Long short-term search session-based document re-ranking model

Abstract

Document re-ranking is a core task in session search. However, most existing methods only focus on the short-term session and ignore the long-term history sessions. This leads to inadequate understanding of the user’s search intent, which affects the performance of model re-ranking. At the same time, these methods have weaker capability in understanding user queries. In this paper, we propose a long short-term search session-based re-ranking model (LSSRM). Firstly, we utilize the BERT model to predict the topic relevance between the query and candidate documents, in order to improve the model’s understanding of user queries. Secondly, we initialize the reading vector with topic relevance and use the personalized memory encoder module to model the user’s long-term search intent. Thirdly, we input the user’s current session interaction sequence into Transformer to obtain the vector representation of the user’s short-term search intent. Finally, the user’s search intent and topical relevance information are hierarchically fused to obtain the final document ranking scores. Then re-rank the documents according to this score. We conduct extensive experiments on two real-world session datasets. The experimental results show that our method outperforms the baseline models for the document re-ranking task.