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https://github.com/mvijaikumar/SoRecGAT
SoRecGAT implementation
https://github.com/mvijaikumar/SoRecGAT
Last synced: about 1 month ago
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SoRecGAT implementation
- Host: GitHub
- URL: https://github.com/mvijaikumar/SoRecGAT
- Owner: mvijaikumar
- Created: 2019-06-28T19:20:10.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-06-28T19:28:29.000Z (over 5 years ago)
- Last Synced: 2024-08-03T17:11:06.908Z (5 months ago)
- Language: Python
- Size: 8.06 MB
- Stars: 4
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation
This is our tensorflow implementation of the paper "SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation SoRecGAT implementation".
## Environment
tensorflow-gpu == 1.12.0
numpy == 1.16.0
Python == 2.7.12
scipy == 1.2.0
## Example to Run the Codes
### For Music dataset
> python Main.py --method sorecgatitem --path ../data/music/ --dataset music --res_path ../results/ --epochs 60 --batch_size 1024 --valid_batch_siz 256 --lr 0.0004 --initializer xavier --stddev 0.02 --optimizer rmsprop --loss ce --num_factors 64 --num_negatives 1 --keep_prob 0.5 --attn_keep 1.0 --ffd_keep 1.0 --proj_keep 1.0 --hid_units [32] --n_heads [12,6] --at_k 5 --num_thread 8
### For Art dataset
> python Main.py --method sorecgatuser --path ../data/art/ --dataset art --res_path ../results/ --epochs 60 --batch_size 1024 --valid_batch_siz 256 --lr 0.0001 --initializer xavier --stddev 0.02 --optimizer rmsprop --loss ce --num_factors 64 --num_negatives 1 --keep_prob 0.5 --attn_keep 1.0 --ffd_keep 1.0 --proj_keep 0.7 --hid_units [32] --n_heads [12,6] --at_k 5 --num_thread 8
## Dataset Description
### Music Dataset
- music.train
- Train file
- Each line contains UserID, ItemID, ImplicitRating and Some extra details (tab separated)
- music.valid
- Valid file
- Each line contains (UserID, ItemID, extra detail)::negative item list
- music.test
- Test file
- Each line contains (UserID, ItemID, extra detail)::negative item list (space separated)
- music.social
- Social interaction file
- Each line contains ItemID, SocialEntityID (tab separated)
- music.user_embed.final
- User embedding file
- Each line contains UserID::Initial User Embedding (space separated)
- music.item_embed.final
- Item embedding file
- Each line contains ItemID::Initial Item Embedding (space separated)
- music.socialitem_embed.final
- Social entity embedding file
- Each line contains SocialEntityID::Initial SocialEntity Embedding (space separated)
### Art Dataset
- art.train
- Train file
- Each line contains UserID, ItemID, ImplicitRating and some extra details (tab separated)
- art.valid
- Valid file
- Each line contains (UserID, ItemID, extra details)::negative item list (space separated)
- art.test
- Test file
- Each line contains (UserID, ItemID, extra details)::negative item list (space separated)
- art.social
- Social interaction file
- Each line contains UserID, SocialEntityID (tab separated)
- art.user_embed.final
- User embedding file
- Each line contains UserID::Initial User Embedding (space separated)
- art.item_embed.final
- Item embedding file
- Each line contains ItemID::Initial Item Embedding (space separated)
- art.socialitem_embed.final
- Social entity embedding file
- Each line contains SocialEntityID::Initial SocialEntity Embedding (space separated)
Link for all the datasets: https://www.dropbox.com/sh/3bkratvwuhgzctw/AABIA2GxPy4KZbmX0Do4S8b5a?dl=0