PolyLens: A recommender system for groups of user
PolyLens: A recommender system for groups of user
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DOI：
10.1007/0-306-48019-0_11
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发表时间：
2001-09
期刊：
影响因子：
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通讯作者：
Mark O'Connor;D. Cosley;J. Konstan;J. Riedl
中科院分区：
文献类型：
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作者：
Mark O'Connor;D. Cosley;J. Konstan;J. Riedl
我们提出了PolyLens，一个新的协同过滤推荐系统，旨在推荐项目的用户群体，而不是个人。对于多人参与单个活动的领域，如电影和餐馆，组推荐器更合适和有用。我们提出了一个分析的主要设计问题，为组群，包括问题的性质，组成员的权利，社会价值功能的群体，和界面显示组的建议。然后，我们报告了我们的PolyLens原型，以及我们从包括819名用户的9个月试用的使用日志和调查中学到的经验教训。我们发现，用户不仅重视群组推荐，而且愿意放弃一些隐私来获得群组推荐的好处。用户重视对群组推荐系统的扩展，使他们能够通过电子邮件邀请非成员参与。
We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users. We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations. Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email.