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I have read blogpost about TensorFlow is being open sourced.
In the tutorials and the examples on the TensorFlow website, I see that they are mostly classification problems. (e.g. given an image, classify the number written in it)
I am curious about it the software also suitable for solving problems in recommendation system? For example, is it good for solving problems on collaborative filtering / content-based filtering?
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Collaborative filtering (CF) and its modifications is one of the most commonly used recommendation algorithms. Even data scientist beginners can use it to build their personal movie recommender system, for example, for a resume project.
Singular value decomposition also known as the SVD algorithm is used as a collaborative filtering method in recommendation systems.
DLRM is a DL-based model for recommendations introduced by Facebook research. It's designed to make use of both categorical and numerical inputs that are usually present in recommender system training data.
TensorFlow Recommenders (TFRS) is a library for building recommender system models. It helps with the full workflow of building a recommender system: data preparation, model formulation, training, evaluation, and deployment.
Tensorflow is great for deep learning, or training large neural nets. Although, it can be used for several other mathematical applications such as PDEs, various classifiers, recommendation systems etc, there doesn't seem to have a lot of support for them as yet.
This reddit thread might be a good place to start for searching libraries which are centred around recommendation systems.
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