Sensory-Aware Sequential Recommendation via Review-Distilled Representations

Language models (LMs) are increasingly used in recommender systems to generate recommendations or coordinate recommendation tools. However, does their strong performance imply that serving-time LM inference is necessary? We challenge this seemingly reasonable but unverified assumption. To investigate this question, we introduce recovery-based mechanism auditing, which tests whether independent alternatives can achieve the accuracy of LM-based recommender systems without the audited inference mechanisms under controlled information and evaluation conditions. For generative recommendation, we propose LIME-Rec, which combines sequential, collaborative, and offline semantic information without autoregressive LM decoding. On three Amazon Reviews datasets (Beauty, Toys, and Sports), LIME-Rec achieves the highest reported mean in all 12 dataset-metric combinations, with up to a 15.2% relative improvement in Recall@10 over GRAM, an LM-based generative recommender. For agentic recommendation, we propose DART-Rec, which replaces LM-based tool control with deterministic fusion and fixed expert selection. On 1,000 Beauty users, given the same candidate items and expert scores, deterministic fusion achieves a 16.7% relative improvement in Recall@10 over a fine-tuned LM controller. With at most two tool calls, fixed expert selection matches the LM controllers' Hit@10 outcomes for every user in 17 of 18 comparisons. These findings demonstrate that strong recommendation performance does not necessarily require serving-time LM inference: under the evaluated task and information conditions, alternatives without the specified inference mechanisms can match or exceed the accuracy of their LM-based counterparts. To support reproducibility, code is available at https://github.com/Double-wk/LIME-Rec.

Large language models (LLMs) can summarize heterogeneous user evidence in natural language, but current LLM recommenders often collapse enduring preferences, transient intent, and exposure-induced behavior into one profile. This makes recommendation vulnerable to feedback loops: repeated exposure is mistaken for preference, immediate clicks dominate delayed satisfaction, and fluent explanations need not reflect the ranking decision. We propose our method, a model-agnostic framework for long-horizon recommendation. Our method uses a frozen multimodal language model to convert item content and feedback into evidence-grounded semantic atoms, then maintains separate short-term, long-term, and exposure memories. Propensity-weighted updates reduce policy-induced exposure bias, while a conservative offline critic reranks candidates for delayed satisfaction under a behavior-support constraint. Explanations use only influential evidence atoms and are checked by counterfactual deletion. We provide an identification result and evaluate the framework in e-commerce-like, news-like, and short-video-like environments. Across ten seeds, our method improves discounted long-term value over the strongest alternative by 6.1%, 7.6%, and 6.7%, respectively. Twenty-seed paired ablations show significant value drops after removing propensity correction (0.739 +/- 0.191) or conservative support regularization (0.523 +/- 0.234). A frozen instruction language model also more than doubles semantic-atom NDCG over TF-IDF on a held-out paraphrase benchmark.

Next-basket repurchase recommendation is commonly formulated as a ranking task: given a customer's purchase history, the system ranks previously purchased items that may be needed again. In production settings, however, ranking accuracy is only one component of recommendation quality. Customers may also benefit from concise evidence about why an item is recommended now. Large language models (LLMs) offer a potential way to surface such evidence through feature-based, human-readable rationales grounded in interpretable behavioral signals. We construct repurchase features spanning cadence, frequency, recency, user behavior, and item popularity, and evaluate LLMs on two public grocery datasets and one proprietary retail dataset. We investigate (1) whether off-the-shelf LLMs can use these features as next-basket scorers relative to heuristic and supervised rankers, and (2) whether LLM-cited features carry outcome-grounded ranking signal. For the latter, we compare LLM-cited features with model-specific attribution methods under a cross-model feature-masking protocol that measures ranking degradation after masking selected features. Our results show that LLM scores are not competitive with supervised rankers, suggesting that off-the-shelf LLMs should not be used as standalone repurchase recommenders. However, changes in prompt and evidence representation can improve outcome-grounded feature-masking results in some settings even when ranking performance does not improve; the effect is dataset-dependent and does not consistently match attribution baselines. These findings suggest a practical role for LLMs as validated explanation components rather than primary rankers, with rationale quality evaluated separately from ranking accuracy.