FAERec fuses collaborative ID embeddings with LLM semantic embeddings using adaptive gating and dual-level alignment to enhance tail-item sequential recommendations.
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AKT-Rec generates semantic IDs via MLLMs and RQ-VAE then applies cluster-guided adaptive embeddings with asymmetric transfer and hierarchical aggregation to improve long-tail recommendation metrics on industrial data.
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Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation
FAERec fuses collaborative ID embeddings with LLM semantic embeddings using adaptive gating and dual-level alignment to enhance tail-item sequential recommendations.
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From Head to Tail: Asymmetric Knowledge Transfer in Long-tail Recommendation with Generative Semantic IDs
AKT-Rec generates semantic IDs via MLLMs and RQ-VAE then applies cluster-guided adaptive embeddings with asymmetric transfer and hierarchical aggregation to improve long-tail recommendation metrics on industrial data.