TRACE: Targeted Ranking-Aware Counterfactual Explanation for Sequential Recommendation

초록

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 © 2026 Copyright held by the owner/author(s).

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