RippleNet

This repository is the 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

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.

Files in the folder

• data/
  • book/
    • BX-Book-Ratings.csv: raw rating file of Book-Crossing dataset;
    • item_index2entity_id.txt: the mapping from item indices in the raw rating file to entity IDs in the KG;
    • kg.txt: knowledge graph file;
  • movie/
    • item_index2entity_id.txt: the mapping from item indices in the raw rating file to entity IDs in the KG;
    • kg_part1.txt and kg_part2.txt: knowledge graph file;
    • ratrings.dat: raw rating file of MovieLens-1M;
• src/: implementations of RippleNet.

Required packages

The code has been tested running under Python 3.6.5, with the following packages installed (along with their dependencies):

• tensorflow-gpu == 1.4.0
• numpy == 1.14.5
• sklearn == 0.19.1

Running the code

$ cd src
$ python preprocess.py --dataset movie (or --dataset book)
$ python main.py --dataset movie (note: use -h to check optional arguments)