A Machine Learning-Based Approach for Intelligent Game Recommendation System (I-GRS)
Abstract
Hybrid game recommendation system would resolve the cold started problem on most recommendations engines, especially in digital gaming domain. General recommendation methods (Especially collaborative filtration) need users to be well interacted, which is not always the case. The hybrid model is the combination of collaborative filtration and content-based filtration to get the precision and recall on both new user case scenario as well as the experienced one. This model combines SVD matrix factorization with a gradient descent optimization which is well suited for sparse data in the user-item interaction matrix. At the same time, natural language processing (NLP) techniques analyze game plot descriptions so that the model can detect thematic similarities between games. This research proposed hybrid method on a data set of 787 games and found its results more valid to that of classical collaborative filtration–the mean precision is 0.015960 and the mean recall is 0.158242 and it also improves user recommendations for users that have interacted with very few items. Through 100 case studies with extensive records of game reviews, testing shows the model can operate within a sweet spot of recommendation accuracy and computational performance by performing best at approximately between 75–100 iterations. The combiner model is also responsible for more specificity and sensitivity than the best world records reported in benchmark datasets. Since game content and user preference change over time the system is suitable for online deployment as it interacts with users in real-time. User feedback and up-to-date content will drive its model, ensuring sustained interest as things to talk about change over time. To test with a large user base that includes the in-content groups and thus better constitute potentially cold-start users, as well as applying hybrid recommendation system techniques for use cases with other sparsity challenges. This research highlights the importance of combining collaborative filtration and content-based approaches for better recommendations, higher user satisfaction, and engagement on digital platforms.
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