Comments (6)
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@hexiangnan Thank you for answering.
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@hexiangnan Hi, Professor He. I've rewritten the code to test eALS in the same way as you mentioned in the NCF paper, i.e., ranks 99 sampled negatives + 1 positive item. Besides, I use ml-1m.train.rating & ml-1m.test.rating & ml-1m.test.negative, which you provided in the neural_collaborative_filtering project. However, the performance of eALS is still not satisfying. For top-10 recommendation, the best iteration among 50 iterations yields
<iter, hr, ndcg, prec>: 16 0.6078 0.3411 0.2594,
which is much lower than what you presented in the NCF paper. The parameter settings is unchanged as:
Data ml-1m
#Users 6040
#Items 3706
#Ratings 994169 (train), 6040(testPositive), 6040(testNegative)
fastALS: showProgress=false, factors=64, maxIter=50, reg=0.010000, w0=10.00, alpha=0.75
Do you have any suggestions to reproduce the results shown in the NCF paper?
Thank you for taking your precious time to read this. Your prompt reply will be highly appreciated.
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from sigir16-eals.
@hexiangnan Thank you. I'll keep on trying.
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@hexiangnan Problem solved. Just to remind future visitors of this issue, I tried alpha=0.0 and w0=160 and the model yields:
MF_fastALS Best Iteration: <iter, hr, ndcg, prec>: 39 0.6978 0.4242 0.3393 [00:02:11.766]
This is pretty close to the performance that Professor He has presented in the NCF paper.
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