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PRQ-KMeans achieves up to 11.8% improvement in recommendation performance by rethinking how semantic identifiers are tokenized.
Automated refinement of e-commerce recommendation strategies can boost order volume by 0.71% and reduce operational costs significantly.
OneRetrieval achieves the unprecedented feat of real-time editability in generative retrieval, significantly enhancing e-commerce search performance without compromising recall quality.
Reward your LLM's search queries like a discerning librarian: IG-Search uses information gain to give step-by-step feedback, boosting multi-hop reasoning without the annotation overhead.
Despite users preferring human-created videos, AI-generated content can achieve similar overall engagement on video platforms by flooding the system with sheer volume.
LLMs for e-commerce search can now be trained and evaluated on a massive, realistic dataset spanning the entire search pipeline, unlike existing datasets with anonymized data and limited scope.
