PyTorch中简单但灵活的推荐引擎_Jupyter Notebook_Python_下载.zip

Simple and Flexible Deep Recommenders in PyTorch ==============================  Simple but flexible Deep Recommenders in PyTorch. Plese review the deck to see the accompanying written & visual content.  [View the deck here](https://docs.google.com/presentation/d/1gv7osHoSX8CHf0uzKSqOlxmmAvPPdmstL0nrZHWiHQM/edit#slide=id.p) Check out the notebooks within to step through variations of matrix factorization models. Here's what we'll cover: 0. [Step 0] Introduction to autograd & deep learning using PyTorch, the Ignite library, and recommendation engines. 1. [Step 1] Build a simple matrix-factorization model in PyTorch. These models are a fundamental core to Netflix's, Pandora's, Stitch Fix's and Amazon's recommendations engines. 2. [Step 2] We'll expand on that model to include biases for extra predictive power 4. [Step 3] Add in "side" features, especially useful in coldstart cases 5. [Step 4] Model temporal effects which can track seasonal and periodic changes. 3. [Step 5] We'll take detour and see how word2vec is mathematically identical to recommendation engines 6. [Step 6] Upgrade the core of matrix factorization to Factorization Machines, which enables a huge number of interactions while keeping computation under control. 8. [Step 7] We'll wrap up with Bayesian Deep Learning applied to rec engines. This Variational Matrix Factorization is a great way to dip your toes into explore & exploit problems. 8. [Step 8] We'll build a real-time recommender using Transformers to read in an input ratings stream and generate recommendations. # To get started. If at all possible, please check out and pre-install the environment. ``` git clone https://github.com/cemoody/simple_mf.git cd simple_mf ``` ## 1. Create environment. Create the environment by following the steps below. If you choose to use your own environment, you'll need access to have the Python packages in `requirements.txt` installed. Make sure you you have pytorch installed; if not, follow the instructions [here](https://pytorch.org/get-started/locally/) ``` pip install pytorch-lightning optuna ``` Follow the directions the above command spits out. ## 2. Setup W & B account You'll be creating using a (free) weights & biases account to track model metrics and performance over time. TO kickstart that process: ``` pip install wandb wandb login ``` Setup your W&B account, then go to the W&B authorization page: https://app.wandb.ai/authorize and copy the auth code into your terminal when prompted by `wandb login` ## 3. Download and preprocess data: This will download and preprocess the MovieLens 1M dataset. We'll use this canonical dataset to test drive our code. ``` # required. will download the movielens 1M dataset. python src/download.py # optional! this is a bigger dataset we'll use for more # advanced models. python src/download_ml20.py # optional too. This is used for word2vec notebook. python src/skipgram.py ``` ## 4. Does it work? Open up and execute every line within the `01 MF model.ipynb` notebook. If it works, you're golden.