Implicit feedback is easy to collect and can be useful to build a recommendation system in online service. However, the feedback suffered from popularity bias because a user gives feedback to an item only if it is exposed. To build an unbiased recommendation system, counterfactual learning and meta learning approaches are applied to deal with such observational data.