| dc.description.abstract | here are more than 30,000 games on the Steam platform. These numerous games make it difficult for users to choose games that match their own preferences. Therefore, user need to spend more time to find games that match their preferences. If we can develop an effective recommendation system, user can easily reach games which users are interested in and also attract user to purchase.
This study uses the steam platform dataset, combined with traditional user-based and model-based recommendation methods such as singular value decomposition (SVD) and non-negative matrix decomposition (NMF) method, designed and implement a selected recommender system based on computer game evaluation.
For the use of research, we pre-processing the original data set, combining into user scoring matrix. Then the scoring matrix is used to train the two models, and the predictions of the two models are combined and selected optimized by different degrees of criteria. The last combined predictions produces mixed recommendation results. In the first experiment, we propose a method of choosing the best combination between the two models, so as to recommend the user. In the second experiment, we improved the method of first experiment, improved the original method and standard, and found that the result of second experiment was not much different from the overall result of first experiment, but the method proposed in second experiment allows users to access games that have not been touched. | en_US |