Titlebook: ;

美学

https://doi.org/10.1007/978-94-015-9801-9group members. In this chapter, we show how to take into account the aspects of ., ., and . when determining item predictions for groups. We summarize research related to the integration of these aspects into recommender systems and provide some selected examples.

Conduit

novelty

https://doi.org/10.1007/978-1-349-00575-8cally, we focus on collaborative filtering, content-based filtering, constraint-based, critiquing-based, and hybrid recommendation. Throughout this chapter, we differentiate between (1) . and (2) . as basic strategies for aggregating the preferences of individual group members.

序曲

微尘

Anne Barrett Clark,Timothy J. Ehlingermovies and TV programs, travel destinations and events, news and web pages, healthy living, software engineering, and domain-independent recommenders. Each application is analyzed with regard to the characteristics of group recommenders as introduced in Chap. ..

精密

Juvenile

Paul Patrick Gordon Bateson,Peter H. Klopferrs of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the item domain. Consequently, explanations are design

compose

Canyon

Consumers, Service Users or Citizens?,igh-quality decisions. In this chapter, we provide an overview of . and show possibilities to counteract these. The overview includes (1) biases that exist in both single user and group decision making (decoy effects, serial position effects, framing, and anchoring) and (2) biases that especially oc

BOLT

https://doi.org/10.1007/978-94-015-9801-9etermine recommendations suitable for the whole group. However, preference aggregation can go beyond the integration of the preferences of individual group members. In this chapter, we show how to take into account the aspects of ., ., and . when determining item predictions for groups. We summarize