# entity2rec
Implementation of the entity recommendation algorithm described in [entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation](https://enricopal.github.io/enricopal.github.io/publications/entity2rec.pdf). Compute user and item embeddings from a Knowledge Graph encompassing both user feedback information and item information. It is based on property-specific entity embeddings, which are obtained via entity2vec (https://github.com/MultimediaSemantics/entity2vec). Slides can be found on [Slideshare]( https://www.slideshare.net/EnricoPalumbo2/entity2rec-recsys).
The main difference between the current implementation and what is reported in the paper is the evaluation protocol, which now ranks all the items for each user, and the use of hybrid property-specific subgraphs to compute the property-specific knowledge graph embeddings.
The command:
`python entity2rec/main.py --dataset LibraryThing --run_all`
will run entity2rec on the LibraryThing dataset. The first time it will generate the embeddings files and save them into emb/LibraryThing/. Afterwards, it will check whether the embeddings files already exist in the folder. Then, it computes property-specific relatedness scores and evaluates recommendations using a set of possible aggregation functions (LambdaMart, average, max and min) on a set of relevant metrics.
The configuration of properties.json is used to select the properties. By default, it will use hybrid property-specific subgraphs (feedback + content property). To use collaborative-content subgraphs, the user should replace the content of config/properties.json with that of config/properties_collaborative_content_example.json.
## Requirements
- Python 2.7 or above
If you are using `pip`:
pip install -r requirements.txt
## Our Publications
* Palumbo E., Rizzo G., Troncy R. (2017) [entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation](https://enricopal.github.io/enricopal.github.io/publications/entity2rec.pdf). In 11th ACM Conference on Recommender Systems (RecSys) , Como, Italy,
* Palumbo E., Monti D., Rizzo G., Troncy R., Baralis E. (2020) [entity2rec: Property-specific Knowledge Graph Embeddings for Recommender Systems](https://www.sciencedirect.com/science/article/pii/S0957417420300610), Expert Systems with Applications
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entity2rec使用特定于属性的知识图嵌入生成项目推荐_Python_下载.zip
共128个文件
edgelist:65个
py:39个
dat:12个
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entity2rec使用特定于属性的知识图嵌入生成项目推荐_Python_下载.zip (128个子文件)
all.dat 59.2MB
train.dat 41.44MB
all.dat 23.11MB
train.dat 16.17MB
test.dat 11.85MB
val.dat 5.91MB
test.dat 4.63MB
all.dat 3.86MB
train.dat 2.7MB
val.dat 2.31MB
test.dat 795KB
val.dat 395KB
feedback_dct:subject.edgelist 24.6MB
feedback_dbo:starring.edgelist 20.56MB
feedback_dbo:writer.edgelist 19.68MB
feedback_dbo:producer.edgelist 19.68MB
feedback_dbo:distributor.edgelist 19.64MB
feedback_dbo:director.edgelist 19.62MB
feedback_dbo:musicComposer.edgelist 19.61MB
feedback_dbo:cinematography.edgelist 19.53MB
feedback_dbo:editing.edgelist 19.5MB
feedback.edgelist 19.34MB
feedback_dct:subject.edgelist 14.91MB
feedback_dct:subject.edgelist 11.34MB
feedback_dbo:author.edgelist 10.4MB
feedback_dbo:literaryGenre.edgelist 10.28MB
feedback_dbo:publisher.edgelist 10.28MB
feedback_dbo:mediaType.edgelist 9.99MB
feedback_dbo:subsequentWork.edgelist 9.9MB
feedback_dbo:previousWork.edgelist 9.84MB
feedback_dbo:series.edgelist 9.76MB
feedback_dbo:language.edgelist 9.7MB
feedback_dbo:country.edgelist 9.7MB
feedback_dbo:coverArtist.edgelist 9.66MB
feedback.edgelist 9.53MB
dct:subject.edgelist 8.9MB
dct:subject.edgelist 5.38MB
dct:subject.edgelist 5.25MB
feedback_dbo:genre.edgelist 4.5MB
feedback_dbo:associatedBand.edgelist 4.13MB
feedback_dbo:associatedMusicalArtist.edgelist 4.13MB
feedback_dbo:recordLabel.edgelist 4.11MB
feedback_dbo:hometown.edgelist 3.42MB
feedback_dbo:bandMember.edgelist 3.06MB
feedback_dbo:formerBandMember.edgelist 3.05MB
feedback_dbo:birthPlace.edgelist 2.88MB
feedback_dbo:instrument.edgelist 2.78MB
feedback_dbo:occupation.edgelist 2.69MB
feedback.edgelist 2.44MB
dbo:genre.edgelist 2.07MB
dbo:associatedBand.edgelist 1.69MB
dbo:associatedMusicalArtist.edgelist 1.69MB
dbo:starring.edgelist 1.22MB
dbo:hometown.edgelist 1004KB
dbo:author.edgelist 887KB
dbo:literaryGenre.edgelist 768KB
dbo:publisher.edgelist 762KB
dbo:bandMember.edgelist 636KB
dbo:formerBandMember.edgelist 630KB
dbo:mediaType.edgelist 474KB
dbo:birthPlace.edgelist 451KB
dbo:subsequentWork.edgelist 379KB
dbo:instrument.edgelist 350KB
dbo:writer.edgelist 346KB
dbo:producer.edgelist 343KB
dbo:previousWork.edgelist 314KB
dbo:distributor.edgelist 299KB
dbo:director.edgelist 282KB
dbo:musicComposer.edgelist 273KB
dbo:occupation.edgelist 257KB
dbo:series.edgelist 240KB
dbo:cinematography.edgelist 196KB
dbo:recordLabel.edgelist 192KB
dbo:language.edgelist 176KB
dbo:country.edgelist 169KB
dbo:editing.edgelist 164KB
dbo:coverArtist.edgelist 129KB
.gitignore 58B
properties.json 992B
properties_collaborative_content_example.json 742B
LICENSE 11KB
README.md 2KB
entity2rec workflow.png 95KB
evaluator.py 20KB
node2vec.py 12KB
fmrec.py 11KB
trans_recommender.py 11KB
mml_recommender.py 11KB
entity2rec.py 9KB
sparql.py 8KB
entity2vec.py 6KB
turi_ranking_fm.py 6KB
data_preprocessing.py 5KB
surprise_recommender.py 5KB
parse_args.py 5KB
node2vec_recommender.py 4KB
entity2rel.py 4KB
optimize_hyper_params.py 3KB
main.py 3KB
optimize_node2vec_hyper_params.py 3KB
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