Add preprocessed data
Browse files- Movies/nm/collab_emb.pt +3 -0
- Movies/nm/img_emb.pt +3 -0
- Movies/nm/mappings.pkl +3 -0
- Movies/nm/mod_presence.pt +3 -0
- Movies/nm/test.pkl +3 -0
- Movies/nm/train.pkl +3 -0
- Movies/nm/txt_emb.pt +3 -0
- Movies/nm/valid.pkl +3 -0
- README.md +37 -0
- SHA256SUMS.txt +8 -0
Movies/nm/collab_emb.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:3681d0d9ee52e0c41ae981b5d29d33b1309fcf20ec5ada9d32a367d637a61fd6
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size 22081707
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Movies/nm/img_emb.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:de2bc7b43636f50ea63f6fc1ba4cd336679af9e51162942b41bb6f28c0bc1039
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size 66242716
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Movies/nm/mappings.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:1819440f7e89f65254c7a90abf77e732c0a1f38baeee819d55a620986088b4e9
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size 344853
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Movies/nm/mod_presence.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:83320b915ed987d169d51b41b3c4981dca5246d0e8a62b8744b746465d64e785
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size 44277
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Movies/nm/test.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:f6d3a3842e080bad0907a1823a2483ef1fe4e38c6e526957e2a6db5514e15a1d
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size 2333097
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Movies/nm/train.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:c6b0dce411fbd8d0281bbb6dc10440a33ff106687c3537f821505e9867fbb690
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size 10196833
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Movies/nm/txt_emb.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:85bfe63ab71241964a3e78eb7cb3cad8c835268f46712d2cfdffb3406c0a8253
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size 66242716
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Movies/nm/valid.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:6de4148a296a02d52e76a95ab07324a10098323fc1a7806656d7135276236e91
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size 2895267
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README.md
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---
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language:
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- en
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tags:
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- recommendation
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- multimodal
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- sequential-recommendation
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---
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# MoSID preprocessed data
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Preprocessed item features and sequence splits for the Movies dataset in nm mode.
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## Layout
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The files under `Movies/nm/` use the same contiguous item IDs. ID 0 is reserved for padding; valid item IDs are 1 through N.
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| File | Content |
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| --- | --- |
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| txt_emb.pt | Text embedding tensor, shape [N+1, 768] |
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| img_emb.pt | Visual embedding tensor, shape [N+1, 768] |
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| collab_emb.pt | Collaborative embedding tensor, shape [N+1, 256] |
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| mod_presence.pt | Boolean tensor, shape [N+1, 2], for text and visual availability |
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| mappings.pkl | Mapping dictionary containing item2id |
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| train.pkl | Training item_seqs and labels |
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| valid.pkl | Validation item_seqs and labels |
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| test.pkl | Test item_seqs and labels |
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Place the `Movies` directory under `datasets/amazon/` in the MoSID source repository. Semantic IDs and codebook weights are generated by the Stage 1 training and export commands provided in the source repository.
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## Integrity
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`SHA256SUMS.txt` contains the SHA-256 hashes of the released data files.
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## Serialization
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The `.pt` files contain PyTorch tensors. The `.pkl` files use Python pickle. Only load serialized files from a trusted source.
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SHA256SUMS.txt
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85bfe63ab71241964a3e78eb7cb3cad8c835268f46712d2cfdffb3406c0a8253 Movies/nm/txt_emb.pt
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de2bc7b43636f50ea63f6fc1ba4cd336679af9e51162942b41bb6f28c0bc1039 Movies/nm/img_emb.pt
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3681d0d9ee52e0c41ae981b5d29d33b1309fcf20ec5ada9d32a367d637a61fd6 Movies/nm/collab_emb.pt
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83320b915ed987d169d51b41b3c4981dca5246d0e8a62b8744b746465d64e785 Movies/nm/mod_presence.pt
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1819440f7e89f65254c7a90abf77e732c0a1f38baeee819d55a620986088b4e9 Movies/nm/mappings.pkl
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c6b0dce411fbd8d0281bbb6dc10440a33ff106687c3537f821505e9867fbb690 Movies/nm/train.pkl
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6de4148a296a02d52e76a95ab07324a10098323fc1a7806656d7135276236e91 Movies/nm/valid.pkl
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f6d3a3842e080bad0907a1823a2483ef1fe4e38c6e526957e2a6db5514e15a1d Movies/nm/test.pkl
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