RippleNet

This repository is a PyTorch implementation of RippleNet (arXiv):

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo
The 27th ACM International Conference on Information and Knowledge Management (CIKM 2018)

For the authors' official TensorFlow implementation, see hwwang55/RippleNet.

RippleNet is a deep end-to-end model that naturally incorporates the knowledge graph into recommender systems. Ripple Network overcomes the limitations of existing embedding-based and path-based KG-aware recommendation methods by introducing preference propagation, which automatically propagates users' potential preferences and explores their hierarchical interests in the KG.

Usage

Train model

python preprocess_ml-100k.py # ouput: ratings_final.txt, kg_final.txt
python train.py

Eval model

python evaluate.py

Tensorboard

tensorboard --logdir=experiments/base_model # port: 6006

Dataset

Movielens 100k

• link: https://drive.google.com/drive/folders/1OkDVEqetvOrtbuWebxl4y1JlZ_YjjfWj
• Dataset source: Movielens 100k
• Knoweldge graph: freebase subgraph

Info

• *.inter file:

user_id	item_id	rating	timestamp
196	242	3	881250949
186	302	3	891717742
22	377	1	878887116
244	51	2	880606923

• *.link

item_id	entity_id
476	m.08gjyx
362	m.035_kw
1612	m.0fwd14
1181	m.0gksh95

• *.item

item_id	movie_title	release_year	class
1	Toy Story	1995	Animation Children's Comedy
2	GoldenEye	1995	Action Adventure Thriller
3	Four Rooms	1995	Thriller
4	Get Shorty	1995	Action Comedy Drama

• *.user

user_id	age	gender	occupation	zip_code
1	24	M	technician	85711
2	53	F	other	94043
3	23	M	writer	32067
4	24	M	technician	43537

• *.kg

head_id	relation_id	tail_id
m.04ctbw8	film.producer.film	m.0bln8
m.0c3wmn	film.film.actor	m.02vxxgs
m.04t36	film.film_genre...	m.05sbv3
m.08jl3y	film.film.actor	m.0v187kf

• Detail

	Movielens 10k
#Users	944
#Items	1683
#Interactions	100,000
#Triplets	91631
#Entities	34713
#Relations	26
Rating	[1-5]

Movielens 1M

• link: https://github.com/hwwang55/RippleNet
• Dataset source: Movielens 1M
• Knoweldge graph: Mircosoft Satori

Info

• Training Dataset: 452253, Eval Dataset: 150740, Test Dataset: 150737

• number of users: 6036

• number of items: 2445

• number of entities (containing items): 182011

• number of relations: 12

• Detail

	Movielens 1M
#Users	6,040
#Items	3,629
#Interactions	836,478
#Triplets	91631
#Entities	34713
#Relations	26
Rating	[1-5]

Result

MovieLens 1M:

	Train AUC	Train ACC	Eval AUC	Eval ACC	Test AUC	Test ACC
Epoch 5	0.9533	0.8836	0.9216	0.8466	0.9206	0.8457

• use origin

Eval: train auc: 0.5092 acc: 0.5002 eval auc: 0.4851 acc: 0.5015 test auc: 0.4869 acc: 0.4977

MovieLens 100k

	Train AUC	Train ACC	Eval AUC	Eval ACC	Test AUC	Test ACC
Epoch 6	0.9604	0.8931	0.8997	0.8248	0.9006	0.8250

Required packages

The code has been tested running under Python 3.6, with the following packages installed (along with their dependencies):

• tqdm
• logging
• pytorch >= 1.0
• numpy >= 1.14.5
• sklearn >= 0.19.1

Relative link

• RecBole: https://github.com/RUCAIBox/RecBole
• RecSysDataset: https://github.com/RUCAIBox/RecSysDatasets
• KGAT: https://github.com/kangxiatao/KGAT-pytorch-master
• KB4Rec: https://github.com/RUCDM/KB4Rec