Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank

2023/02/28 by Haitao Li, Li, Haitao, Jia Chen +7 · 2 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Text and Document Classification Technologies

paper · pdf · doi:10.48550/arxiv.2303.04710

openalex publication_date 2023/02/28 · openalex created_date 2023/03/10 · openalex updated_date 2026/07/28

Abstract

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.

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