Online Structured Prediction via Coactive Learning
Online Structured Prediction via Coactive Learning
复制标题
通过协作学习进行在线结构化预测
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
T. Joachims
中科院分区：
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作者：
Pannagadatta K. Shivaswamy;T. Joachims
我们提出协同学习作为一个学习系统和人类用户之间的交互模型，两者都有一个共同的目标，即为用户提供最大效用的结果。在每个步骤中，系统（例如搜索引擎）接收上下文（例如查询）并预测对象（例如排名）。如果有必要，用户通过纠正系统来做出响应，提供稍微改进的-但不一定是最佳的-对象作为反馈。我们认为，这样的反馈往往可以推断出可观察到的用户行为，例如，从点击在网络搜索。通过对用户的基数效用评估预测，我们提出了有效的学习算法，具有O（1/10 T）的平均遗憾，即使学习算法从来没有观察基数效用值在传统的在线学习。我们证明了我们的模型和学习算法的电影推荐任务的适用性，以及网络搜索的排名。
We propose Coactive Learning as a model of interaction between a learning system and a human user, where both have the common goal of providing results of maximum utility to the user. At each step, the system (e.g. search engine) receives a context (e.g. query) and predicts an object (e.g. ranking). The user responds by correcting the system if necessary, providing a slightly improved - but not necessarily optimal - object as feedback. We argue that such feedback can often be inferred from observable user behavior, for example, from clicks in web-search. Evaluating predictions by their cardinal utility to the user, we propose efficient learning algorithms that have O(1/√T) average regret, even though the learning algorithm never observes cardinal utility values as in conventional online learning. We demonstrate the applicability of our model and learning algorithms on a movie recommendation task, as well as ranking for web-search.