arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.06576cs.IR
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从用户与排名的互动中学习：领域的统一

Learning from User Interactions with Rankings: A Unification of the Field

Harrie Oosterhuis

备注：
PhD Thesis of Harrie Oosterhuis defended at the University of Amsterdam on November 27th 2020
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摘要：

排名系统构成了在线搜索引擎和推荐服务的基础。它们处理大量的项目，例如网页或电子商务产品，并向用户呈现一个小的有序选择。排名系统的目标是帮助用户以最少的努力找到他们正在寻找的项目。因此，它们产生的排名应该将最相关或最受欢迎的项目放在排名的顶部。学习排序是机器学习中的一个领域，其涵盖了优化排序系统的方法。这个目标传统的监督学习排序方法利用专家判断进行评价和学习，然而，在许多情况下，这样的判断是不可能或不可行的。作为解决方案，已经引入了替代地基于用户点击来执行学习排序的方法。点击的困难在于，它们不仅受到用户偏好的影响，还受到显示的排名的影响。因此，这些方法必须防止被用户偏好之外的其他因素所偏置。本论文主要研究基于用户点击的学习排序方法，旨在统一不同的排序方法。作为一个整体，本文的第二部分提出了一个框架，弥合了在线，反事实和监督学习排序领域之间的许多差距。它采用了以前被认为是独立的方法，并将它们统一为一种方法，用于广泛适用和有效的学习排序。

英文摘要：

Ranking systems form the basis for online search engines and recommendation services. They process large collections of items, for instance web pages or e-commerce products, and present the user with a small ordered selection. The goal of a ranking system is to help a user find the items they are looking for with the least amount of effort. Thus the rankings they produce should place the most relevant or preferred items at the top of the ranking. Learning to rank is a field within machine learning that covers methods which optimize ranking systems w.r.t. this goal. Traditional supervised learning to rank methods utilize expert-judgements to evaluate and learn, however, in many situations such judgements are impossible or infeasible to obtain. As a solution, methods have been introduced that perform learning to rank based on user clicks instead. The difficulty with clicks is that they are not only affected by user preferences, but also by what rankings were displayed. Therefore, these methods have to prevent being biased by other factors than user preference. This thesis concerns learning to rank methods based on user clicks and specifically aims to unify the different families of these methods. As a whole, the second part of this thesis proposes a framework that bridges many gaps between areas of online, counterfactual, and supervised learning to rank. It has taken approaches, previously considered independent, and unified them into a single methodology for widely applicable and effective learning to rank from user clicks.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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