Estimated Rating Based on Hours Played for Video Game Recommendation
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.
KeywordsRating estimation Rating prediction Video games Collaborative filtering Recommender systems
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.
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