Do Users Matter? The Contribution of User-Driven Feature Weights to Open Dataset Recommendations

The vast volumes of open data pose a challenge for users in finding relevant datasets. To address this, we developed a hybrid dataset recommendation model that combines content-based similarity with item-to-item co-occurrence. The features used by the recommender include dataset properties and usage statistics. In this paper, we focus on fine-tuning the weights of these features. We experimentally compare two feature weighting approaches: a uniform one with predefined weights and a user-driven one, where the weights are informed by the opinions of system users. We evaluated the two approaches in a study, involving the users of a real-life data portal. The results suggest that user-driven feature weights can improve dataset recommendations, although not at all levels of data relevance, and highlight the importance of incorporating target users in the design of recommender systems.