What Reasoning Should a Recommender Have? Designing Reasoning for Inference-Time Scaling in Recommendation

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Large language models can gain accuracy by using more computation at inference, most visibly through chain-of-thought, where the model proceeds step by step in language before answering. The mechanism that converts the extra computation into a better answer is what we call reasoning. How reasoning should be designed for recommendation, and in what form, is far less settled. This dissertation studies that question, so that a recommender can turn extra inference-time computation into better predictions. Whether the reasoning trace is latent, textual, or mixed is a design choice, not a prior commitment, and the work is organized around three questions. The first asks how to design the reasoning trace for a conventional sequential recommender (RQ1). My first study, RecRec (accepted at RecSys 2026), gives the recommender a reasoning trace separate from the interests it predicts from, and outperforms prior reasoning-enhanced methods across four datasets. The second question (RQ2) moves to generative recommendation over semantic IDs, where the trace can be designed in more ways, and asks which design is native to the task. The third (RQ3) turns to the economics of these traces. Latent and textual forms cost very different amounts of computation, so the question is which form yields the most accuracy for a given budget, and how to compare methods at matched compute. The field has no settled recipe for any of this, and the appropriate designs are what I bring to the symposium.