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

## TL;DR

This paper explores tripartite recommendations in data markets, extending beyond traditional bipartite models by evaluating algorithms on the Meta Kaggle dataset for four distinct recommendation use cases involving users, datasets, and services.

## Contribution

It introduces a tripartite recommendation framework for data markets and evaluates the effectiveness of popularity and collaborative filtering algorithms on this new setting.

## Key findings

- Recommendation accuracy varies significantly across use cases.
- Collaborative filtering outperforms popularity-based methods in certain scenarios.
- The work advances the understanding of tripartite recommendation systems in data markets.

## Abstract

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.