How to restructure my dataset for interpretability without losing performance?
What may be happening is that your attribute predictors are weak predictors, they are noisy. Meaningful decision trees can't be made out of product attribute features by xgb. 

When you are adding name as a predictor, xgb finds some signal wrt your target variable - rating and thus you get a better score. So your name plus attributes model may be performing better than attributes only model for this reason.

So if you from domain experience know product attributes are very weakly related to rating then you can conclude that this feature set of attributes is not going to help you make accurate predictions. Or instead of relying  on d omain expertise, you can use correlation or relevant statistical tests to understand attributes relation to rating and if found that relationship is non existent or very weak you can conclude model isn't possible.

So may be add more relevant features if possible if you want to make a reasonably good model.

Regards
Vik