CAO J. Group recommendation system: present situation and prospect [J]. Small Microcomputer System, 2021, 42 (06): 1144-1151.
[9]
Mahyar H, Ghalebi K E, Morshedi S M, Centrality-based group formation in group recommender systems[C]//Proceedings of the 26th International Conference on World Wide Web Companion. 2017: 1187-1196.
[10]
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: 165-174.
[11]
Christensen I A, Schiaffino S. Entertainment recommender systems for group of users[J]. Expert systems with applications, 2011, 38(11): 14127-14135.
[12]
CHEN J T, GU T L, CHANG L, BIN C Z, LIANG C. Tourism group recommendation method integrating collaborative filtering and user preference [J]. Journal of Intelligent Systems, 2018, 13 (06): 999-1005.
[13]
TAO Y C, DING X, SHI L, WEI L. Group interest point recommendation model based on user sign-in behavior [J]. Small Microcomputer System, 2018, 39 (10): 2260-2265.
[14]
WANG X, DENG W W, YU J J. A group recommendation algorithm considering the acceptance and similarity of group members [J]. Computer Application Research, 2017, 34 (11): 3285-3290 +3298.
[15]
Nguyen Thuy Ngoc,Ricci Francesco. A chat-based group recommender system for tourism[J]. Information Technology & Tourism, 2018, 18(1-4).
[16]
YIN Q S. Group recommendation method based on improved matrix decomposition of group information [J]. Computer Application and Software, 2020, 37 (09): 328-333.
[17]
PUJAHARI A,SISODIA D S.Preference relation based collaborative filtering with graph aggregation for group recommender system[J].Applied Intelligence, 2021, 51 (1): 1-15.
[18]
WANG Y G, ZHANG J.Group recommendation method combining probability matrix factorization and ER rule [J]. Computer Engineering and Application, 2023, 59 (05): 252-261.
Index Terms
- Group recommendation algorithm based on adaptive aggregation
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Published In
November 2023
1156 pages
ISBN:9798400716478
DOI:10.1145/3656766
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 01 June 2024
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ICBAR 2023
ICBAR 2023: 2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management
November 24 - 26, 2023
Chengdu, China
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