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Hybrid system for video game recommendation based on implicit ratings and social networks

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Abstract

The digital entertainment sector is one of the fastest growing in recent years. In the case of video games, the productions of some of the most popular titles are on a par with film productions. The sale of video games is in the millions, and yet there are few works on the recommendation of video games. In this work a hybrid system of video game recommendation is presented, through the use of collaborative filtering and content-based filtering, and the construction of relationship graphs. In order to improve the recommendations, a new method for estimating implicit ratings is proposed that takes into account the hours of play. The proposed recommender system improves the results of other techniques presented in the state of the art.

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Notes

  1. https://movielens.org/.

  2. https://www.kaggle.com/tamber/steam-video-games.

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Acknowledgements

This research has been supported by the Department of Education of the Junta de Castilla y León (Spain) through the program of funding for research groups (ORDEN EDU/667/2019). Project code: SA064G19.

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Authors and Affiliations

  1. Department of Computer Science and Automatics, Universidad de Salamanca, Plaza de la Merced, s/n, 37008, Salamanca, Spain

    Javier Pérez-Marcos, Lucía Martín-Gómez, Diego M. Jiménez-Bravo, Vivian F. López & María N. Moreno-García

  2. Department of Computer Science, Universidad Pontificia de Salamanca, C/ Compañía, 5, 37002, Salamanca, Spain

    Javier Pérez-Marcos & Lucía Martín-Gómez

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  1. Javier Pérez-Marcos
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  2. Lucía Martín-Gómez
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  3. Diego M. Jiménez-Bravo
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  4. Vivian F. López
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  5. María N. Moreno-García
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Correspondence to Javier Pérez-Marcos.

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Pérez-Marcos, J., Martín-Gómez, L., Jiménez-Bravo, D.M. et al. Hybrid system for video game recommendation based on implicit ratings and social networks. J Ambient Intell Human Comput 11, 4525–4535 (2020). https://doi.org/10.1007/s12652-020-01681-0

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