TY - CHAP
T1 - Algorithms for Group Recommendation
AU - Felfernig, Alexander
AU - Atas, Müslüm
AU - Helic, Denis
AU - Tran, Thi Ngoc Trang
AU - Stettinger, Martin
AU - Samer, Ralph
PY - 2018
Y1 - 2018
N2 - In this chapter, our aim is to show how group recommendation can be implemented on the basis of recommendation paradigms for individual users. Specifically, we focus on collaborative filtering, content-based filtering, constraint-based, critiquing-based, and hybrid recommendation. Throughout this chapter, we differentiate between (1) aggregated predictions and (2) aggregated models as basic strategies for aggregating the preferences of individual group members.
AB - In this chapter, our aim is to show how group recommendation can be implemented on the basis of recommendation paradigms for individual users. Specifically, we focus on collaborative filtering, content-based filtering, constraint-based, critiquing-based, and hybrid recommendation. Throughout this chapter, we differentiate between (1) aggregated predictions and (2) aggregated models as basic strategies for aggregating the preferences of individual group members.
KW - group recommendation
KW - algorithms
UR - https://link.springer.com/chapter/10.1007/978-3-319-75067-5_2
U2 - https://doi.org/10.1007/978-3-319-75067-5_2
DO - https://doi.org/10.1007/978-3-319-75067-5_2
M3 - Chapter
SN - 978-3-319-75066-8
T3 - SpringerBriefs in Electrical and Computer Engineering
SP - 27
EP - 58
BT - Group Recommender Systems
PB - Springer
CY - Cham
ER -