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tffm is working quite well on my machine, much faster than other single core libraries like libfm and fastFM. But I found a small problem for regression, tffm tends to give higher values than libfm or fastFM, I guess this may be caused by not given max or min of predictions.
In libfm, there is a line
double p = predict_case(data);
p = std::min(max_target, p);
p = std::max(min_target, p);
I don't think these code is a must, making it configurable definitely offers more choices.
Another interesting point, tffm seems using a lot more memory, a recent test shows that after init with about 40GB memory, the peak for each epoch may reach 70-80GB, but drops to 40GB at the end of each epoch.
Need to compare tffm and libfm in terms of speed and quality.
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