  We propose and design recommendation systems that incentivize efficient
exploration. Agents arrive sequentially, choose actions and receive rewards,
drawn from fixed but unknown action-specific distributions. The recommendation
system presents each agent with actions and rewards from a subsequence of past
agents, chosen ex ante. Thus, the agents engage in sequential social learning,
moderated by these subsequences. We asymptotically attain optimal regret rate
for exploration, using a flexible frequentist behavioral model and mitigating
rationality and commitment assumptions inherent in prior work. We suggest three
components of effective recommendation systems: independent focus groups, group
aggregators, and interlaced information structures.
