基于网页查询结果的广告查询扩展研究

在计算广告学中,为用户查询返回相关的广告一直是研究的热点。然而用户的查询一般比较简短,广告的表示也局限在简短的创意和一些竞价词上,返回符合用户查询意图的广告十分困难。为了解决这个问题,该文提出利用多特征融合的方法进行广告查询扩展,先将查询输入到搜索引擎中,获得Top-k网页查询结果,将它们作为获取扩展词的外部资源,由于采用一般的特征选取方法获取扩展词采用的特征比较单一,缺乏语义信息,容易产生主题漂移现象,该文通过计算扩展词和查询词在网页查询结果中的共现度,并融合传统的TF特征和词性信息,获得与原始查询语义相关的扩展词。在真实的广告语料上的实验结果显示,基于多特征融合的选择广告扩展词的方法能有效地提高返回广告的相关性。

Query Expansion Based on Web Search Results for Sponsored Search

In the computational advertising, how to return more relevant ad results for web query is a fundamental issue. Due to short web queries and the short ads which contains 15-20 bid phrases on average for each ad, it is very difficult to return the relevant ads meeting the need of users. In this paper, we propose a query expansion approach based on feature fusion to solve the problem. We use web search results initially returned for the query to create a pool of relevant documents. To avoid the topic drift of the normal query expansion algorithms based on simple feature and lack of semantic information, we compute the co-occurrence of expansion term and query term in the web search results with the traditional feature of TF and part-of-speech information. The result got on the authentic ads dataset shows that the query expansion approach based on multi-fusion can return more relevant ads.
Key wordssponsored search; query expansion; term co-occurrence