Attribute-Structured Temporal Rule Representations for Sequential Recommendation

Logic-based sequential recommenders introduce logical operators to impose structured reasoning for better recommendation, but few of them explicitly explore the impact of logical structure on this complex preference learning. Item attributes offer a natural grounding for making the role of logical structure explicit and empirically testable. yet existing methods rarely construct logic at this level. To address this gap, we introduce TAPR, a Temporal Attribute Probabilistic Rule model that grounds logical preference rules in explicit item attributes derived from a user’s interaction history. Probabilistic conjunction then composes this evidence into a structured rule that directly shapes the final ranking score. Implemented in a Beta representation space, this formulation captures soft preference evidence while remaining closed under probabilistic conjunction, allowing composed rules to stay in the same representation space for ranking. Experiments on three real-world datasets show that TAPR is competitive with strong sequential recommendation baselines. Synthetic experiments further verify that the explicit logical structure contributes to ranking behavior rather than serving only as a descriptive representation. Additional empirical analyses show that the learned rules are behaviorally faithful and that their utility depends on data availability and attribute-combination difficulty.