This talk will give an overview of some of the large-scale recommender systems at LinkedIn such as People You May Know (PYMK) and Suggested Skills Endorsements. This talk will also address how we formulate machine learning modeling problems to build these recommender systems and evaluate our models. Modeling for these recommender systems involves careful feature engineering and incorporating user feedback - both explicit and implicit. This talk will describe how we feature engineer through an example of modeling organizational overlap between people for link prediction and community detection over social graph. Also, how we incorporate user feedback through impression discounting ignored recommended results will be described. Careful evaluation of modeling changes both offline and online (A/B testing) is inherent part of measuring effectiveness of our recommender systems. We have built a sophisticated end-to-end A/B testing and evaluation platform called XLNT at LinkedIn and this talk will also cover how we use XLNT for power analysis, A/B testing, and measuring confidence of the results.