Significant effort in machine learning and information retrieval has been devoted to identifying personalized content such as recommendations and search results. Personalized human computation has the potential to go beyond existing techniques like collaborative filtering to provide personal­ized results on demand, over personal data, and for complex tasks. This work-in-progress compares two approaches to personal­ized human computation. In both, users annotate a small set of training examples which are then used by the crowd to annotate unseen items. In the first approach, which we call taste-matching, crowd members are asked to annotate the same set of training examples, and the ratings of similar users on other items are then used to infer personal­ized ratings. In the second approach, taste-grokking, the crowd is presented with the training examples and asked to use them predict the ratings of the target user on other items.