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Unverified Commit 73123c95 authored by darrylong's avatar darrylong Committed by GitHub
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Update Readme to include Cornac-AB section (#638)


* Add Cornac-AB section to Readme.md

* Update README.md

* Update assets directory and downsized images

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Co-authored-by: default avatarTuan Truong <tqtg@users.noreply.github.com>
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......@@ -63,7 +63,7 @@ brew install gcc | brew link gcc
## Getting started: your first Cornac experiment
![](flow.jpg)
![](assets/flow.jpg)
<p align="center"><i>Flow of an Experiment in Cornac</i></p>
```python
......@@ -125,6 +125,14 @@ $ curl -X GET "http://localhost:8080/recommend?uid=63&k=5&remove_seen=false"
```
If we want to remove seen items during training, we need to provide `TRAIN_SET` which has been saved with the model earlier, when starting the serving app. We can also leverage [WSGI](https://flask.palletsprojects.com/en/3.0.x/deploying/) server for model deployment in production. Please refer to [this](https://cornac.readthedocs.io/en/stable/user/iamadeveloper.html#running-an-api-service) guide for more details.
## Model A/B testing
[Cornac-AB](https://github.com/preferredAI/cornac-ab) is an extension of Cornac using the Cornac Serving API. Easily create and manage A/B testing experiments to further understand your model performance with online users.
| User Interaction Solution | Recommendations Dashboard | Feedback Dashboard |
|:------------------------:|:------------------------:|:------------------:|
| <img src="assets/demo.png" alt="demo" width="250"/> | <img src="assets/recommendation-dashboard.png" alt="recommendations" width="250"/> | <img src="assets/feedback-dashboard.png" alt="feedback" width="250"/> |
## Efficient retrieval with ANN search
One important aspect of deploying recommender model is efficient retrieval via Approximate Nearest Neighbor (ANN) search in vector space. Cornac integrates several vector similarity search frameworks for the ease of deployment. [This example](tutorials/ann_hnswlib.ipynb) demonstrates how ANN search will work seamlessly with any recommender models supporting it (e.g., matrix factorization).
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