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GALLM redefines sequential recommendation by seamlessly integrating collaborative signals into LLMs, achieving significant performance gains without extra complexity.
SmartGR achieves an 8.6% boost in recommendation performance while slashing inference time by over 2.3 times, tackling unique challenges in generative recommendation systems.
Length Bias in LLM-based recommendations can be effectively mitigated, leading to a 16.82% improvement in accuracy and fairness without significant computational costs.
Telecom World Models fuse the flexibility of LLMs with the fidelity of Digital Twins, enabling uncertainty-aware predictive planning that existing approaches can't match.
Mixing easy and hard examples during recommender pre-ranking hurts performance, but this new method disentangles them to boost user engagement by 0.4% in a real-world deployment.
