Estimated Rating Based on Hours Played for Video Game Recommendation
Conference paper
First Online:
Abstract
This work presents a method to estimate ratings for video games based on the user’s playing hours. Based on these ratings, through collaborative filtering techniques, it is possible to make recommendations for video games without taking into account their popularity, solving the problem of long tail. The item-based k-NN algorithms and SVD++ are the ones that obtains the best results with the proposed estimation method, improving the original one and obtaining similar results in the rest of cases.
Keywords
Rating estimation Rating prediction Video games Collaborative filtering Recommender systemsNotes
Acknowledgements
This work was supported by the Spanish Ministry, Ministerio de Economía y Competitividad and FEDER funds. Project. SURF: Intelligent System for integrated and sustainable management of urban fleets TIN2015-65515-C4-3-R.
References
- 1.Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 263–272. IEEE, December 2008. http://ieeexplore.ieee.org/document/4781121/
- 2.Koren, Y., Research, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data 4(1) (2010). http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdfCrossRefGoogle Scholar
- 3.Koren, Y.: Factorization meets the neighborhood. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2008, p. 426. ACM Press, New York (2008). http://dl.acm.org/citation.cfm?doid=1401890.1401944
- 4.Lemire, D., Maclachlan, A.: Slope One Predictors for Online Rating-Based Collaborative Filtering, February 2007. http://arxiv.org/abs/cs/0702144
- 5.Pacula, M.: A matrix factorization algorithm for music recommendation using implicit user feedback (2009)Google Scholar
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