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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/P18-2111.pdf
- Code
- ajaech/query_completion + additional community code