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書(shū)目名稱(chēng)Group Recommender Systems影響因子(影響力)




書(shū)目名稱(chēng)Group Recommender Systems影響因子(影響力)學(xué)科排名




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書(shū)目名稱(chēng)Group Recommender Systems網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




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書(shū)目名稱(chēng)Group Recommender Systems讀者反饋




書(shū)目名稱(chēng)Group Recommender Systems讀者反饋學(xué)科排名

刪減

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.

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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.

符合國(guó)情

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. ..

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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.

壓碎

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

壓碎

提煉

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

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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

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