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Improving recommendation systems and search in the age of LLMs
Model architectures, data generation, training paradigms, and unified frameworks inspired by LLMs.
Finally, supervised fine-tuning (SFT) applies multi-turn chat templates designed to enhance the model’s understanding of member-entity interactions, improving its predictive capabilities across specific user interfaces. Offline experiments demonstrated incremental improvements through various enhancements: two-stage data filtering (+0.24% recall@200), synthetic positive queries (+0.7%), additional product features (+1.15%), query-to-query followed by query-to-product fine-tuning (+2.44%), and model weight merging (+4.67%). Li, Yuening, et al. “Short-Form Video Needs Long-Term Interests: An Industrial Solution for Serving Large User Sequence Models.” Proceedings of the 18th ACM Conference on Recommender Systems, Association for Computing Machinery, 2024, pp.
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