LLMs exhibit mid-layer representation advantage for recommendations; MARC compresses representations modularly to reduce costs while improving performance, as shown in a large-scale online advertising deployment.
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MPZCH applies multi-probe linear hashing plus eviction policies to achieve zero collisions on user embeddings and higher freshness on item embeddings while keeping training and inference speeds comparable to standard methods.
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Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations
LLMs exhibit mid-layer representation advantage for recommendations; MARC compresses representations modularly to reduce costs while improving performance, as shown in a large-scale online advertising deployment.
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Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders
MPZCH applies multi-probe linear hashing plus eviction policies to achieve zero collisions on user embeddings and higher freshness on item embeddings while keeping training and inference speeds comparable to standard methods.