Model based approaches to content based recommenders. How does this work? I have a question regarding the use of model based approaches to recommender systems. 
So, the goal is to create a model that predicts the user reaction to a specific item.  Either a rating scale or a “like/dislike” expectation. The problem that I can’t quite understand is that, in order to so, you would need a specific model for each user. How can that be done in practice?
Let’s take, for instance, a content based recommender that uses a classifier or regression model to predict the user’s interest on items based on its features. Ex: rate movies based on director, cast, genre, etc. Since every    user has a distinct individual taste you would need to learn a specific parameter for each user-feature. 
So you need to fit a model for each user? How can that work given the fact that the number of observations per user is usually very small compared to number of features?  There will be more explanatory variables than observations. Besides the resulting model would be prone to overfitting given the lack of data would it not?
 A: Mining Massive Datasets is freely available as a pdf and has an entire chapter on recommendation systems. The Coursera course by the same name also has a set of videos that walk through the topic. The questions you ask are good, but quite broad, so I'd recommend checking out those sources as they should address your questions.
A: I'm new to recommenders, and had the same question about the feasibility of making a separate model for each user. Who would want to maintain potentially millions of models, most of which are likely to be overfit given the small user-specific samples and massive feature space? I read the chapter that @Tchotchke referenced and found this particular section (page 319):

Unfortunately, classifiers of all types tend to take a long time to
  construct. For instance, if we wish to use decision trees, we need one
  tree per user. Con- structing a tree not only requires that we look at
  all the item profiles, but we have to consider many different
  predicates, which could involve complex com- binations of features.
  Thus, this approach tends to be used only for relatively small problem
  sizes.

In summary, the authors acknowledge the same reservations.
The link to the original pdf of the book seems to be down, but I was able to find a copy of the pdf hosted on GitHub here.
