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A TensorFlow recommendation algorithm and framework in Python.
| Date | Stars |
|---|---|
| 2026-07-24 | 1300 |
| 2026-07-25 | 1300 |
| 2026-07-28 | 1300 |
| 2026-07-30 | 1300 |
| 2026-08-06 | 1300 |
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# TensorRec
A TensorFlow recommendation algorithm and framework in Python.
[](https://badge.fury.io/py/tensorrec) [](https://travis-ci.org/jfkirk/tensorrec) [](https://gitter.im/tensorrec)
## 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:
[TensorFlow Ranking](https://github.com/tensorflow/ranking/)
[Spotlight](https://github.com/maciejkula/spotlight)
[LightFM](https://github.com/lyst/lightfm)
## 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: `user_features`, `item_features`, and `interactions`. It uses this data to learn to make and rank recommendations.
For an overview of TensorRec and its usage, please see the [wiki.](https://github.com/jfkirk/tensorrec/wiki)
For more information, and for an outline of this project, please read [this blog post.](https://medium.com/@jameskirk1/tensorrec-a-recommendation-engine-framework-in-tensorflow-d85e4f0874e8)
For an introduction to building recommender systems, please see [these slides.](https://www.slideshare.net/JamesKirk58/boston-ml-architecting-recommender-systems)
### Example: Basic usage
```python
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))
```
## Quick Start
TensorRec can be installed via pip:
```pip install tensorrec```
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Tim Gates · IRESS · Australia
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:00a444f478fefb69, topic:tensorflow