<p>This survey is intended to inform non-expert readers about the field of recommender systems, particularly collaborative filtering, through the lens of the impactful Netflix Prize competition. Readers will quickly be brought up to speed on pivotal recommender systems advances through the Netflix Prize, informing their prospective state-of-the-art research with meaningful historic artifacts. We begin with the pivotal FunkSVD approach early in the competition. We then discuss Probabilistic Matrix Factorization and the importance and extensibility of the model. We examine the strategies of the Netflix Prize winner, providing comparisons to the Probabilistic Matrix Factorization framework as well as commentary as to why one approach became extensively used in research while another did not. Collectively, these models help to understand the progression of collaborative filtering through the Netflix Prize era. In each topic, we include ample discussion of results and background information. Finally, we highlight major veins of research following the competition.</p>