It's all about features and data scale. Recommendation system itself is actually a large table, DL method already proved effectiveness there. Let's say if you have text in your tabular data. Tree model(with traditional method such as tfidf) will do much worse than the transformer-based model. DL always suffers from inadequate data, so if there's no enough data or inductive biases, tree model can be a better choice in that way.