Personalization of Dataset Retrieval Results using a Metadata-based Data Valuation Method

TL;DR

This paper introduces a new data valuation method based on metadata and user preferences to improve dataset retrieval, validated against stakeholder rankings, showing promising results in ranking accuracy.

Contribution

The paper presents the first application of data valuation to dataset retrieval, leveraging metadata and user preferences for personalized ranking.

Findings

Achieved 0.8207 NDCG@5 in dataset ranking

Validated data valuation approach against stakeholder rankings

Demonstrated potential of data valuation in dataset retrieval

Abstract

In this paper, we propose a novel data valuation method for a Dataset Retrieval (DR) use case in Ireland's National mapping agency. To the best of our knowledge, data valuation has not yet been applied to Dataset Retrieval. By leveraging metadata and a user's preferences, we estimate the personal value of each dataset to facilitate dataset retrieval and filtering. We then validated the data value-based ranking against the stakeholders' ranking of the datasets. The proposed data valuation method and use case demonstrated that data valuation is promising for dataset retrieval. For instance, the outperforming dataset retrieval based on our approach obtained 0.8207 in terms of NDCG@5 (the truncated Normalized Discounted Cumulative Gain at 5). This study is unique in its exploration of a data valuation-based approach to dataset retrieval and stands out because, unlike most existing methods, our approach is validated using the stakeholders ranking of the datasets.