作者: 脫水 時間: 2025-3-22 00:19 作者: jocular 時間: 2025-3-22 02:57
Perspectives in Environmental Managementtechniques for group recommender systems are often the same or similar to those that are used for single user recommenders. We show how to apply these techniques on the basis of examples and introduce evaluation approaches that are specifically useful in group recommendation scenarios.作者: 收養(yǎng) 時間: 2025-3-22 07:45 作者: 傾聽 時間: 2025-3-22 10:46 作者: 租約 時間: 2025-3-22 16:23 作者: 租約 時間: 2025-3-22 20:16
Algorithms for Group Recommendationcally, 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.作者: MAOIS 時間: 2025-3-22 21:16 作者: Crohns-disease 時間: 2025-3-23 01:30
Group Recommender Applicationsmovies 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. ..作者: IOTA 時間: 2025-3-23 06:08 作者: 討厭 時間: 2025-3-23 11:11
Further Choice Scenariosrios exist that differ in the way alternatives are represented and recommendations are determined. We introduce a categorization of these scenarios and discuss knowledge representation and group recommendation aspects on the basis of examples.作者: 有雜色 時間: 2025-3-23 14:27 作者: 使服水土 時間: 2025-3-23 18:40
ConclusionsIn this chapter, we shortly summarize the contributions provided in this book.作者: discord 時間: 2025-3-24 00:04
Perspectives in Electronic Structure Theoryems that determine recommendations for groups. In this chapter, we provide an introduction to basic types of recommendation algorithms for individual users and characterize related decision tasks. This introduction serves as a basis for the introduction of group recommendation algorithms in Chap. ..作者: BAIL 時間: 2025-3-24 06:07
Decision Tasks and Basic Algorithmsems that determine recommendations for groups. In this chapter, we provide an introduction to basic types of recommendation algorithms for individual users and characterize related decision tasks. This introduction serves as a basis for the introduction of group recommendation algorithms in Chap. ..作者: FANG 時間: 2025-3-24 10:08 作者: 善變 時間: 2025-3-24 12:15 作者: Entropion 時間: 2025-3-24 16:34
Biases in Group Decisionsexist in both single user and group decision making (decoy effects, serial position effects, framing, and anchoring) and (2) biases that especially occur in the context of group decision making (GroupThink, polarization, and emotional contagion).作者: 發(fā)牢騷 時間: 2025-3-24 21:28
Personality, Emotions, and Group Dynamicsgroup 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.作者: 使?jié)M足 時間: 2025-3-24 23:28
Consumers, Service Users or Citizens?,exist in both single user and group decision making (decoy effects, serial position effects, framing, and anchoring) and (2) biases that especially occur in the context of group decision making (GroupThink, polarization, and emotional contagion).作者: 美學(xué) 時間: 2025-3-25 03:55
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.作者: Conduit 時間: 2025-3-25 07:38 作者: novelty 時間: 2025-3-25 12:28
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.作者: 序曲 時間: 2025-3-25 18:05 作者: 微塵 時間: 2025-3-25 21:36
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. ..作者: 精密 時間: 2025-3-26 00:14 作者: Juvenile 時間: 2025-3-26 06:55
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作者: compose 時間: 2025-3-26 12:27 作者: Canyon 時間: 2025-3-26 14:00
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作者: BOLT 時間: 2025-3-26 17:41
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作者: interrogate 時間: 2025-3-26 21:39 作者: liposuction 時間: 2025-3-27 04:21
Algorithms for Group Recommendationcally, 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.作者: evince 時間: 2025-3-27 07:29
Evaluating Group Recommender Systemstechniques for group recommender systems are often the same or similar to those that are used for single user recommenders. We show how to apply these techniques on the basis of examples and introduce evaluation approaches that are specifically useful in group recommendation scenarios.作者: Occipital-Lobe 時間: 2025-3-27 13:15 作者: 連接 時間: 2025-3-27 15:50 作者: adroit 時間: 2025-3-27 18:33
Explanations for Groupsrs 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作者: GLIDE 時間: 2025-3-27 22:56 作者: Adenocarcinoma 時間: 2025-3-28 04:01
Biases in Group Decisionsigh-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作者: emission 時間: 2025-3-28 07:39 作者: 忘恩負(fù)義的人 時間: 2025-3-28 12:57
