Socially-Aware Recommendation for Over-Constrained Problems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationRecent Trends and Future Technology in Applied Intelligence
Subtitle of host publicationIEA/AIE 2018
Place of PublicationCham
PublisherSpringer
Pages267-278
Number of pages12
ISBN (Print)978-3-319-92057-3
DOIs
Publication statusPublished - 2018
Event31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Recent Trends and Future Technology in Applied Intelligence - Concordia University, Montreal, Canada
Duration: 25 Jun 201828 Jun 2018
http://ieaaie2018.encs.concordia.ca

Publication series

Name Lecture Notes in Computer Science
Volume10868

Conference

Conference31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
Abbreviated titleIEA/AIE 2018
CountryCanada
CityMontreal
Period25/06/1828/06/18
Internet address

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