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.
|Title of host publication||Group Recommender Systems|
|Subtitle of host publication||An Introduction|
|Place of Publication||Cham|
|Number of pages||32|
|Publication status||Published - 2018|
|Name||SpringerBriefs in Electrical and Computer Engineering|
- group recommendation