Personalized Language Model for Query Auto-Completion

Aaron Jaech, Mari Ostendorf


Abstract
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.
Anthology ID:
P18-2111
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
700–705
Language:
URL:
https://aclanthology.org/P18-2111
DOI:
10.18653/v1/P18-2111
Bibkey:
Cite (ACL):
Aaron Jaech and Mari Ostendorf. 2018. Personalized Language Model for Query Auto-Completion. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 700–705, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Personalized Language Model for Query Auto-Completion (Jaech & Ostendorf, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-2111.pdf
Presentation:
 P18-2111.Presentation.pdf
Video:
 https://aclanthology.org/P18-2111.mp4
Code
 ajaech/query_completion +  additional community code