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License: MIT License
Bayesian Personalized Ranking for Spark
License: MIT License
If you did not use checkpoint or anything else, persist may not cut the data lineage.
then the data lineage would crash your jvm.
See you.
Hi, I have two questions.
In BPR the object function is to maximize the posterior probability, so the learning algorithm should be stochastic gradient ascent, that is to say we need subtract the regularized term not add. The codes in your implementation:
prodMat(prodPos, ::) :+= ((userMat(userId, ::) :* scale) + (prodMat(prodPos, ::) :* lambdaReg)) :* alpha
prodMat(prodNeg, ::) :+= ((-userMat(userId, ::) :* scale) + (prodMat(prodNeg, ::) :* lambdaReg)) :* alpha
userMat(userId, ::) :+= (((prodMat(prodPos, ::) - prodMat(prodNeg, ::)) :* scale) +
(userMat(userId, ::) :* lambdaReg)) :* alpha
should be changed as following:
prodMat(prodPos, ::) :+= ((userMat(userId, ::) :* scale) - (prodMat(prodPos, ::) :* lambdaReg)) :* alpha
prodMat(prodNeg, ::) :+= ((-userMat(userId, ::) :* scale) - (prodMat(prodNeg, ::) :* lambdaReg)) :* alpha
userMat(userId, ::) :+= (((prodMat(prodPos, ::) - prodMat(prodNeg, ::)) :* scale) -
(userMat(userId, ::) :* lambdaReg)) :* alpha
Indexes for the matrices are not present. No string indexer in place.
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