TRACE: Targeted Ranking-Aware Counterfactual Explanation for Sequential Recommendation

Abstract

Ranking-constraint counterfactual explanation for sequential recommendation requires query-limited search to decide where to edit and what to substitute—the bottlenecked for query efficiency lies more in how the search space is structured than in the mutation rate alone. We propose TRACE (Targeted Ranking-Aware Counterfactual Explanation), which decomposes the search into three stages under an embedding-accessible, training-free setting: influence-guided position selection, plausibility-aware candidate retrieval, and actual-margin beam pruning. Across five datasets and three recommender architectures, TRACE outperforms the GA-based baseline GECE in validity, query efficiency, edit cost, and likelihood preservation, with the largest gains on bring-in (raising a target item to top-1) and up to an order-of-magnitude reduction in queries on push-out (displacing the current top-1 beyond top-K). Ablations confirm that the gains arise from structuring the search space before evaluation, rather than from increased mutation frequency. Available code: https://github.com/devUuung/TRACE