Learning to rank search results for time-sensitive queries

Abstract

Retrieval effectiveness of temporal queries can be improved by taking into account the time dimension. Existing temporal ranking models follow one of two main approaches: 1) a mixture model linearly combining textual similarity and temporal similarity, and 2) a probabilistic model generating a query from the textual and temporal part of document independently. In this paper, we propose a novel time-aware ranking model based on learning-to-rank techniques. We employ two classes of features for learning a ranking model, entity-based and temporal features, which are derived from annotation data. Entity-based features are aimed at capturing the semantic similarity between a query and a document, whereas temporal features measure the temporal similarity. Through extensive experiments we show that our ranking model significantly improves the retrieval effectiveness over existing time-aware ranking models.