Contextual bandit algorithms have become popular tools in online recommendation and advertising systems. Ofﬂine evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their “partial-label” nature. A common practice is to create a simulator which simulates the online environment for the problem at hand and then run an algorithm against this simulator. However, creating the simulator itself is often difﬁcult and modeling bias is usually unavoidably introduced. The purpose of this paper is two-fold. First, we review a recently proposed ofﬂine evaluation technique. Different from simulator-based approaches, the method is completely data-driven, is easy to adapt to different applications, and more importantly, provides provably unbiased evaluations. We argue for the wide use of this technique as standard practice when comparing bandit algorithms in real-life problems. Second, as an application of this technique, we compare and validate a number of new algorithms based on generalized linear models. Experiments using real Yahoo! data suggest substantial improvement over algorithms with linear models when the rewards are binary.