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This work addresses the problem of providing and evaluating recommendationsin data markets. Since most of the research in recommender systems is focusedon the bipartite relationship between users and items (e.g., movies), we extendthis view to the tripartite relationship between users, datasets and services,which is present in data markets. Between these entities, we identify four usecases for recommendations: (i) recommendation of datasets for users, (ii)recommendation of services for users, (iii) recommendation of services fordatasets, and (iv) recommendation of datasets for services. Using the open MetaKaggle dataset, we evaluate the recommendation accuracy of a popularity-basedas well as a collaborative filtering-based algorithm for these four use casesand find that the recommendation accuracy strongly depends on the given usecase. The presented work contributes to the tripartite recommendation problemin general and to the under-researched portfolio of evaluating recommendersystems for data markets in particular.

Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets