books2rec
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A recommender system built for book lovers.
.npy files required in the data load aren't generated during runtime
We want to try and find ways of having information (ratings and book features) on newer books. Must be obtained legally.
Very cool project, I was happy to stumble upon it. I've been using Goodreads for many years, and always been disappointed by the recommendations - I don't think I've read...
I think it would be useful if we want to create an mobile app or if anyone wants integrate recommendations from books2rec to his/her website.
[hpfrec](https://github.com/david-cortes/hpfrec) uses Poisson matrix factorization for recommendations. We want to see if it works better than our SVD.
We believe that the genre of a recommended book is very important to whether or not a user will like a book. I want to quantify this idea. Here are...
We need to implement [Mean Reciprocal Rank](https://en.wikipedia.org/wiki/Mean_reciprocal_rank) in order to compare our results to [literally the only other paper to use Goodbooks-10k](https://arxiv.org/pdf/1711.08379.pdf). It seems to be very straightforward to implement.
We should add [unit tests](https://docs.python.org/3/library/unittest.html) wherever possible, and set up Travis CI.