"2. Use plain linear models for non-linear interaction" It should be noted that Linear models are only linear in the model parameters, while the features can be transformed using non-linear functions. This trick makes linear models very powerful. Also if you have big data (in millions/billions) then you are better off with linear models, as SVM is very difficult to scale.

In my experience (all in big data), I rarely seen people use SVM, usual choices are logistic regressions and tree based models. In some finance and insurance industries you are restricted to use only interpretable models, which linear models are.