Improving Consumer Health Search with Field-Level Learning-to-Rank Techniques

Abstract

In the area of consumer health search (CHS), there is an increasing concern about returning topically relevant and understandable health information to the user. Besides being used to rank topically relevant documents, Learning to Rank (LTR) has also been used to promote understandability ranking. Traditionally, features coming from different document fields are joined together, limiting the performance of standard LTR, since field information plays an important role in promoting understandability ranking. In this paper, a novel field-level Learning-to-Rank (f-LTR) approach is proposed, and its application in CHS is investigated by developing thorough experiments on CLEF’ 2016–2018 eHealth IR data collections. An in-depth analysis of the effects of using f-LTR is provided, with experimental results suggesting that in LTR, title features are more effective than other field features in promoting understandability ranking. Moreover, the fused f-LTR model is compared to existing work, confirming the effectiveness of the methodology.