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Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph
In summary, LLM-KERec addresses the limitations of traditional recommendation systems by incorporating complementary knowledge and utilizing a large language model to capture user intent transitions, adapt to new items, and enhance recommendation efficiency in the evolving e-commerce landscape.
Proximal SCOPE for Distributed Sparse Learning
Based on the defined metric, we theoretically prove that pSCOPE is convergent with a linear convergence rate if the data partition is good enough.
Proximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate
Based on the defined metric, we theoretically prove that pSCOPE is convergent with a linear convergence rate if the data partition is good enough.
Feature-Distributed SVRG for High-Dimensional Linear Classification
Linear classification has been widely used in many high-dimensional applications like text classification.
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