# Context Attentive Document Ranking and Query Suggestion

arXiv: https://arxiv.org/abs/1906.02329 · HTML: https://mlsanity.com/paper/1906.02329/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 15 of 15)

- [CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning](https://mlsanity.com/paper/2112.08558/) (2021)
- [Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion](https://mlsanity.com/paper/2311.06318/) (2023)
- [GERE: Generative Evidence Retrieval for Fact Verification](https://mlsanity.com/paper/2204.05511/) (2022)
- [RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph](https://mlsanity.com/paper/2202.06129/) (2022)
- [From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking](https://mlsanity.com/paper/2208.10226/) (2022)
- [Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow](https://mlsanity.com/paper/2102.00826/) (2021)
- [Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions](https://mlsanity.com/paper/2503.00501/) (2025)
- [Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search](https://mlsanity.com/paper/2505.14156/) (2025)
- [Query Augmentation by Decoding Semantics from Brain Signals](https://mlsanity.com/paper/2402.15708/) (2024)
- [TPRM: A Topic-based Personalized Ranking Model for Web Search](https://mlsanity.com/paper/2108.06014/) (2021)
- [Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs](https://mlsanity.com/paper/2112.04344/) (2021)
- [Improving Sequential Query Recommendation with Immediate User Feedback](https://mlsanity.com/paper/2205.06297/) (2022)
- [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)
- [Navigation-Informed Embeddings: Dense-Retriever Adaptation from Agent Search Traces](https://mlsanity.com/paper/2608.15956/) (2026)

## Abstract (arXiv)

We present a context-aware neural ranking model to exploit users' on-task search activities and enhance retrieval performance. In particular, a two-level hierarchical recurrent neural network is introduced to learn search context representation of individual queries, search tasks, and corresponding dependency structure by jointly optimizing two companion retrieval tasks: document ranking and query suggestion. To identify the variable dependency structure between search context and users' ongoing search activities, attention at both levels of recurrent states are introduced. Extensive experiment comparisons against a rich set of baseline methods and an in-depth ablation analysis confirm the value of our proposed approach for modeling search context buried in search tasks.