In the first chapter, a recommendation platform sequentially collects information on a new product revealed from past consumer trials and uses it to better guide later consumers. Because consumers do not internalize the value of information they bring to others, their incentive for trying out the product can be socially insufficient. Given such a challenge, I study how the platform can maximize the total social surplus generated on it by designing its recommendation policy. In a model with binary product quality and general trial-generated signals, I show that the optimal design features a sequence of time-specific thresholds, which vary in a U-shaped pattern over the product's life. At any time, the platform should recommend the product if, based on its current belief, the probability of the product's quality being high is above the current threshold. My analysis also illustrates the potential usefulness of a Lagrangian duality approach for dynamic information design. The second chapter studies optimal information provision by a search goods seller. While the seller controls a consumer's pre-search information, he cannot control post-search information because the consumer will inevitably learn the product's match after search. A relaxed problem approach is developed to solve the optimal design, which accommodates both continuous value distributions and ex-ante heterogeneous consumers with privately known outside options. The optimal design is shown to crucially depend on the outside option value distribution, and can be implemented by a simple upper-censorship signal under certain regularity conditions. Several applications are provided, including comparing information designs of search goods and experience goods, and studying the effect of competition with a large number of sellers. The third chapter studies optimal disclosure regulation for entrepreneur public financing with a post-financing moral hazard problem. I show that partial disclosure can improve social welfare over full disclosure through reducing efficiency loss caused by the moral hazard problem. As a result, a properly designed partial disclosure rule would be optimal without assuming any disclosure cost. This remains true after allowing for endogenous entrepreneur types with adverse selection concerns. With (constrained) Bayesian persuasion tools, the optimal disclosure rule is fully characterized.