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.
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Notes
- 1.
The work presented in this paper has been partially conducted within the scope of the research projects WeWant (basic research project funded by the Austrian Research Promotion Agency) and OpenReq (Horizon 2020 project funded by the European Union).
- 2.
This approach follows group synthesis approaches as introduced in [2].
- 3.
Graz University of Technology (www.tugraz.at) and Alpen-Adria Universität Klagenfurt (www.aau.at).
- 4.
All the products from the product catalog were manually collected from www.nikonusa.com and www.nikon.de.
- 5.
The precision of each aggregation function is not high, because these are the average precisions calculated for all different combinations of weight sequences and group sizes (we varied 210 different weight sequences and six different group sizes).
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Atas, M., Tran, T.N.T., Felfernig, A., Samer, R. (2018). Socially-Aware Recommendation for Over-Constrained Problems. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_25
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