A new interests burn-down diffusion process models decaying user interests for personalized collaborative filtering and outperforms prior generative methods in the StageCF implementation.
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cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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Interests Burn-down Diffusion Process for Personalized Collaborative Filtering
A new interests burn-down diffusion process models decaying user interests for personalized collaborative filtering and outperforms prior generative methods in the StageCF implementation.
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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.