A Hybrid Recommendation Approach For Open Research Datasets

Open data initiatives and policies have triggered a dramatic increase in the volume of available research data. This, in turn, has brought to the fore the challenge of helping users to discover relevant datasets. Research data repositories support data search primarily through keyword search and faceted navigation. However, these mechanisms may suit users, who are familiar with the structure and terminology of the repository. This raises the problem of personalized dataset recommendations for users unfamiliar with the repository or not able to clearly articulate their information needs. To this end, we present and evaluate in this paper a recommendation approach applied to a new task - recommending research datasets. Our approach hybridizes content-based similarity with item-to-item co-occurrence, tuned to a feature weighting model obtained through a survey involving real users. We applied the approach in the context of a live research data repository and evaluated it in a user study. The obtained user judgments reveal the ability of the proposed approach to accurately quantify the relevance of datasets and they constitute an important step towards developing a practical dataset recommender.