getting the number of Koren's SVD++

[Kublai-Jing]

solved.... my data has some issue

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Hi,

I am trying to replicate the result on Netflix probe set in Koren's 2008 KDD paper using graphchi implementation of SVD++ so I set up the following parameters in
graphchi-cpp/toolkits/collaborative_filtering/svdpp.cpp

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svdpp.step_dec = get_option_float("svdpp_step_dec", 0.9);
svdpp.itmBiasStep = get_option_float("svdpp_item_bias_step", 0.007);
svdpp.itmBiasReg = get_option_float("svdpp_item_bias_reg", 0.005);
svdpp.usrBiasStep = get_option_float("svdpp_user_bias_step", 0.007);
svdpp.usrBiasReg = get_option_float("svdpp_user_bias_reg", 0.005);
svdpp.usrFctrStep = get_option_float("svdpp_user_factor_step", 0.007);
svdpp.usrFctrReg = get_option_float("svdpp_user_factor_reg", 0.015);
svdpp.itmFctrReg = get_option_float("svdpp_item_factor_reg", 0.015);
svdpp.itmFctrStep = get_option_float("svdpp_item_factor_step", 0.007);
svdpp.itmFctr2Reg = get_option_float("svdpp_item_factor2_reg", 0.015);
svdpp.itmFctr2Step = get_option_float("svdpp_item_factor2_step", 0.001);

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However I am getting something like this:

[minval] => [1]
[maxval] => [5]
[max_iter] => [40]
[quiet] => [1]
[D] => [50]
28.9275) Iteration: 0 Training RMSE: 0.991109 Validation RMSE: 1.05016 ratings_per_sec: 0
56.988) Iteration: 1 Training RMSE: 1.13069 Validation RMSE: 1.13803 ratings_per_sec: 1.702e+06
84.9509) Iteration: 2 Training RMSE: 1.37655 Validation RMSE: 1.43292 ratings_per_sec: 2.29897e+06
113.139) Iteration: 3 Training RMSE: 1.68918 Validation RMSE: 1.87668 ratings_per_sec: 2.59855e+06
140.337) Iteration: 4 Training RMSE: 1.88749 Validation RMSE: 2.08639 ratings_per_sec: 2.79937e+06
169.082) Iteration: 5 Training RMSE: 1.98145 Validation RMSE: 2.06916 ratings_per_sec: 2.90865e+06
197.412) Iteration: 6 Training RMSE: 1.98927 Validation RMSE: 2.12117 ratings_per_sec: 2.99253e+06
225.531) Iteration: 7 Training RMSE: 1.98473 Validation RMSE: 2.11817 ratings_per_sec: 3.05846e+06
254.665) Iteration: 8 Training RMSE: 1.97704 Validation RMSE: 2.14269 ratings_per_sec: 3.09737e+06
283.274) Iteration: 9 Training RMSE: 1.97679 Validation RMSE: 2.14098 ratings_per_sec: 3.13412e+06
310.496) Iteration: 10 Training RMSE: 1.97564 Validation RMSE: 2.1162 ratings_per_sec: 3.17823e+06
338.768) Iteration: 11 Training RMSE: 1.97641 Validation RMSE: 2.11651 ratings_per_sec: 3.20535e+06
366.073) Iteration: 12 Training RMSE: 1.97204 Validation RMSE: 2.06543 ratings_per_sec: 3.23678e+06
394.577) Iteration: 13 Training RMSE: 1.97018 Validation RMSE: 2.0808 ratings_per_sec: 3.25387e+06
423.161) Iteration: 14 Training RMSE: 1.9682 Validation RMSE: 2.08301 ratings_per_sec: 3.26834e+06
452.649) Iteration: 15 Training RMSE: 1.9673 Validation RMSE: 2.05791 ratings_per_sec: 3.27412e+06
481.934) Iteration: 16 Training RMSE: 1.96858 Validation RMSE: 2.05757 ratings_per_sec: 3.28081e+06
511.155) Iteration: 17 Training RMSE: 1.96518 Validation RMSE: 2.06031 ratings_per_sec: 3.28712e+06
539.405) Iteration: 18 Training RMSE: 1.96373 Validation RMSE: 2.08988 ratings_per_sec: 3.29856e+06
569.019) Iteration: 19 Training RMSE: 1.96371 Validation RMSE: 2.05959 ratings_per_sec: 3.30103e+06

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Any idea what happened ??

Thanks !