Personalized Language Model for Query Auto-Completion

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 LLM was suggested as a method of generating query completions. We show how an adaptable LLM 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.