Same prediction when using user features and item features

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Hey,
After reading issues number #320 and #497. I was creating 3 different models.
1. Train a model with the user and item features.
2. Train a model only with user features
3. Train a model only with item features

For now, on the user features, I've got 1 feature: "**country**"
on the item features, I've got 2 features: "**gender**", "**category**""

To keep it simple, let's say that on **country** I have 3 values : "c1","c2","c3"
**gender** : "m", "f"
**category**: "a", "b", "c", "d"

All the features are categorical and to fit the values to the dataset I call :
`dataset.fit( users=user_features['user_id'].unique(),
items=item_features['item_id'].unique(),
item_features=["m", "f","a", "b", "c", "d"],
user_features=["c1","c2","c3"] )`

To build the item features for the model:
`item_tuple = ((1, ['f', 'a']), (2, ['m', 'b'])...)`
`item_features_m = dataset.build_item_features(item_tuple)`

To build the user features for the model:
`user = ((1, [ 'c1']), (2, ['c2'])...)`
`user_features_m = dataset.build_user_features(user_tuple)`

Then, I call the fit function of a warp lightfm model:

```
warp_model.fit(interactions,
user_features=user_features_m,
item_features=item_features_m,
epochs=10,
num_threads=1)
```

**And my weird problem is when I added the user_features all the users got almost the same prediction. (to 100,000 users got only 16 unique items from 4,000 optional items)**

When I remove the user features and trained without it's much better (to 100,000 users got 2,000 unique items from 4,000 optional items)

But in the future, I want to use more features that will maybe classify better than matrix factorization, and the features will help me to predict cold-start user prediction.

Any answer and advice will help me.

Thank you all

Start by reproducing the three training cases with the documented dataset.fit, build_user_features, build_item_features, and WARP fit calls shown in the issue. Compare prediction diversity with and without user features; the issue does not name a source file, test, or an agreed completion condition.

Written by the indexing model from the issue text.