Collaborative future event recommendation
Collaborative future event recommendation
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DOI：
10.1145/1871437.1871542
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
Einat Minkov;B. Charrow;J. Ledlie;S. Teller;T. Jaakkola
中科院分区：
文献类型：
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作者：
Einat Minkov;B. Charrow;J. Ledlie;S. Teller;T. Jaakkola
我们展示了一种方法，为未来事件的协作排名。推荐系统的先前工作通常依赖于对特定项目（如电影）的反馈，并将其推广到其他项目或其他人。相比之下，我们检查一个设置，其中没有反馈存在于特定的项目。因为对于没有发生的事件不存在直接反馈，所以我们根据个人对过去事件的偏好，结合其他人的好恶来推荐它们。我们通过对学术（科学）谈话推荐的用户研究来研究看不见的项目推荐的主题，我们的目标是正确估计每个用户的排名函数，预测他们最感兴趣的谈话。然后通过将用户参数分解为共享维度和个体维度，我们根据用户共享这些维度的程度推导出用户之间的相似性度量。我们表明，未来事件的协作排名预测比纯粹的基于内容的推荐更有效。最后，为了进一步减少对明确的用户反馈的需要，我们提出了一种主动学习的方法，用于引发反馈和方法，将可用的隐式用户线索。
We demonstrate a method for collaborative ranking of future events. Previous work on recommender systems typically relies on feedback on a particular item, such as a movie, and generalizes this to other items or other people. In contrast, we examine a setting where no feedback exists on the particular item. Because direct feedback does not exist for events that have not taken place, we recommend them based on individuals' preferences for past events, combined collaboratively with other peoples' likes and dislikes. We examine the topic of unseen item recommendation through a user study of academic (scientific) talk recommendation, where we aim to correctly estimate a ranking function for each user, predicting which talks would be of most interest to them. Then by decomposing user parameters into shared and individual dimensions, we induce a similarity metric between users based on the degree to which they share these dimensions. We show that the collaborative ranking predictions of future events are more effective than pure content-based recommendation. Finally, to further reduce the need for explicit user feedback, we suggest an active learning approach for eliciting feedback and a method for incorporating available implicit user cues.