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I came across these 2 papers which combined collaborative filtering (Matrix factorization) and Topic modeling (LDA) to recommend users similar articles/posts based on topic terms of posts/articles that users are interested in.

The papers (in PDF) are: "Collaborative Topic Modeling for Recommending Scientific Articles" and "Collaborative Topic Modeling for Recommending GitHub Repositories"

The new algorithm is called collaborative topic regression. I was hoping to find some python code that implemented this but to no avail. This might be a long shot but can someone show a simple python example?

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Collaborative topic modeling (CTM) model contents, it adjusts this model by viewing signals from readers, models reader preference and makes recommendations by the similarity between preference and content. This should get you started: https://github.com/arongdari/python-topic-model

For example:

class CollaborativeTopicModel:

    n_item: int

        number of items

    n_user: int

        number of users

    R: ndarray, shape (n_user, n_item)

        user x item rating matrix

Hope this answer helps.

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