mlsanityData to 7 Oct 2026

Personalized Language Model for Query Auto-Completion

arXiv 1804.09661 ↗First posted 25 Apr 2018cs.CL cs.IRcited by 25

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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.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Apr 2018.

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Citing papers per year: 2021: 4; 2022: 6; 2023: 6; 2024: 5; 2025: 2; 2026: 2.

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  1. 2023LaMP: When Large Language Models Meet Personalizationcited by 425
  2. 2024Optimization Methods for Personalizing Large Language Models through Retrieval Augmentationcited by 98
  3. 2022Unifying Data Perspectivism and Personalization: An Application to Social Normscited by 22
  4. 2024Retrieval Augmented End-to-End Spoken Dialog Modelscited by 20
  5. 2023Large Human Language Models: A Need and the Challengescited by 11
  6. 2025From Prompting to Alignment: A Generative Framework for Query Recommendationcited by 7
  7. 2022CUE Vectors: Modular Training of Language Models Conditioned on Diverse Contextual Signalscited by 5
  8. 2022Human Language Modelingcited by 5
  9. 2023Using External Off-Policy Speech-To-Text Mappings in Contextual End-To-End Automated Speech Recognitioncited by 5
  10. 2021Contextual Biasing of Language Models for Speech Recognition in Goal-Oriented Conversational Agentscited by 4
  11. 2021Attention-based Contextual Language Model Adaptation for Speech Recognitioncited by 4
  12. 2023Factual and Personalized Recommendations using Language Models and Reinforcement Learningcited by 4
13 more
  1. 2024Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?cited by 4
  2. 2021Deep Natural Language Processing for LinkedIn Searchcited by 3
  3. 2023PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Promptscited by 3
  4. 2022Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletioncited by 2
  5. 2022Exploring and Evaluating Personalized Models for Code Generationcited by 2
  6. 2022Fusing Sentence Embeddings Into LSTM-based Autoregressive Language Modelscited by 1
  7. 2024Search Intenion Network for Personalized Query Auto-Completion in E-Commercecited by 1
  8. 2025QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Enginescited by 1
  9. 2021Deep Natural Language Processing for LinkedIn Search Systemscited by 0
  10. 2023An Ensemble Approach to Personalized Real Time Predictive Writing for Expertscited by 0
  11. 2024An Efficient Self-Learning Framework For Interactive Spoken Dialog Systemscited by 0
  12. 2026Do Implicit Personalization and Explicit Styles Conflict? PsPLUG: A Lightweight Plug-in for Balancing Personalization and Style in Customized LLMscited by 0
  13. 2026Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignmentcited by 0