中文简介：本文对智能个人助理（如Google Now，Microsoft Cortana）中的信息卡片排序进行了研究，从user modeling的角度提出了三组排序特征：implicit feedback features, entity based user interests features以及user demographic features. 其中entity features的提取用到了word embedding和knowledge base信息进行语义相似度的计算。基于大规模产品日志数据的实验表明，提出的排序特征可以提升信息卡片的检索质量。
论文出处：ECIR'16.
英文摘要： Proactive search systems like Google Now and Microsoft Cortana have gained increasing popularity with the growth of mobile Internet. Unlike traditional reactive search systems where search engines return results in response to queries issued by the users, proactive systems actively push information cards to the users on mobile devices based on the context around time, location, environment (e.g., weather), and user interests. A proactive system is a zero-query in-formation retrieval system, which makes user modeling critical for understanding user information needs. In this paper, we study user modeling in proactive search systems and propose a learning to rank method for proactive ranking. We explore a variety of ways of modeling user interests, ranging from direct modeling of historical interaction with content types to finer-grained entity-level modeling, and user demographical information. To reduce the feature sparsity problem in entity modeling, we propose semantic similarity features using word embedding and an entity taxonomy in knowledge base. Experiments performed with data from a large commercial proactive search system show that our method significantly outperforms a strong baseline method deployed in the production system.
下载链接：https://www.microsoft.com/en-us/research/wp-content/uploads/2016/04/ECIR16-ProactiveRanking-cameraready01042016.pdf