RecBole
pytorch_geometric_temporal
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| RecBole | pytorch_geometric_temporal | |
|---|---|---|
| 3 | 18 | |
| 3,150 | 2,470 | |
| 1.8% | - | |
| 8.4 | 2.6 | |
| 17 days ago | 15 days ago | |
| Python | Python | |
| MIT License | MIT License |
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RecBole
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Observe differences in the behavior of recommendation models using RecBole
RecBole seems to be a joint project started by the laboratories of Renmin University of China and Peking University, and it appeared on arxiv in November 2020. In August 2021, the module that we provide reached v1.0, and it seems to be used by various people in earnest.
pytorch_geometric_temporal
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Ask HN: ML Papers to Implement
I have done this a few times now. Alone (e.g. https://github.com/paulmorio/geo2dr) and in collaboration with others (e.g. https://github.com/benedekrozemberczki/pytorch_geometric_tem...) primarily as a way to learn about the methods I was interested in from a research perspective whilst improving my skills in software engineering. I am still learning.
Starting out I would recommend implementing fundamental building blocks within whatever 'subculture' of ML you are interested in whether that be DL, kernel methods, probabilistic models, etc.
Let's say you are interested in deep learning methods (as that's something I could at least speak more confidently about). In that case build yourself an MLP layer, then an RNN layer, then a GNN layer, then a CNN layer, and an attention layer along with some full models with those layers on some case studies exhibiting different data modalities (images, graphs, signals). This should give you a feel for the assumptions driving the inductive biases in each layer and what motivates their existence (vs. an MLP). It also gives you the all the building blocks you can then extend to build every other DL layer+model out there. Another reason is that these fundamental building blocks have been implemented many times so you have a reference to look to when you get stuck.
On that note: here are some fun GNN papers to implement in order of increasing difficulty (try building using vanilla PyTorch/Jax instead of PyG).
What are some alternatives?
implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
osmnx - OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
torchdrug - A powerful and flexible machine learning platform for drug discovery
PFoodReq - Code & data accompanying the WSDM 2021 paper "Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph"
RecSysDatasets - This is a repository of public data sources for Recommender Systems (RS).
gnn - TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.
graphein - Protein Graph Library
pytorch_geometric - Graph Neural Network Library for PyTorch
awesome-graph-classification - A collection of important graph embedding, classification and representation learning papers with implementations.