A Neural Language Model for Query Auto-Completion
Dae Hoon Park, Rikio Chiba · 2017
Query auto-completion (QAC) systems suggest queries that complete a user's text as the user types each character. Such queries are typically selected among previously stored queries, based on specific attributes such as popularity. However, queries cannot be suggested if a user's text does not match any queries in the storage. In order to suggest queries for previously unseen text, we propose a neural language model that learns how to generate a query from a starting text, a prefix. Specifically, we employ a recurrent neural network to handle prefixes in variable length. We perform the first neural language model experiments for QAC, and we evaluate the proposed methods with a public data set. Empirical results show that the proposed methods are as effective as traditional methods for previously seen queries and are superior to the state-of-the-art QAC method for previously unseen queries.