Program analysis tools often produce undesirable output due to various approximations. We present an approach and a system Eugene that allows user feedback to guide such approximations towards producing the desired output. We formulate the problem of user-guided program analysis in terms of solving a combination of hard rules and soft rules: hard rules capture soundness while soft rules capture degrees of approximations and preferences of users. Our technique solves the rules using an off-the-shelf solver in a manner that is sound (satisfies all hard rules), optimal (maximally satisfies soft rules), and scales to real-world analy- ses and programs. We evaluate Eugene on two different analyses with labeled output on a suite of seven Java pro- grams of size 131–198 KLOC. We also report upon a user study involving nine users who employ Eugene to guide an information-flow analysis on three Java micro-benchmarks. In our experiments, Eugene significantly reduces misclassified reports upon providing limited amounts of feedback.