Abstract
World-wide research on recommender systems has resulted in great, highly effective algorithms based on a large variety of different concepts. Two of these promising recommender approaches are the Markov Random Walk and (higher-order) Factorization Machines. Unfortunately, due to the substantial effort for optimizing hyperparameters, most articles that describe new recommender approaches do not compare the obtained results with other state-of-the-art approaches in the recommender domain.
This paper demonstrates how different state-of-the-art recommender algorithms can be compared in a consistent manner. Furthermore, we investigate under which circumstances Factorization Machines should be preferred and in which situations Markov Random Walk is the most striking algorithm. In addition, we include the restart concept into a Markov Random Walk with an optimized walk length and show how the number of factors of each order in a higher-order Factorization Machine can be optimized.
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Knoll, J., Köckritz, D., Groß, R. (2017). Markov Random Walk vs. Higher-Order Factorization Machines: A Comparison of State-of-the-Art Recommender Algorithms. In: Eichler, G., Erfurth, C., Fahrnberger, G. (eds) Innovations for Community Services. I4CS 2017. Communications in Computer and Information Science, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-319-60447-3_7
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DOI: https://doi.org/10.1007/978-3-319-60447-3_7
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