SIPR: Side-Information Pointwise Ranking Model for Scientific Research Project Query

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 side-information, 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.