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Neural Attentive Personalization Model for Query Auto-Completion

Query auto-completion (QAC) is one of the most visible features in modern search engines. It helps users complete their queries by presenting a list of possible completions while they are typing in the search box. Existing works on QAC focus on employing learning-to-rank algorithms over handcrafted features. However, those manually designed features are unable to capture non-linear relationships between users and their submitted queries. Meanwhile, although Recurrent Neural Networks (RNNs) show significant advances in various areas, little attention is paid to its application to QAC. To bridge this gap, we propose three RNN-based models for QAC ranking: a simple session-based RNN model, a personalized RNN model and an attentive RNN model. Extensive experiments are conducted on a real-world query log. The significant improvement over the compared baseline verifies the effectiveness of the personalized RNN model and the attentive RNN model.