TensorRec alternatives and similar packages
Based on the "Recommender Systems" category.
Alternatively, view TensorRec alternatives based on common mentions on social networks and blogs.
9.1 7.3 TensorRec VS annoyApproximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
Do you think we are missing an alternative of TensorRec or a related project?
A TensorFlow recommendation algorithm and framework in Python.
NOTE: TensorRec is not under active development
TensorRec will not be receiving any more planned updates. Please feel free to open pull requests -- I am happy to review them.
Thank you for your contributions, support, and usage of TensorRec!
-James Kirk, @jfkirk
For similar tools, check out:
What is TensorRec?
TensorRec is a Python recommendation system that allows you to quickly develop recommendation algorithms and customize them using TensorFlow.
TensorRec lets you to customize your recommendation system's representation/embedding functions and loss functions while TensorRec handles the data manipulation, scoring, and ranking to generate recommendations.
A TensorRec system consumes three pieces of data:
interactions. It uses this data to learn to make and rank recommendations.
For an overview of TensorRec and its usage, please see the wiki.
For more information, and for an outline of this project, please read this blog post.
For an introduction to building recommender systems, please see these slides.
Example: Basic usage
import numpy as np import tensorrec # Build the model with default parameters model = tensorrec.TensorRec() # Generate some dummy data interactions, user_features, item_features = tensorrec.util.generate_dummy_data( num_users=100, num_items=150, interaction_density=.05 ) # Fit the model for 5 epochs model.fit(interactions, user_features, item_features, epochs=5, verbose=True) # Predict scores and ranks for all users and all items predictions = model.predict(user_features=user_features, item_features=item_features) predicted_ranks = model.predict_rank(user_features=user_features, item_features=item_features) # Calculate and print the recall at 10 r_at_k = tensorrec.eval.recall_at_k(predicted_ranks, interactions, k=10) print(np.mean(r_at_k))
TensorRec can be installed via pip:
pip install tensorrec