Classifying User Search Intents for Query Auto-Completion

The function of query auto-completion in modern search engines is to help users formulate queries fast and precisely. Conventional context-aware methods primarily rank candidate queries according to term- and query- relationships to the context. However, most sessions are extremely short. How to capture search intents with such relationships becomes difficult when the context generally contains only few queries. In this paper, we investigate the feasibility of discovering search intents within short context for query auto-completion. The class distribution of the search session (i.e., issued queries and click behavior) is derived as search intents. Several distribution-based features are proposed to estimate the proximity between candidates and search intents. Finally, we apply learning-to-rank to predict the user's intended query according to these features. Moreover, we also design an ensemble model to combine the benefits of our proposed features and term-based conventional approaches. Extensive experiments have been conducted on the publicly available AOL search engine log. The experimental results demonstrate that our approach significantly outperforms six competitive baselines. The performance of keystrokes is also evaluated in experiments. Furthermore, an in-depth analysis is made to justify the usability of search intent classification for query auto-completion.