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CRIM framework slashes costs for personalized AI search and recommendation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed CRIM, a new framework designed to make personalized search and recommendation systems more efficient. CRIM separates item encoding from user history modeling, using learnable summary tokens to compress items into reusable memories. This approach significantly reduces the computational costs associated with large language models in personalized retrieval by avoiding repeated encoding of item text. Evaluations on datasets like MovieLens-1M and Qilin demonstrate that CRIM achieves state-of-the-art performance with substantially lower inference costs. AI

IMPACT Reduces inference costs for personalized AI search and recommendation systems, potentially enabling wider adoption.

RANK_REASON The cluster describes a new research paper detailing a novel framework for improving AI model efficiency.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

CRIM framework slashes costs for personalized AI search and recommendation

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Haolin Li, Haipeng Zhang, Mang Li, Zhaohui Xia, Xun Zhu, Miao Li, Yu Zhang, Ji Wu ·

    CRIM: Composable and Reusable Item Memory for Efficient Personalized Retrieval

    arXiv:2610.35323v1 Announce Type: cross Abstract: Large language models incur substantial training and inference costs in personalized search and recommendation, as growing interaction histories lengthen request inputs and repeatedly expose the same item text to the model. We pro…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Ji Wu ·

    CRIM: Composable and Reusable Item Memory for Efficient Personalized Retrieval

    Large language models incur substantial training and inference costs in personalized search and recommendation, as growing interaction histories lengthen request inputs and repeatedly expose the same item text to the model. We propose \textbf{C}omposable and \textbf{R}eusable \te…

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