Normalizing the user preference vector is not necessary in this scheme. The magnitude of the preference vector is a constant scale on all of the comparisons to possible recommendable entities, so it drops out.

Overall, this is a reasonable starting point for recommending user actions.

When you have more data, you can try using collaborative filtering to estimate user preferences more robustly from sparse data. You could also try optimizing user and entity parameters by maximizing a likelihood function on followed recommendations composed of probabilities regressed from a function of the user and entity Mark vectors.