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Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data
When running `fit()` with user features, I get the error: ``` python KeyError: 'the users in [user_features] do not match the users in [interactions]' ``` which has been reported previously....
RankFM has user and item features, and that is great. However, I have a use case with features that cannot be put into this nice form. Specifically, I have some...
I'm running the lib on a virtual server with 64gb RAM. My data consist of: 200k distinct interaction between users and item 52k x 11 user_feature matrix 2770 x 49...
I've been trying to install RankFM on Windows but haven't been able to do so. First, I tried installing cygwin to get the GNU Compiler Collection on Windows (now I...
Great code, thanks ! Plese help to understand 1 will it work for third order categorical features interaction ? 2 will it run on Windows computer ? 3 will it...
First of all, thank you for providing good code. But I would like to suggest changing the multiplier in the WARP part (line 269 in _rankfm.pyx) from multiplier = log((I...
`item_features_train = pd.get_dummies(train_interactions[['Items', 'moment']], columns=['moment'])` I am classifying my items into fast, medium, slow moving items. for this I am using the parameter "item_features". It gives me this error: `model.fit(train_user_item,...
Hi, Thank you so much for this library. AFAIK it is the only FM lib with WARP loss. I was thinking of using it, and I was wondering whether you...
Is there a way to get the latent factor space representations of users and items?
thanks for your great library ... one thing i noticed that the model training doesn't utilize all available cpus in the machine, therefore training process is very slow for larger...