Interview-style dialogue for service recommendation must both elicit users’ underlying needs and collect information required for recommendation. We propose a Question Tree–based guidance method that steers dialogue toward target information while allowing externally controlled topic continuation and switching. The Question Tree represents questions as nodes linked by parent–child derivational relations. For each user response, the system generates deepening and target-approaching question candidates, appends suitable candidates to the tree, and selects the next question from child or sibling nodes according to the required topic-control action. Selection uses a weighted combination of sentence-embedding similarity to the current dialogue context and to the target information. We evaluated the method in LLM-based dialogue simulations with externally controlled continuation and switching. The combined method increased the mean target-information collection rate from 10.8% to 26.6% after 10 turns and reduced cross-session variability, without a statistically significant difference from the baseline in the limited human naturalness evaluation. These results indicate that explicit tree-based topic guidance can support information collection when combined with external topic-adaptation modules.