The use of computerized adaptive testing algorithms for ranking items (e.g., college preferences, career choices) involves two major challenges: unacceptably high computation times (selecting from a large item pool with many dimensions) and biased results (enhanced preferences or intensified examinee responses because of repeated statements across items). To address these issues, we introduce subpool partition strategies for item selection and within-person statement exposure control procedures. Simulations showed that the multinomial method reduces computation time while maintaining measurement precision. Both the freeze and revised Sympson-Hetter online (RSHO) methods controlled the statement exposure rate; RSHO sacrificed some measurement precision but increased pool use. Furthermore, preventing a statement's repetition on consecutive items neither hindered the effectiveness of the freeze or RSHO method nor reduced measurement precision.