Prediction Model for Streaming Platform User Recommendation System Based on Collaborative Learning
Keywords:
Recommendation System, Collaborative Learning, Deep Learning, User Profile, Content StreamingAbstract
The rise of content overload on the internet has brought us a new issue for both consumers and service providers. To reduce the quantity of material shown, such as movies, music, or other items, Netflix and Amazon utilise recommender algorithms and user profiling, which try to direct the user through the available information. These technologies amass information about the user in order to provide customised experiences. The majority of current recommender systems use a content-focused approach, yet they often miss the nature of consumers' demands. This paper presents a hybrid approach to improving user profiling of content streaming platforms in order to improve user experience. In this paper, user experience was improved by hybridising the model with a similarity score and by hybridising the collaborative learning of the CNN model with latent based matrix factorization. The result was evaluated based on different latent sizes and also validated using 10-fold cross-validation. The result shows its superiority with respect to existing state-of-the-art models.
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