This paper describes the approach of the THUIR team at the WSDM Cup 2023 Pre-training for Web Search task. This task requires the participant to rank the relevant documents for each query. We propose a new data pre-processing method and conduct pre-training and fine-tuning with the processed data. Moreover, we extract statistical, axiomatic, and semantic features to enhance the ranking performance. After the feature extraction, diverse learning-to-rank models are employed to merge those features. The experimental results show the superiority of our proposal. We finally achieve second place in this competition.
@article{arxiv.2303.04710,
title = {Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank},
author = {Haitao Li and Jia Chen and Weihang Su and Qingyao Ai and Yiqun Liu},
journal= {arXiv preprint arXiv:2303.04710},
year = {2023}
}