IR, NLP, Deep Learning
Title of Thesis
M.Sc. Thesis: Personalizing Query Auto-Completion system using a re-ranker
Description of Thesis
We proposed a re-ranker system that considers a K-best list including candidate queries and their scores as inputs and outputs a re-ranked list. The inputs were provided from a basic QAC system called MPC (Most Popular Completion) and a personalized language model for QAC. The re-ranked list was generated using time-aware features by applying a learning-to-rank algorithm.