lightfm
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lyst
A Python implementation of LightFM, a hybrid recommendation algorithm.
May I ask how to resolve this ?? I recommended_score = self.model.predict(user_code, np.array(list_item_codes), user_features=user_features, item_features=item_features, num_threads=multiprocessing.cpu_count() ) --> 144 recommended_score = self.model.predict(user_code, 145 np.array(list_item_codes), 146 user_features=user_features, c:\Users\\Desktop\ENV\lib\site-packages\lightfm\lightfm.py in predict(self, user_ids,...
Due to the getting the same recommendations for almost all users, I trained the model without user_features as was suggested in [here](https://github.com/lyst/lightfm/issues/320#issuecomment-401329449) (~90 unique items are recommended). The model training...
Here is the result of predict of our dataset： ```python predictions = model1.predict(test_user_ids, test_item_ids, user_features=user_features_matrix) predictions is like array this：array([ -8.672923, -13.849941, -12.703363, ..., -14.409408, -9.897023, -11.761951], dtype=float32) ``` Here...
I've tried the libray in my own laptop and it works well, now I need to install it into development environment which has no internet connected, So I wonder if...
Hi I am trying to create a hybrid recommender using item's features I have a dataset with 478,930 interactions I am quite sure that each user-item pairs available only once...
Hello, Thanks for the great package! I am new to recommendation systems, and I wonder how could I model and specify negative implicit feedback using LightFM: - In my problem,...
I have a set of LightFM recommendations I have trained via WARP loss and just implicit feedback (user-item transaction history; no separate user-features or item-features yet). I have groups of...
Hello! I'm looking for a way for effective item recommendations for new users. I've read all created issues about LightFM (the most relevant existing question is https://github.com/lyst/lightfm/issues/347), but have not...
I have trained a hybrid model. What is the best way to generate predictions for every item-user pair and rank them? I know that `model.predict()` can be used. I also...
I have the following datasets: Users: 10.000 Rows. Features: User-Id, name, age, los, ou, gender, skills, language, grade, career interests Trainings: Training-Id, training name, main skill Trainings Taken User-Id, Training-Id,...
