![]() The demonstration will illustrate how to get recommendations of movies for users that have previously watched and rated other movies. Using a Recommendation System is compelling because it provides value to users by narrowing the search of items to those that they could be more interested in. This section guides you through a demonstration of a Recommendation System. ![]() The diagram below illustrates the structure of the Sample Solution. The model is trained using the Movielens dataset, which is a dataset with movie information and ratings from users that is publicly available for non-commercial purposes and commonly used in tutorials and research projects. The problem is implemented as a classification problem and a Neural Network is trained by using movies watched and rated by users as positive examples, and unwatched movies as negative examples. The Deep Learning model is a Multilayer Perceptron as proposed in Neural Collaborative Filtering (He et al. TensorFlow and TensorRT are available as part of DAIR Cloud infrastructure and allow the development of medium- to large-scale, high performance deep learning models. ![]() Python is a widely used library in machine learning projects by both beginners and experienced practitioners. The solution includes the creation of a Deep Learning model developed in Python and TensorFlow and the deployment to a production-like environment leveraging NVIDIA TensorRT library and TensorRT Inference Server. A key advantage of the collaborative filtering approach is that it can rely only on observed user behaviour, without requiring extra information from the user or the product, making it easily transferable to different business applications where that information may not be available. Collaborative filtering methods are based on users’ behaviours, activities, or preferences and predict what users will like based on their similarity to other users. The solution consists of an end-to-end deep learning collaborative filtering recommendation system from user data.Ĭollaborative Filtering is a widely used approach to implement recommender systems. This Sample Solution showcases how TensorFlow and TensorRT can be used to build a Collaborative Filtering model for movie recommendations that runs on a GPU. Without a recommendation, the user experience is greatly degraded as they are forced to spend too much time searching for items of their interest and are more likely to abandon the task. Recommender Systems use information like users demographics, behaviour, product information, or product ratings provided by users to make predictions about what they will be most interested in at a particular time. When users have thousands, hundreds, or even just tens of options to choose from, it is essential that businesses provide tools to help them discover products of their interest quickly.Īutomatic recommendation systems powered by machine learning aim to solve this problem and have actually become an essential feature for content providers and retail sites. It happens when trying to choose a book, a TV show, a movie, a new electronic device, or even groceries. In today’s world, users have an overwhelming array of options to choose from in practically any online business they interact with.
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