Deep learning based query auto completion for recommendation system

In this study, Long Short-Term Memory based Query Auto-Completion (QAC) has been proposed to generate a query completion list using input prefix. The proposed LSTM based QAC system has been extensively tested using AOL and ORCAS datasets. The performance of the QAC system has been evaluated by using the relevancy score, and the quality of the QAC generation system has been evaluated by using partial and complete matching strategies, success rate, mean average precision and normalized discounted cumulative gain. According to the experimental results, the performance of the proposed QAC system more successful with the partial matching strategy. Also, the quality of the QAC generation list by the proposed QAC system is better on the complete matching strategy.