https://github.com/myui/rtrec
An realtime recommendation system supporting online updates
https://github.com/myui/rtrec
factorization-machines machine-learning online-learning realtime recommendation recommendation-system recommender-system recsys
Last synced: 6 months ago
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An realtime recommendation system supporting online updates
- Host: GitHub
- URL: https://github.com/myui/rtrec
- Owner: myui
- License: bsd-3-clause
- Created: 2024-08-16T02:32:57.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-03T05:10:41.000Z (6 months ago)
- Last Synced: 2025-04-05T10:58:12.513Z (6 months ago)
- Topics: factorization-machines, machine-learning, online-learning, realtime, recommendation, recommendation-system, recommender-system, recsys
- Language: Python
- Homepage:
- Size: 378 KB
- Stars: 16
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
rtrec: Realtime Recommendation Library in Python
================================================
[](https://pypi.org/project/rtrec/)
[](https://pypi.org/project/rtrec/)
[](https://github.com/myui/rtrec/actions)
[](LICENSE.txt)
[](https://colab.research.google.com/github/myui/rtrec/blob/main/notebooks/quickstart_colab.ipynb)
A realtime recommendation system supporting online updates.
## Highlights
- ❇️ Supporting online updates.
- ⚡️ Fast implementation (>=190k samples/sec training on laptop).
- ◍ efficient sparse data support.
- 🕑 decaying weights of user-item interactions based on recency.
-  experimental [Rust implementation](https://github.com/myui/rtrec/tree/rust)
## Supported Recommendation Algorithims
- Sparse [SLIM](https://ieeexplore.ieee.org/document/6137254) with [time-weighted](https://dl.acm.org/doi/10.1145/1099554.1099689) interactions.
- [Factorization Machines](https://ieeexplore.ieee.org/document/5694074) using [LightFM](https://github.com/lyst/lightfm)
## Installation
```bash
pip install rtrec
```
## Usage
Find usages in [notebooks](https://github.com/myui/rtrec/tree/main/notebooks)/[examples](https://github.com/myui/rtrec/tree/main/examples).
### Examples using Raw-level APIs
```py
# Dataset consists of user, item, tstamp, rating
import time
current_unixtime = time.time()
interactions = [('user_1', 'item_1', current_unixtime, 5.0),
('user_2', 'item_2', current_unixtime, -2.0),
('user_2', 'item_1', current_unixtime, 3.0),
('user_2', 'item_4', current_unixtime, 3.0),
('user_1', 'item_3', current_unixtime, 4.0)]
# Fit SLIM model
from rtrec.models import SLIM
model = SLIM()
model.fit(interactions)
# can fit from streams using yield as follows:
def yield_interactions():
for interaction in interactions:
yield interaction
model.fit(yield_interactions())
# Recommend top-5 items for a user
recommendations = model.recommend('user_1', top_k=5)
assert recommendations == ["item_4", "item_2"]
```
### Examples using high level DataFrame APIs
```py
# load dataset
from rtrec.experiments.datasets import load_dataset
df = load_dataset(name='movielens_1m')
# Split data set by temporal user split
from rtrec.experiments.split import temporal_user_split
train_df, test_df = temporal_user_split(df)
# Initialize SLIM model with custom options
from rtrec.recommender import Recommender
from rtrec.models import SLIM
model = SLIM(min_value=0, max_value=15, decay_in_days=180, nn_feature_selection=50)
recommender = Recommender(model)
# Bulk fit
recommender.bulk_fit(train_df)
# Partial fit
from rtrec.experiments.split import temporal_split
test_df1, test_df2 = temporal_split(test_df, test_frac=0.5)
recommender.fit(test_df1, update_interaction=True, parallel=True)
# Evaluation
metrics = recommender.evaluate(test_df2, recommend_size=10, filter_interacted=True)
print(metrics)
# User to Item Recommendation
recommended = recommender.recommend(user=10, top_k=10, filter_interacted=True)
assert len(recommended) == 10
# Item to Item recommendation
similar_items = recommender.similar_items(query_items=[3,10], top_k=5)
assert len(similar_items) == 2
```