Model-based recommendation systems

Memory-based recommendation systems are not always as fast and scalable as we would like them to be, especially in the context of actual systems that generate real-time recommendations on the basis of very large datasets. To achieve these goals, model-based recommendation systems are used.

Model-based recommendation systems involve building a model based on the dataset of ratings. In other words, we extract some information from the dataset, and use that as a "model" to make recommendations without having to use the complete dataset every time. This approach potentially offers the benefits of both speed and scalability.

Although the basic idea behind model-based recommendation systems is the same, there are a number of approaches that we can take to actually build the model and use it. Some examples are:




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