This repository is the implementation of KGCN and KGCN-LS.
data/
movie/
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;
music/
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;user_artists.dat
: raw rating file of Last.FM;
restaurant/
Dianping-Food.zip
: containing the final rating file and the final KG file;
src/
: implementations of KGCN and KGCN-LS.
- Movie
(The raw rating file of MovieLens-20M is too large to be contained in this repository. Download the dataset first.)$ wget http://files.grouplens.org/datasets/movielens/ml-20m.zip $ unzip ml-20m.zip $ mv ml-20m/ratings.csv data/movie/ $ cd src $ python preprocess.py --dataset movie $ python main.py (settings for KGCN and KGCN-LS are different. Plase carefully choose the corresponding code blocks in main.py)
- Music
-
$ cd src $ python preprocess.py --dataset music
-
open
src/main.py
file; -
comment the code blocks of parameter settings for MovieLens-20M;
-
uncomment the code blocks of parameter settings for Last.FM;
-
$ python main.py (settings for KGCN and KGCN-LS are different. Plase carefully choose the corresponding code blocks in main.py)
-
- Restaurant
$ cd data/restaurant $ unzip Dianping-Food.zip
-
open
src/main.py
file; -
comment the code blocks of parameter settings for MovieLens-20M;
-
uncomment the code blocks of parameter settings for Dianping-Food;
-
$ python main.py (settings for KGCN and KGCN-LS are different. Plase carefully choose the corresponding code blocks in main.py)
-