We make a contextual recommendation engine as a service for online publishers at our startup ParallelDots. We discovered the problem of tags not really working well for recommendations on our clients websites too. We ended up using unsupervised word embeddings and auto encoders on top of them to solve the problem. We dont still use it for personalization though, just contextually similar articles. Great seeing some of similar problems being solved at New York Times too. :)