Core Concepts
A novel framework that integrates soft lambda loss and permutation-sensitive learning to effectively align the objectives of language generation and ranking tasks, enabling Large Language Models to perform efficient and accurate list-wise recommendation.
Abstract
The paper introduces a novel framework, ALRO, that aims to enhance the ranking capabilities of Large Language Models (LLMs) for recommendation systems. The key highlights are:
Soft Lambda Loss (SLL): The authors propose a differentiable ranking score by combining the soft-argmax function with the traditional Lambda loss. This helps align the objectives of language generation and ranking tasks.
Permutation-Sensitive Learning (PSL): To address the position bias issue in LLM-based recommendation, the authors introduce a permutation-sensitive learning framework. This minimizes the output distribution distance between the original and permuted candidate lists during the fine-tuning stage, improving the model's permutation invariance.
Comprehensive Evaluation: The authors conduct extensive experiments on two real-world datasets, comparing ALRO against various state-of-the-art baselines in both embedding-based and LLM-based recommendation models. The results demonstrate the superior performance of ALRO in ranking tasks.
Ablation Study: The authors perform an ablation study to quantify the contributions of the individual components (SLL and PSL) within the ALRO framework.
Efficiency Analysis: The authors compare the performance and efficiency of ALRO against the bootstrapping method, showing that ALRO can achieve comparable outcomes while significantly reducing inference time.
Scalability: The authors investigate the adaptability of ALRO across different LLM parameter sizes, showcasing its consistent performance improvements over traditional supervised fine-tuning approaches.
Overall, the ALRO framework represents a significant advancement in leveraging LLMs for efficient and accurate list-wise recommendation, addressing key challenges such as objective alignment and position bias.
Stats
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Quotes
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