Personalized Game Recommendation Method Based on Implicit Feedback

doi:10.16182/j.issn1004731x.joss.19-0636

Abstract

Abstract: Traditional recommendation systems often use explicit feedback for personalized recommendations. But the explicit feedback data is not easy to obtain, and the quality is poor, and the recommendation results unable to meet the requitrment of the user. Implicit feedback data is easier to obtain and can provide users with the better content. A personalized game recommendation method based on implicit feedback data is proposed. The method builds an implicit feedback recommendation model for game user data based on implicit feedback data such as the game duration and game numbers. A personalized recommendation of the game is implemented through an implicit semantic recommendation algorithm. Through comparative experiments on a large number of real data sets, it is shown that the accuracy and recall of the proposed method are better than other methods.

Key words: recommended system, implicit feedback, LFM(Latent Factor Model), personalized recommendations

CLC Number:

Sha Jing, Zeng Gongli, Yang Yang, Wei Yao. Personalized Game Recommendation Method Based on Implicit Feedback[J]. Journal of System Simulation, 2021, 33(4): 809-817.

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