Learning-based time-sensitive re-ranking for web search

Po-Tzu Chang, Yen-Chieh Huang, Cheng-Lun Yang, Shou-De Lin, Pu-Jen Cheng

Abstract: To model time-dependent user intent for Web search, this paper proposes a novel method using machine learning techniques to exploit temporal features for effective time-sensitive search result re-ranking. We propose models to incorporate users' click through information for queries that are seen in the training data, and then further extend the model to deal with unseen queries considering the relationship between queries. Experiment shows significant improvement on search result ranking over original search outputs.