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An example of the workflow to run entity2rec on the Movielens dataset.
Before starting:
mkdir datasets/movielens
mkdir datasets/movielens/graphs
Move your train and test files inside datasets/your_dataset:
mv train.dat datasets/movielens/train.dat
mv test.dat datasets/movielens/test.dat
(1): cut -f1,2 datasets/movielens/train.dat -d' ' > datasets/movielens/graphs/feedback.edgelist
The training and test set must have the format:
user_id item_id rating timestamp
where the user_id should be an integer, possibly preceded by the string user (i.e. 13 or user13).
(2): It can be done manually if using local data or querying a SPARQL endpoint using the --sparql argument (only the first time, as it will download locally the files)
(3) + (4): python src/entity2rec.py --dataset movielens --train datasets/movielens/training.dat --test datasets/movielens/test.dat --run_all
entity2rec accepts all the params of entity2vec and, in addition:
| option | default | description |
|---|---|---|
train |
null (Required) | Path of the train set |
test |
null (Required) | Path of the test set |
run_all |
false | If true, it runs entity2vec to compute the embeddings before the recommendation task (in this case, it is suggested to add also the related command line arguments (https://github.com/MultimediaSemantics/entity2vec)). Otherwise, it expects that the embeddings are in the emb/ folder. Note that this needs to be done only the first time. |
implicit |
false | If true, it expects that the ratings are binary values (0/1) instead of a range of scores |
As an output, entity2rec will generate a set of property-specific relatedness scores in the SVM format inside the folder features/my_dataset:
1 qid:1 1:0.186378 2:0.000000 3:0.329318 4:0.420169 5:0.000000 6:0.407551 7:0.000000 8:0.355113 9:0.198874 10:0.146273 11:0.354844 # http://dbpedia.org/resource/The_Secret_Garden_(1993_film)
(5): From ranking/ folder: java -jar RankLib-2.1-patched.jar -train ../features/movielens/p1_q1/train.svm -ranker 6 -metric2t P@10 -tvs 0.9 -test ../features/movielens/p1_q1/test.svm -norm sum
This command runs RankLib to learn the global relatedness model.