SIPR: Side-Information Pointwise Ranking Model for Scientific Research Project Query
作者单位：Beijing Key Laboratory of Intelligent Telecommunication Software and MultimediaSchool of Computer ScienceBeijing University of Posts and Telecommunications
会议名称：《2021中国智能自动化大会(CIAC2021)》
会议日期：2021年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Learning to rank Semantic matching Relevance matching Deep language model Machine learning
摘 要：Learning to rank has been applied to many web searches, but it cannot be directly applied to retrieval scientific research projects. In scientific research project query, people are not only concerned about the name of the project, but also the digital information and sideinformation, such as duration, achievements, funding amount of this project. Howerver, the existing learning to rank methods ignore the side-information of scientific research projects. Therefore, we propose a Side-information Pointwise Ranking model(SIPR) for scientific research project query based on deep language model, click model and learning to rank. First, we use the deep language model to extract the semantic information of text, and design a relevance calculation model to extract features of side-information, then we merge the above two features. After that we use the click model to eliminate position bias, and get a ranking score through pointwise DNN. Finally, we can get the query results ordered by these scores. Experiments on real scientific research projects dataset demonstrate that our model can achieve better performance.