偏好学习Preference Learning |
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课程网址: | http://videolectures.net/ecmlpkdd2010_hullermeier_furnkranz_pl/ |
主讲教师: | Johannes Fürnkranz, Eyke Hullermeier |
开课单位: | 达姆施塔特工业大学 |
开课时间: | 2010-11-16 |
课程语种: | 英语 |
中文简介: | “偏好”主题最近在人工智能和机器学习中引起了相当多的关注,其中偏好学习的主题已经成为一个新的跨学科研究领域,与运筹学,社会选择和决策理论等相关领域有着密切的联系。粗略地说,偏好学习是关于从显性或隐性偏好信息学习偏好模型的方法,通常用于预测个体或一组个体的偏好。与该领域相关的方法包括学习特殊类型的偏好模型,例如词典顺序,而不是“学习排名”。用于信息检索到推荐系统的协同过滤技术。本教程的主要目标是在当前的开发阶段调查偏好学习领域。演讲将侧重于系统概述不同类型的偏好学习问题,解决这些问题的方法和算法,以及评估从数据引起的偏好模型的性能的指标。 |
课程简介: | The topic of "preferences" has recently attracted considerable attention in artificial intelligence in general and machine learning in particular, where the topic of preference learning has emerged as a new, interdisciplinary research field with close connections to related areas such as operations research, social choice and decision theory. Roughly speaking, preference learning is about methods for learning preference models from explicit or implicit preference information, typically used for predicting the preferences of an individual or a group of individuals. Approaches relevant to this area range from learning special types of preference models, such as lexicographic orders, over "learning to rank" for information retrieval to collaborative filtering techniques for recommender systems. The primary goal of this tutorial is to survey the field of preference learning in its current stage of development. The presentation will focus on a systematic overview of different types of preference learning problems, methods and algorithms to tackle these problems, and metrics for evaluating the performance of preference models induced from data. |
关 键 词: | 偏好学习; 系统协同过滤技术; 性能评价指标 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-18:dingaq |
阅读次数: | 204 |