Hi, I am Haritha Sreedharan Nair (github username- haritha1313). I would like to work on implementing better collaborative filtering models in mlpack as part of GSOC. I had worked on recommendation system based projects earlier and I see a lot of scope in mlpack's CF implementation.
As of now I have been through the research paper cited in mlpack/methods/cf and realized that some concepts mentioned in the paper (the ones explaining how to handle biases etc.) haven't been implemented yet. I have also explored a few other research papers and articles including the one mentioned in mlpack's ideas wiki. I found this (https://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf) paper interesting - it gives importance to implicit feedback, performs better than existing methods and it is able to generalize the matrix factorization methods we use in mlpack too. Since it is a pretty new research paper I am not able to find any discussions on it and would like to know if the maintainers find it to be worth implementing. I would also like to clarify a doubt. Is there any reason why quic_svd and randomized_svd have not been used for matrix factorization in CF? Thank you.
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