short-paper

Preference Networks and Non-Linear Preferences in Group Recommendations

Authors:

Francesco Ricci,

Julia NeidhardtAuthors Info & Claims

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

Pages 403 - 407

https://doi.org/10.1145/3350546.3352556

Published: 14 October 2019 Publication History

Abstract

Group recommender systems generate recommendations for a group by aggregating individual members’ preferences and finding items that are liked by most of the members. In this paper we introduce a new approach to preference aggregation and group choice prediction that is based on a new form of weighting individuals’ preferences. The approach is based on network science, and, in particular, it relies on the computation of node centrality scores in preferences similarity networks of groups. We also motivate and introduce a non-linear (exponential) remapping of the individuals’ preferences. Based on offline experiments we demonstrate: 1) non-linear remapping of preferences is useful to better predict group choices and generate recommendations; and 2) our weighted approach predicts the actual group choices more accurately than current state-of-the-art methods for group recommendations.

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Cited By

Byabartta Kar MKrishankumar RPamucar DKar S(2023)A decision framework with nonlinear preferences and unknown weight information for cloud vendor selectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118982213:PAOnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118982
Tran TFelfernig ALe V(2023)An overview of consensus models for group decision-making and group recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09380-z34:3(489-547)Online publication date: 22-Sep-2023
https://doi.org/10.1007/s11257-023-09380-z
Kumar JPatra BSahoo BBabu K(2023)Group recommendation exploiting characteristics of user-item and collaborative rating of usersMultimedia Tools and Applications10.1007/s11042-023-16799-483:10(29289-29309)Online publication date: 12-Sep-2023
https://doi.org/10.1007/s11042-023-16799-4
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Index Terms

Preference Networks and Non-Linear Preferences in Group Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

October 2019

507 pages

ISBN:9781450369343

DOI:10.1145/3350546

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 14 October 2019

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Short-paper
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WI '19

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

October 14 - 17, 2019

Thessaloniki, Greece

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Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

Byabartta Kar MKrishankumar RPamucar DKar S(2023)A decision framework with nonlinear preferences and unknown weight information for cloud vendor selectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118982213:PAOnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118982
Tran TFelfernig ALe V(2023)An overview of consensus models for group decision-making and group recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09380-z34:3(489-547)Online publication date: 22-Sep-2023
https://doi.org/10.1007/s11257-023-09380-z
Kumar JPatra BSahoo BBabu K(2023)Group recommendation exploiting characteristics of user-item and collaborative rating of usersMultimedia Tools and Applications10.1007/s11042-023-16799-483:10(29289-29309)Online publication date: 12-Sep-2023
https://doi.org/10.1007/s11042-023-16799-4
Ceh-Varela ECao HLauw H(2022)Performance Evaluation of Aggregation-based Group Recommender Systems for Ephemeral GroupsACM Transactions on Intelligent Systems and Technology10.1145/354280413:6(1-26)Online publication date: 22-Sep-2022
https://dl.acm.org/doi/10.1145/3542804
García-Zamora DLabella ÁRodríguez RMartínez L(2022)Nonlinear Scaled Preferences in Linguistic Multi-criteria Group Decision MakingReal Life Applications of Multiple Criteria Decision Making Techniques in Fuzzy Domain10.1007/978-981-19-4929-6_3(59-83)Online publication date: 2-Dec-2022
https://doi.org/10.1007/978-981-19-4929-6_3
Delić ANguyen TTkalčič M(2022)Group Decision-Making and Designing Group Recommender SystemsHandbook of e-Tourism10.1007/978-3-030-48652-5_57(941-963)Online publication date: 2-Sep-2022
https://doi.org/10.1007/978-3-030-48652-5_57
Tran TFelfernig ATintarev N(2021)Humanized Recommender Systems: State-of-the-art and Research IssuesACM Transactions on Interactive Intelligent Systems10.1145/344690611:2(1-41)Online publication date: 21-Jul-2021
https://dl.acm.org/doi/10.1145/3446906
García‐Zamora DLabella ÁRodríguez RMartínez L(2021)Nonlinear preferences in group decision‐making. Extreme values amplifications and extreme values reductionsInternational Journal of Intelligent Systems10.1002/int.2256136:11(6581-6612)Online publication date: 24-Sep-2021
https://dl.acm.org/doi/10.1002/int.22561
Delić ANguyen TTkalčič M(2020)Group Decision-Making and Designing Group Recommender SystemsHandbook of e-Tourism10.1007/978-3-030-05324-6_57-1(1-23)Online publication date: 2-Apr-2020
https://doi.org/10.1007/978-3-030-05324-6_57-1

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