自适应上下文Bandits关键词抽取在停泊域广告中的应用
Adaptive Keywords Extraction with Contextual Bandits for Advertising on Parked Domains
摘要：
域名注册商和URL缩短器服务提供商将广告放置在停放的域（未使用的互联网域名）上以产生利润。由于Web内容已被删除，因此确保显示的广告与已被定向到停放域的访问者的意图直接相关至关重要。由于这些领域中缺少内容，因此生成关键字来描述先前的内容并因此描述用户意图是不平凡的。本文讨论了自适应关键词抽取问题，提出了一种基于BM25F词加权和线性多臂强盗的自适应关键词抽取算法。我们在生产域注册系统上构建了一个原型，并使用众包进行多次迭代对其进行评估。原型与其他流行的方法进行了比较，并被证明是更有效的。
英文摘要：
Domain name registrars and URL shortener service providers place advertisements on the parked domains (Internet domain names which are not in service) in order to generate profits. As the web contents have been removed, it is critical to make sure the displayed ads are directly related to the intents of the visitors who have been directed to the parked domains. Because of the missing contents in these domains, it is non-trivial to generate the keywords to describe the previous contents and therefore the users intents. In this paper we discuss the adaptive keywords extraction problem and introduce an algorithm based on the BM25F term weighting and linear multi-armed bandits. We built a prototype over a production domain registration system and evaluated it using crowdsourcing in multiple iterations. The prototype is compared with other popular methods and is shown to be more effective.
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。
