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tfrs-movierec-serving

This repository contains movie recommendation systems using tensorflow-recommenders(tfrs) which is a library for building recommender system models using tensorflow.

• The code is based on Tensorflow and keras, but designed to be modular and scalable.
• For experiment management (training, parameter search, and storing model) we use 'Weight & Biases'.
• We use 'Annoy' to build an Approximate Nearest Neighbor (ANN) index to export for efficient candidate retrieval.
• The recommender systems are packaged as a REST API.
• This repository also contains a minimal implementation of local web server where users can rate recommended movies as well as check his/her movie history and information of those movies.

Setup

• If you don't have the repo in your box, open a terminal and run

git clone https://github.com/hojinYang/tfrs-movierec-serving.git
cd tfrs-movierec-serving

• To set up the Python environment, simply run pip install -r requirements.txt. I would recommend to use virtual environment(e.g. conda, venv) with python 3.7. If that line didn't work, you may need to manually install some of packages using pip or conda.
• Also, two main directories(webserver and recommenders) are needed to be add to PYTHONPATH, or or you will get errors like ModuleNotFoundError: No module named 'recommenders'. Run

PYTHONPATH= "your/dir/tfrs-movierec-serving/webserver:your/dir/tfrs-movierec-serving/recommenders:$PYTHONPATH"
export PYTHONPATH

before executing any commands later on, or add those lines to .bashrc if you want to add them permanently.

Recommenders

In general, the recommendation system consists of two stages: retrieval and ranking. (1) The retrieval phase serves to select an initial set of hundreds of candidates from all possible items. (2) The ranking model takes the output of the retrieval model and fine-tune it to increase recommendation quality. This repository contains tfrs-based retrieval and ranking models. We also use Annoy to build an Approximate Nearest Neighbor (ANN) index to export for efficient candidate retireval.

For now this repository contains MF network for retrieval and NCF-style network for ranking model, though you can define your custom networks in recommenders/networks. There are two warpper classes called Retireval model and Ranking model in recommenders/models. When training model, (custom) networks are plugged in these classes which support loss function and evaluation metric.

We use Weight & Bias (W&B) for experiment management. W&B is an experiment tracking tool that ensures you never lose track of your progress. Especially, W&B provides built in support for running sweeps, which enable automated trials of hyper-parameters. W&B is cloud-based system, thus you can easily share your logs with others online.

• Retrieval model W&B sweep tracking
• Ranking model W&B sweep tracking

To run retrieval sweep, run

cd recommenders
wandb sweep train/sweep_retrieval_model.yml
# wandb will give you a sweep id
wandb agent your/sweep/id

Please check out some tutorials on sign-up and sweep if you are not familiar with W&B. If you are interested in training model in parallel, check out ray which supports distributed training on top of W&B.

Web Server

This repository also contains a minimal web server implementation using Flask and SQLite. The database was initialized using 'movielens-latest-small', and IMDbPy is used to get additional information about the movie. This repository already has the database(app.db)), although you can initialize it yourself with movielens data by running python utils/add_movielens_to_db.py.

To start the server on your local host, run

cd webserver
flask run

You also need to run recommenders local api server to generate recommendations. Open another shell and run

cd recommenders
python api/api.py

User page	Movie page

Acknowledgement

We referred to the directory structure of full-stack deep-learning project repo when designing our recommenders library. We also borrowed a few utility functions for W&B sweep from fsdl repo.