Group Recommender Systems: Aggregation, Satisfaction and Group Attributes
Group Recommender Systems: Aggregation, Satisfaction and Group Attributes
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DOI：
10.1007/978-1-4899-7637-6_22
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发表时间：
2015
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影响因子：
--
通讯作者：
Judith Masthoff
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文献类型：
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作者：
Judith Masthoff
这 第一章展示了一个系统如何通过聚合来自各个用户模型的信息并对用户的情感状态进行建模来向一组用户推荐 .它总结了以前在这些领域的研究成果。它探讨了如何将组属性合并到聚合中 战略布局此外，它还展示了如何进行群体推荐 当向个人推荐时，可以应用技术，特别是用于解决冷启动问题和处理多个标准。
This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modeling the user’s affective state . It summarizes results from previous research in these areas. It explores how group attributes can be incorporated in aggregation strategies. Additionally, it shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.