Extracting structured data for Relevance Ranking

Presented by:
Tuan Nguyen Joe Zeimen Zachary Alexander
Abstract:

Learning to Rank (LTR) is the application of machine learning to rank search results according to their degree of relevance to the query. Salesforce Enterprise Search employs a hand-crafted ranking function to score search results and order them accordingly for users. Data about this ranking process are stored in JSON format, which is a nested tree with arbitrary depth. We present our first effort to parse this data, and extract the inputs of the ranking function into a tabular format. Next we present some of the potential questions and hypotheses we can ask with this data, and how we use machine learning algorithms to answer these questions.