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

arXiv: https://arxiv.org/abs/2108.10510 · HTML: https://mlsanity.com/paper/2108.10510/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2021.

## Built on by (most cited first, 18 of 18)

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- [From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking](https://mlsanity.com/paper/2208.10226/) (2022)
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- [Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search](https://mlsanity.com/paper/2505.14156/) (2025)
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- [Domain Adaptation of Multilingual Semantic Search -- Literature Review](https://mlsanity.com/paper/2402.02932/) (2024)
- [Enhancing Multi-field B2B Cloud Solution Matching via Contrastive Pre-training](https://mlsanity.com/paper/2402.07076/) (2024)
- [Query-oriented Data Augmentation for Session Search](https://mlsanity.com/paper/2407.03720/) (2024)
- [Bridge the Gap between Past and Future: Siamese Model Optimization for Context-Aware Document Ranking](https://mlsanity.com/paper/2505.14180/) (2025)

## Abstract (arXiv)

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.