| Popis: |
— By acquiring a deeper understanding of the user's preferences, recommendation systems are utilized to boost user appeal. These systems have grown in popularity as a result of their capacity to provide users with customized material that is relevant to the interests of them.The implementation of these systems can be done by collaborative filtering ,hybrid models,contenet-based, and also by neural networks. The goal of this research is to see if the machine learning approaches like Content-based filtering and Cosine similarity can be used to detect content similarities and suggest related movies. Cosine similarity and Euclidean distance are two ways to find similarity in genres.We have uses the many metrics that are used to compute similarity between products/movies in this study. |
