Auditing Semantic Gains in Sequential Recommendation with a Lightweight Recovery Test

The paper introduces LIME-Rec, an auditable recovery test combining a SASRec sequential expert, an ItemCF co-occurrence expert, and a semantic expert built from frozen BAAI/bge-base-en-v1.5 item embeddings. On Amazon Beauty, Toys, and Sports, it reports R@10 scores of 0.0996, 0.1105, and 0.0593, exceeding the strongest comparison baseline by 7.0%-12.0%. Permuting item-text embeddings across item IDs reduces R@10 by 13.6%-17.5%, suggesting that genuine text-item correspondence, rather than representation capacity alone, contributes to the gains.