# Personalized Language Model for Query Auto-Completion

arXiv: https://arxiv.org/abs/1804.09661 · HTML: https://mlsanity.com/paper/1804.09661/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 25 of 25)

- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation](https://mlsanity.com/paper/2404.05970/) (2024)
- [Unifying Data Perspectivism and Personalization: An Application to Social Norms](https://mlsanity.com/paper/2210.14531/) (2022)
- [Retrieval Augmented End-to-End Spoken Dialog Models](https://mlsanity.com/paper/2402.01828/) (2024)
- [Large Human Language Models: A Need and the Challenges](https://mlsanity.com/paper/2312.07751/) (2023)
- [From Prompting to Alignment: A Generative Framework for Query Recommendation](https://mlsanity.com/paper/2504.10208/) (2025)
- [CUE Vectors: Modular Training of Language Models Conditioned on Diverse Contextual Signals](https://mlsanity.com/paper/2203.08774/) (2022)
- [Human Language Modeling](https://mlsanity.com/paper/2205.05128/) (2022)
- [Using External Off-Policy Speech-To-Text Mappings in Contextual End-To-End Automated Speech Recognition](https://mlsanity.com/paper/2301.02736/) (2023)
- [Contextual Biasing of Language Models for Speech Recognition in Goal-Oriented Conversational Agents](https://mlsanity.com/paper/2103.10325/) (2021)
- [Attention-based Contextual Language Model Adaptation for Speech Recognition](https://mlsanity.com/paper/2106.01451/) (2021)
- [Factual and Personalized Recommendations using Language Models and Reinforcement Learning](https://mlsanity.com/paper/2310.06176/) (2023)
- [Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?](https://mlsanity.com/paper/2401.12492/) (2024)
- [Deep Natural Language Processing for LinkedIn Search](https://mlsanity.com/paper/2108.13300/) (2021)
- [PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Prompts](https://mlsanity.com/paper/2309.07727/) (2023)
- [Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion](https://mlsanity.com/paper/2204.10936/) (2022)
- [Exploring and Evaluating Personalized Models for Code Generation](https://mlsanity.com/paper/2208.13928/) (2022)
- [Fusing Sentence Embeddings Into LSTM-based Autoregressive Language Models](https://mlsanity.com/paper/2208.02402/) (2022)
- [Search Intenion Network for Personalized Query Auto-Completion in E-Commerce](https://mlsanity.com/paper/2403.02609/) (2024)
- [QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines](https://mlsanity.com/paper/2505.07345/) (2025)
- [Deep Natural Language Processing for LinkedIn Search Systems](https://mlsanity.com/paper/2108.08252/) (2021)
- [An Ensemble Approach to Personalized Real Time Predictive Writing for Experts](https://mlsanity.com/paper/2308.13576/) (2023)
- [An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems](https://mlsanity.com/paper/2409.10515/) (2024)
- [Do Implicit Personalization and Explicit Styles Conflict? PsPLUG: A Lightweight Plug-in for Balancing Personalization and Style in Customized LLMs](https://mlsanity.com/paper/2601.06362/) (2026)
- [Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment](https://mlsanity.com/paper/2602.01023/) (2026)

## Abstract (arXiv)

Query auto-completion is a search engine feature whereby the system suggests completed queries as the user types. Recently, the use of a recurrent neural network language model was suggested as a method of generating query completions. We show how an adaptable language model can be used to generate personalized completions and how the model can use online updating to make predictions for users not seen during training. The personalized predictions are significantly better than a baseline that uses no user information.