作者: 刪減 時(shí)間: 2025-3-21 23:54
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: hurricane 時(shí)間: 2025-3-22 03:36
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: 符合國(guó)情 時(shí)間: 2025-3-22 08:05
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: reflection 時(shí)間: 2025-3-22 10:23
Handling Preferencescept of . and then discuss how preferences can be handled for different recommendation approaches. Furthermore, we sketch how to deal with inconsistencies such as contradicting preferences of individual users.作者: 壓碎 時(shí)間: 2025-3-22 13:29
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: 壓碎 時(shí)間: 2025-3-22 18:08 作者: 提煉 時(shí)間: 2025-3-23 01:17
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: insomnia 時(shí)間: 2025-3-23 01:25
Personality, Emotions, and Group Dynamicsetermine 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: freight 時(shí)間: 2025-3-23 08:34 作者: CLOUT 時(shí)間: 2025-3-23 10:35 作者: adumbrate 時(shí)間: 2025-3-23 13:53
Bibha Chetia Borah,Biswajyoti Bordoloitechniques 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: 一回合 時(shí)間: 2025-3-23 19:21
R. C. Fetecau,J. E. Marsden,M. Westmovies 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: Conjuction 時(shí)間: 2025-3-24 01:17 作者: NICE 時(shí)間: 2025-3-24 04:57 作者: 無(wú)價(jià)值 時(shí)間: 2025-3-24 06:57 作者: 顛簸下上 時(shí)間: 2025-3-24 11:47 作者: GULP 時(shí)間: 2025-3-24 17:07 作者: 聽(tīng)覺(jué) 時(shí)間: 2025-3-24 20:30 作者: 舔食 時(shí)間: 2025-3-25 01:34
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: Commission 時(shí)間: 2025-3-25 05:46 作者: 連系 時(shí)間: 2025-3-25 10:33 作者: Nausea 時(shí)間: 2025-3-25 13:57
Mariella Nocenzi,Alessandra Sannellahis chapter, we provide an overview of existing research related to explanations in recommender systems, and specifically discuss aspects relevant to group recommendation scenarios. In this context, we present different ways of explaining and visualizing recommendations determined on the basis of . and . strategies.作者: LITHE 時(shí)間: 2025-3-25 16:09
Explanations for Groupshis chapter, we provide an overview of existing research related to explanations in recommender systems, and specifically discuss aspects relevant to group recommendation scenarios. In this context, we present different ways of explaining and visualizing recommendations determined on the basis of . and . strategies.作者: jarring 時(shí)間: 2025-3-25 21:35
Introduction to Warps in Illustrator,ems 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: 不感興趣 時(shí)間: 2025-3-26 03:55
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: Missile 時(shí)間: 2025-3-26 05:48
Alexander Felfernig,Ludovico Boratto,Marko Tkal?i?作者: 外貌 時(shí)間: 2025-3-26 11:01 作者: 原始 時(shí)間: 2025-3-26 13:19 作者: indubitable 時(shí)間: 2025-3-26 20:09
ConclusionsIn this chapter, we shortly summarize the contributions provided in this book.作者: 人造 時(shí)間: 2025-3-26 23:08
,John Fowles’s , and William Golding’s ,cally, 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: Vulnerable 時(shí)間: 2025-3-27 04:35 作者: Blasphemy 時(shí)間: 2025-3-27 09:11 作者: 機(jī)警 時(shí)間: 2025-3-27 11:58
https://doi.org/10.1007/978-3-642-49264-8cept of . and then discuss how preferences can be handled for different recommendation approaches. Furthermore, we sketch how to deal with inconsistencies such as contradicting preferences of individual users.作者: 脖子 時(shí)間: 2025-3-27 14:52 作者: Phonophobia 時(shí)間: 2025-3-27 17:50
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: 內(nèi)向者 時(shí)間: 2025-3-28 01:01
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: 的闡明 時(shí)間: 2025-3-28 05:25
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: 碎石 時(shí)間: 2025-3-28 06:59
Handling Preferencescept of . and then discuss how preferences can be handled for different recommendation approaches. Furthermore, we sketch how to deal with inconsistencies such as contradicting preferences of individual users.作者: 可耕種 時(shí)間: 2025-3-28 13:24
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: Free-Radical 時(shí)間: 2025-3-28 16:50
Group Recommender Systems978-3-319-75067-5Series ISSN 2191-8112 Series E-ISSN 2191-8120 作者: conceal 時(shí)間: 2025-3-28 19:30
Allan Simsing. For the tropics, the development of synoptic models (see reviews by Forsdyke, 1960; La Seur, 1964; Johnson, 1964; Riehl, 1954, pp. 210–234, 281–357, 1979, pp. 315–496; Ramage, 1971, pp. 38–85, 91–100) begins only in the 1940’s, except for the Tropical Cyclone. Moreover, synthesis is rendered di作者: Acetabulum 時(shí)間: 2025-3-29 01:07
Immune system and fault-tolerant computing,dures playing the role of B-cells. Those properties have previously been learnt, automatically, during testing phase using mutation analysis techniques. When a program state has to be recovered, T-cells procedures are activated in order to recover erroneous states. In this way, each software algorit作者: 合同 時(shí)間: 2025-3-29 04:10 作者: 關(guān)心 時(shí)間: 2025-3-29 08:40 作者: 希望 時(shí)間: 2025-3-29 11:47
al machines and other specialists in electrical engineering..This book presents the design methodology and electrical diagrams of symmetrical six-phase windings, the main elements of the six-phase? that are being developed to help meet the demand for high power electric drive systems that are resili作者: ODIUM 時(shí)間: 2025-3-29 16:00
