massquantity

I'll reply in that issue. I'm also curious that does `caser` offer a good performance on your data?

There are multiple ways of splitting data in this library, and you can find some description in [User Guide](https://github.com/massquantity/LibRecommender/tree/master/examples#data-format). The `python_chrono_split` you mentioned is equivalent to `split_by_ratio_chrono` in LibRecommender. You...

1. No, you typically need to generate negative samples for both training and test data. The reason of generating negative samples for test data is evaluating. One can't get a...

Your data has 1.6 million users or 1.6 million records? That makes quite a difference. If you have 1.6 million users, the whole data may be large, easily exceeding 100...

Did you get all the column names right? If the column names are correct, then I guess the problem lies in their way of processing data. I think the problem...

Besides, they use `set` instead of `numpy.array` for all the items of every user, and `set` costs way more memory than the compact `numpy.array`. Also be aware that in the...

Reindexing and negative samples are fine. The real problem is the last line `apply(lambda x: self.item_pool - x)` . `item_pool` contains all the items, and `item_pool - x` means remove...

I don't have time to look into every single line of their code, but I think the implementations are roughly the same. You can also deal with ranking problem and...

After calling `train_data, eval_data = split_by_ratio_chrono`, `train_data` and `eval_data` are all DataFrames just like the original data. So I don't get it, why you can't get items and labels for...

Well, I don't recommend you doing this id-mapping thing. To ensure smooth code running for users, LibRecommender applies a couple of special processings. The source code of id-mapping is something...