# Semantic Modelling with Long-Short-Term Memory for Information Retrieval

arXiv: https://arxiv.org/abs/1412.6629 · HTML: https://mlsanity.com/paper/1412.6629/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2014.

## Built on by (most cited first, 17 of 17)

- [Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting](https://mlsanity.com/paper/2408.09439/) (2024)
- [Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning](https://mlsanity.com/paper/2308.05379/) (2023)
- [Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce](https://mlsanity.com/paper/2407.09395/) (2024)
- [Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning](https://mlsanity.com/paper/2411.13045/) (2024)
- [A Survey on Awesome Korean NLP Datasets](https://mlsanity.com/paper/2112.01624/) (2021)
- [Robust Interaction-Based Relevance Modeling for Online e-Commerce Search](https://mlsanity.com/paper/2406.02135/) (2024)
- [Centrality-aware Product Retrieval and Ranking](https://mlsanity.com/paper/2410.15930/) (2024)
- [A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems](https://mlsanity.com/paper/2502.10284/) (2025)
- [Secure Your Ride: Real-time Matching Success Rate Prediction for Passenger-Driver Pairs](https://mlsanity.com/paper/2109.07571/) (2021)
- [Thinking Broad, Acting Fast: Latent Reasoning Distillation from Multi-Perspective Chain-of-Thought for E-Commerce Relevance](https://mlsanity.com/paper/2601.21611/) (2026)
- [Semantic Similarity Computing for Scientific Academic Conferences fused with domain features](https://mlsanity.com/paper/2203.12593/) (2022)
- [Effective and Efficient Query-aware Snippet Extraction for Web Search](https://mlsanity.com/paper/2210.08809/) (2022)
- [CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search](https://mlsanity.com/paper/2412.01269/) (2024)
- [Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences](https://mlsanity.com/paper/2204.04888/) (2022)
- [Address Matching Based On Hierarchical Information](https://mlsanity.com/paper/2305.05874/) (2023)
- [RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment](https://mlsanity.com/paper/2512.24943/) (2025)
- [Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems](https://mlsanity.com/paper/2603.03770/) (2026)

## Abstract (arXiv)

In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike common IR methods that use bag of words representation for queries and documents, we treat them as a sequence of words and use long short term memory (LSTM) to capture contextual dependencies. To the best of our knowledge, this is the first time that LSTM is applied to information retrieval tasks. Unlike training traditional LSTMs, the training strategy is different due to the special nature of information retrieval problem. Experimental evaluation on an IR task derived from the Bing web search demonstrates the ability of the proposed method in addressing both lexical mismatch and long-term context modelling issues, thereby, significantly outperforming existing state of the art methods for web document retrieval task.