Personalization of Dataset Retrieval Results Using a Data Valuation Method

Malick Ebiele, Malika Bendechache, Eamonn Clinton, Rob Brennan

Abstract: In this paper, we propose a data valuation method that is used for Dataset Retrieval (DR) results re-ranking. Dataset retrieval is a specialization of Information Retrieval (IR) where instead of retrieving relevant documents, the information retrieval system returns a list of relevant datasets. To the best of our knowledge, data valuation has not yet been applied to dataset retrieval. By leveraging metadata and users’ preferences, we estimate the personal value of each dataset to facilitate dataset ranking and filtering. With two real users (stakeholders) and four simulated users (users’ preferences generated using a uniform weight distribution), we studied the user satisfaction rate. We define users’ satisfaction rate as the probability that users find the datasets they seek in the top k = {5,10} of the retrieval results. Previous studies of fairness in rankings (position bias) have shown that the probability or the exposure rate of a document drops exponentially from the top