Socially-Aware Recommendation for Over-Constrained Problems

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

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.
Originalspracheenglisch
TitelRecent Trends and Future Technology in Applied Intelligence
UntertitelIEA/AIE 2018
ErscheinungsortCham
Herausgeber (Verlag)Springer
Seiten267-278
Seitenumfang12
ISBN (Print)978-3-319-92057-3
DOIs
PublikationsstatusVeröffentlicht - 2018
Veranstaltung31st 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 Jun 201828 Jun 2018
http://ieaaie2018.encs.concordia.ca

Publikationsreihe

Name Lecture Notes in Computer Science
Band10868

Konferenz

Konferenz31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
KurztitelIEA/AIE 2018
LandKanada
OrtMontreal
Zeitraum25/06/1828/06/18
Internetadresse

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  • Dieses zitieren

    Atas, M., Tran, T. N. T., Felfernig, A., & Samer, R. (2018). Socially-Aware Recommendation for Over-Constrained Problems. in Recent Trends and Future Technology in Applied Intelligence : IEA/AIE 2018 (S. 267-278). ( Lecture Notes in Computer Science; Band 10868). Cham: Springer. https://doi.org/10.1007/978-3-319-92058-0_25