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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Evaluation of Algorithms for Interaction-Sparse Recommendations: Neural Networks don’t Always Win
Organization Unit
Authors
  • Yasamin Klingler
  • Claude Lehmann
  • João Pedro Monteiro
  • Carlo Saladin
  • Abraham Bernstein
  • Kurt Stockinger
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
ISBN 978-3-89318-086-8
Event Title Proceedings of the 25th International Conference on Extending Database Technology (EDBT)
Event Type conference
Event Location Edinburgh, UK
Event Start Date March 29 - 2022
Event End Date April 1 - 2022
Series Name OpenProceedings.org
Place of Publication OpenProceedings.org
Publisher OpenProceedings.org
Abstract Text In recent years, top-K recommender systems with implicit feed- back data gained interest in many real-world business scenarios. In particular, neural networks have shown promising results on these tasks. However, while traditional recommender systems are built on datasets with frequent user interactions, insurance recommenders often have access to a very limited amount of user interactions, as people only buy a few insurance products. In this paper, we shed new light on the problem of top-K recommendations for interaction-sparse recommender problems. In particular, we analyze six different recommender algorithms, namely a popularity-based baseline and compare it against two matrix factorization methods (SVD++, ALS), one neural network approach (JCA) and two combinations of neural network and factorization machine approaches (DeepFM, NeuFM). We evaluate these algorithms on six different interaction-sparse datasets and one dataset with a less sparse interaction pattern to elucidate the unique behavior of interaction-sparse datasets. In our experimental evaluation based on real-world insurance data, we demonstrate that DeepFM shows the best performance followed by JCA and SVD++, which indicates that neural network approaches are the dominant technologies. However, for the remaining five datasets we observe a different pattern. Overall, the matrix factorization method SVD++ is the winner. Surprisingly, the simple popularity-based approach comes out second followed by the neural network approach JCA. In summary, our experimental evaluation for interaction-sparse datasets demonstrates that in general matrix factorization methods outperform neural network approaches. As a consequence, traditional well- established methods should be part of the portfolio of algorithms to solve real-world interaction-sparse recommender problems.
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