Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking

TL;DR

This paper introduces a contrastive learning approach with data augmentation strategies to create robust user behavior sequence representations, significantly improving context-aware document ranking performance.

Contribution

It proposes a novel contrastive learning framework with three data augmentation methods to better model variable user behavior sequences for document ranking.

Findings

Outperforms state-of-the-art methods on real query log datasets

Demonstrates robustness of user behavior representations

Enhances context-aware document ranking accuracy

Abstract

Context information in search sessions has proven to be useful for capturing user search intent. Existing studies explored user behavior sequences in sessions in different ways to enhance query suggestion or document ranking. However, a user behavior sequence has often been viewed as a definite and exact signal reflecting a user's behavior. In reality, it is highly variable: user's queries for the same intent can vary, and different documents can be clicked. To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences. Specifically, we propose three data augmentation strategies to generate similar variants of user behavior sequences and contrast them with other sequences. In so doing, the model is forced to be more robust regarding the possible variations. The optimized sequence representation is incorporated into document ranking. Experiments on two real query log datasets show that our proposed model outperforms the state-of-the-art methods significantly, which demonstrates the effectiveness of our method for context-aware document ranking.