Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review

• URL: http://arxiv.org/abs/2402.18590v3
• Date: Tue, 19 Mar 2024 07:56:40 GMT
• Title: Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
• Authors: Arpita Vats, Vinija Jain, Rahul Raja, Aman Chadha,
• Abstract summary: The paper underscores the significance of Large Language Models in reshaping recommender systems. LLMs exhibit exceptional proficiency in recommending items, showcasing their adeptness in comprehending intricacies of language. Despite their transformative potential, challenges persist, including sensitivity to input prompts, occasional misinterpretations, and unforeseen recommendations.
• Score: 2.780460221321639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paper underscores the significance of Large Language Models (LLMs) in reshaping recommender systems, attributing their value to unique reasoning abilities absent in traditional recommenders. Unlike conventional systems lacking direct user interaction data, LLMs exhibit exceptional proficiency in recommending items, showcasing their adeptness in comprehending intricacies of language. This marks a fundamental paradigm shift in the realm of recommendations. Amidst the dynamic research landscape, researchers actively harness the language comprehension and generation capabilities of LLMs to redefine the foundations of recommendation tasks. The investigation thoroughly explores the inherent strengths of LLMs within recommendation frameworks, encompassing nuanced contextual comprehension, seamless transitions across diverse domains, adoption of unified approaches, holistic learning strategies leveraging shared data reservoirs, transparent decision-making, and iterative improvements. Despite their transformative potential, challenges persist, including sensitivity to input prompts, occasional misinterpretations, and unforeseen recommendations, necessitating continuous refinement and evolution in LLM-driven recommender systems.

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