wehub-resource-sync
69 lines
2.9 KiB
Markdown
69 lines
2.9 KiB
Markdown
# PinSAGE example
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## Requirements
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- dask
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- pandas
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- torchtext>=0.9.0
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## Prepare datasets
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### MovieLens 1M
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1. Download and extract the MovieLens-1M dataset from http://files.grouplens.org/datasets/movielens/ml-1m.zip
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into the current directory.
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2. Run `python process_movielens1m.py ./ml-1m ./data_processed`.
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Replace `ml-1m` with the directory you put the `.dat` files, and replace `data_processed` with
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any path you wish to put the output files.
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### Nowplaying-rs
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1. Download and extract the Nowplaying-rs dataset from https://zenodo.org/record/3248543/files/nowplayingrs.zip?download=1
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into the current directory.
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2. Run `python process_nowplaying_rs.py ./nowplaying_rs_dataset ./data_processed`
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## Run model
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### Nearest-neighbor recommendation
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This model returns items that are K nearest neighbors of the latest item the user has
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interacted. The distance between two items are measured by Euclidean distance of
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item embeddings, which are learned as outputs of PinSAGE.
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```
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python model.py data_processed --num-epochs 300 --num-workers 2 --device cuda:0 --hidden-dims 64
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```
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The implementation here also assigns a learnable vector to each item. If your hidden
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state size is so large that the learnable vectors cannot fit into GPU, use this script
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for sparse embedding update (written with `torch.optim.SparseAdam`) instead:
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```
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python model_sparse.py data_processed --num-epochs 300 --num-workers 2 --device cuda:0 --hidden-dims 1024
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```
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Note that since the embedding update is done on CPU, it will be significantly slower than doing
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everything on GPU.
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The HITS@10 is 0.01241, compared to 0.01220 with SLIM with the same dimensionality.\
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## Difference from the paper
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The implementation here is different from what being described in the paper:
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1. The paper described a supervised setting where the authors have a ground truth set of which items are
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relevant. However, in traditional recommender system datasets we don't have such labels other than
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which items are interacted by which users (as well as the user/item's own features). Therefore, I
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adapted PinSAGE to an unsupervised setting where I predict whether two items are cointeracted by the
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same user.
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2. PinSAGE paper explicitly stated that the items do not learnable embeddings of nodes, but directly
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express the embeddings as a function of node features. While this is reasonable for rich datasets like
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Pinterest's where images and texts are rich enough to distinguish the items from each other, it is
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unfortunately not the case for traditional recommender system datasets like MovieLens or Nowplaying-RS
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where we only have a bunch of categorical or numeric variables. I found adding a learnable embedding
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for each item still helpful for those datasets.
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3. The PinSAGE paper directly pass the GNN output to an MLP and make the result the final item
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representation. Here, I'm adding the GNN output with the node's own learnable embedding as
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the final item representation instead.
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