moveforever opened a new issue #18346: URL: https://github.com/apache/incubator-mxnet/issues/18346
In recommendation, there appear many features: - dense features, for instance, ctr, item exposure, item click, item statis features, etc. - one-hot categorical features, for instance, user's network, user's gender, item_id to score - mulit-hot categorical features, for instance, user profiles which describes interest, and there exists much interests for a user. So the length of the multi-hot categorical features is variable. The sample can be as followed.  The row is splited by ^A(\001). There are five columns. The first column is label, supporting multi labels. And the second column is dense feature, and the third column is categorical feature, and the the fourth column is multi-hot categorical feature which can be splited by comma , and the fifth column is sparse feature which is support wide input for google wide and deep model. The sample contains many formated features which are storaged by csv or sparse. So it is complicated. To high performance parse the sample, i implent c++ DataIter. Train at gpu will not be hold back by cpu parsing sample. May i contribute the code to mxnet? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
