@inproceedings{ma2019jointly,
  title={Jointly Learning Explainable Rules for Recommendation with Knowledge Graph},
  author={Ma, Weizhi and Zhang, Min and Cao, Yue and Jin, Woojeong and Wang, Chenyang and Liu, Yiqun and Ma, Shaoping and Ren, Xiang},
  booktitle={The World Wide Web Conference},
  pages={1210--1221},
  year={2019},
  organization={ACM}
}

Formatting: 
	user id \t item id

Formatting: 
	item id in Amazon \t the title/brand/description of this item

Formatting:
	item id in Amazon \t items that have also\_buy/also\_view/buy\_after\_view/buy\_together association with this item, split by ' '

Formatting:
	item id in Amazon \t entity name \t entity id in Freebase

# Example:
> python getItemItemDic.py Cellphone abu
> python selectRules.py Cellphone abu 50
> python getFeatures.py Cellphone abu 50

# Example 1: Use Cellphone dataset
> java -jar BPRMF.jar --recommender=BPRMF --training-file=./RuleRecInput/Cellphone/trainingSet.txt --test-file=./RuleRecInput/Cellphone/testSet.txt --candidateFile=./RuleRecInput/Cellphone/candidates.txt --trainingPairFile=./RuleRecInput/Cellphone/trainingPairs.txt --trainingFeatures=./RuleRecInput/Cellphone/trainingFeatures.txt --testFeatures=./RuleRecInput/Cellphone/testFeatures.txt --learningRate=0.1 --usermodel=0 --iter-times=30 --rule-weight=0.005  --ruleWeightNumber=200 --resultFile=result.txt 
# output:recall@5=0.34968 recall@10=0.48024 NDCG@10=0.28287 [email protected] num_users=27840 num_items=100 num_lists=27840

# Example 2: Use Cellphone dataset with jointly learning
> java -jar BPRMF.jar --recommender=BPRMF --training-file=./RuleRecInput/Cellphone/trainingSet.txt --test-./RuleRecInput/Cellphone/testSet.txt --candidateFile=./RuleRecInput/Cellphone/candidates.txt --trainingPairFile=./RuleRecInput/Cellphone/trainingPairs.txt --trainingFeatures=./RuleRecInput/Cellphone/trainingFeatures.txt --testFeatures=./RuleRecInput/Cellphone/testFeatures.txt --learningRate=0.1 --usermodel=0 --iter-times=30 --rule-weight=0.005  --ruleWeightNumber=200 --resultFile=result.txt --trainTogether=2  --lossType=sigmoid --lossCombineRate=0.2 --ruleselectTrain=./RuleRecInput/Cellphone/ruleselect/ --ruleselectResult=./RuleRecInput/Cellphone/ruleselect/ 
# output:recall@5=0.36430 recall@10=0.49429 NDCG@10=0.29536 MRR=@10=0.23214 num_users=27840 num_items=100 num_lists=27840

# Example 3: Use Electronic dataset
> java -jar BPRMF.jar --recommender=BPRMF --training-file=./RuleRecInput/Electronic/trainingSet.txt --test-file=./RuleRecInput/Electronic/testSet.txt --candidateFile=./RuleRecInput/Electronic/candidates.txt --trainingPairFile=./RuleRecInput/Electronic/trainingPairs.txt --trainingFeatures=./RuleRecInput/Electronic/trainingFeatures.txt --testFeatures=./RuleRecInput/Electronic/testFeatures.txt --learningRate=0.05 --ruleWeightNumber=200 --usermodel=0 --iter-times=30 --rule-weight=0.01 --resultFile=result.txt 
# output:recall@5=0.20694 recall@10=0.29726 NDCG@10=0.17284 MRR=@10=0.13483 num_users=18223 num_items=100 num_lists=18223

# Example 4: Use Electronic dataset with jointly learning
> java -jar BPRMF.jar --recommender=BPRMF --training-file=./RuleRecInput/Electronic/trainingSet.txt --test-file=./RuleRecInput/Electronic/testSet.txt --candidateFile=./RuleRecInput/Electronic/candidates.txt --trainingPairFile=./RuleRecInput/Electronic/trainingPairs.txt --trainingFeatures=./RuleRecInput/Electronic/trainingFeatures.txt --testFeatures=./RuleRecInput/Electronic/testFeatures.txt --learningRate=0.05 --ruleWeightNumber=200 --usermodel=0 --iter-times=30 --rule-weight=0.01 --resultFile=result.txt --trainTogether=2  --lossType=sigmoid --lossCombineRate=0.005 --ruleselectTrain=./RuleRecInput/Electronic/ruleselect/ --ruleselectResult=./RuleRecInput/Electronic/ruleselect/ 
# output:recall@5=0.20798 recall@10=0.29979 NDCG@10=0.17407 MRR=@10=0.13570 num_users=18223 num_items=100 num_lists=18223