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Personalized Delivery of On-Line Search Advertisement Based on User Interests

机译：基于用户兴趣的个性化在线搜索广告投放

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摘要

Search engine advertising has become the main stream of online advertising system. In current search engine advertising systems, all users will get the same advertisement rank if they use the same query. However, different users may have different degree of interest to each advertisement even though they query the same word. In other words, users prefer to click the interested ad by themselves. For this reason, it is important to be able to accurately estimate the interests of individual users and schedule the advertisements with respect to individual users' favorites. For users that have rich history queries, their interests can be evaluated using their query logs. For new users, interests are calculated by summarizing the interests of other users who use similar queries. In this paper, we provide a model to automatically learn individual user's interests based on features of user history queries, user history views of advertisements, user history clicks of advertisements. Then, advertisement schedule is performed according to individual user's interests in order to raise the clickthrough rate of search engine advertisements in response to each user's query. We simulate user's interests of ads and clicks in our experiments. As a result, our personalized ranking scheme of delivering online ads can increase both search engine revenues and users' satisfactions.
机译：搜索引擎广告已成为在线广告系统的主流。在当前的搜索引擎广告系统中，如果所有用户使用相同的查询，他们将获得相同的广告评级。但是，即使不同的用户查询相同的单词，他们对每个广告的兴趣程度也可能不同。换句话说，用户喜欢自己点击感兴趣的广告。因此，重要的是能够准确地估计单个用户的兴趣并针对单个用户的收藏夹安排广告。对于具有丰富历史记录查询的用户，可以使用其查询日志评估其兴趣。对于新用户，兴趣是通过汇总使用类似查询的其他用户的兴趣来计算的。在本文中，我们提供了一个基于用户历史查询的功能，广告的用户历史视图，广告的用户历史点击次数自动学习单个用户兴趣的模型。然后，根据各个用户的兴趣来执行广告调度，以便响应于每个用户的查询来提高搜索引擎广告的点击率。我们在实验中模拟用户对广告和点击的兴趣。因此，我们提供在线广告的个性化排名方案可以提高搜索引擎收入和用户满意度。

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