ATLASREC: LANGUAGE-GROUNDED MULTI-HORIZON SUCCESSOR DYNAMICS FOR SEQUENTIAL RECOMMENDATION

Language-enhanced sequential recommenders improve how items and histories are represented, but next-item prediction also requires direction: knowing what a sequence means is not the same as knowing where it is going. We formulate this task as a state–dynamics–decision problem and introduce ATLASREC. Frozen language representations estimate a recency-weighted semantic state; training interactions estimate sparse, popularity-relative successor dynamics; and a pretrained recommender provides the behavioral decision reference. An event \(r\) positions back queries the \((r + 1)\)-step operator, so every source is interpreted at its exact distance from the target. We show that repeating a one-step operator assumes horizon-invariant dynamics and otherwise incurs a discounted conditional-KL mismatch. A minimum-information closure incorporates the two evidence fields while deviating minimally from the behavioral prior, and finite-sample successor uncertainty yields an explicit robust ranking condition. On three same-setting Amazon benchmarks, ATLASREC is best in 11 of 12 comparisons with prior methods. Against its exact anchor, it improves all 21 full-catalog metrics, with relative gains up to 18.64%. Component, horizon, state, popularity, encoder-transfer, and efficiency results validate the complete formulation.