Abstract
Group recommender systems support the identification of items that best fit the individual preferences of all group members. A group recommendation can be determined on the basis of aggregation functions. However, to some extent it is still unclear which aggregation function is most suitable for predicting an item to a group. In this paper, we analyze different preference aggregation functions with regard to their prediction quality. We found out that consensus-based aggregation functions (e.g., Average, Minimal Group Distance, Multiplicative, Ensemble Voting) which consider all group members’ preferences lead to a better prediction quality compared to borderline aggregation functions, such as Least Misery and Most Pleasure which solely focus on preferences of some individual group members.
Originalsprache | englisch |
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Titel | Recent Trends and Future Technology in Applied Intelligence |
Untertitel | IEA/AIE 2018 |
Erscheinungsort | Cham |
Herausgeber (Verlag) | Springer |
Seiten | 267-278 |
Seitenumfang | 12 |
ISBN (Print) | 978-3-319-92057-3 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2018 |
Veranstaltung | 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Recent Trends and Future Technology in Applied Intelligence - Concordia University, Montreal, Kanada Dauer: 25 Juni 2018 → 28 Juni 2018 http://ieaaie2018.encs.concordia.ca |
Publikationsreihe
Name | Lecture Notes in Computer Science |
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Band | 10868 |
Konferenz
Konferenz | 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems |
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Kurztitel | IEA/AIE 2018 |
Land/Gebiet | Kanada |
Ort | Montreal |
Zeitraum | 25/06/18 → 28/06/18 |
Internetadresse |