Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets
It addresses the under-researched problem of recommender systems for data markets, which is incremental as it adapts existing methods to a new domain.
This work tackles the problem of providing and evaluating recommendations in data markets by extending bipartite user-item models to tripartite relationships among users, datasets, and services, and finds that recommendation accuracy varies significantly across four identified use cases.
This work addresses the problem of providing and evaluating recommendations in data markets. Since most of the research in recommender systems is focused on the bipartite relationship between users and items (e.g., movies), we extend this view to the tripartite relationship between users, datasets and services, which is present in data markets. Between these entities, we identify four use cases for recommendations: (i) recommendation of datasets for users, (ii) recommendation of services for users, (iii) recommendation of services for datasets, and (iv) recommendation of datasets for services. Using the open Meta Kaggle dataset, we evaluate the recommendation accuracy of a popularity-based as well as a collaborative filtering-based algorithm for these four use cases and find that the recommendation accuracy strongly depends on the given use case. The presented work contributes to the tripartite recommendation problem in general and to the under-researched portfolio of evaluating recommender systems for data markets in particular.