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 language | English |
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Title of host publication | Recent Trends and Future Technology in Applied Intelligence |
Subtitle of host publication | IEA/AIE 2018 |
Place of Publication | Cham |
Publisher | Springer |
Pages | 267-278 |
Number of pages | 12 |
ISBN (Print) | 978-3-319-92057-3 |
DOIs | |
Publication status | Published - 2018 |
Event | 31st 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 2018 → 28 Jun 2018 http://ieaaie2018.encs.concordia.ca |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 10868 |
Conference
Conference | 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems |
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Abbreviated title | IEA/AIE 2018 |
Country/Territory | Canada |
City | Montreal |
Period | 25/06/18 → 28/06/18 |
Internet address |