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.
Related papers
- Emerging Synergies Between Large Language Models and Machine Learning in
Ecommerce Recommendations [19.405233437533713]
Large language models (LLMs) have superior capabilities in basic tasks of language understanding and generation.
We introduce a representative approach to learning user and item representations using LLM as a feature encoder.
We then reviewed the latest advances in LLMs techniques for collaborative filtering enhanced recommendation systems.
arXiv Detail & Related papers (2024-03-05T08:31:00Z) - Empowering Few-Shot Recommender Systems with Large Language Models --
Enhanced Representations [0.0]
Large language models (LLMs) offer novel insights into tackling the few-shot scenarios encountered by explicit feedback-based recommender systems.
Our study can inspire researchers to delve deeper into the multifaceted dimensions of LLMs's involvement in recommender systems.
arXiv Detail & Related papers (2023-12-21T03:50:09Z) - Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling [18.297332953450514]
We propose LANCER, which leverages the semantic understanding capabilities of pre-trained language models to generate personalized recommendations.
Our approach bridges the gap between language models and recommender systems, resulting in more human-like recommendations.
arXiv Detail & Related papers (2023-09-19T08:54:47Z) - LLM-Rec: Personalized Recommendation via Prompting Large Language Models [62.481065357472964]
Large language models (LLMs) have showcased their ability to harness commonsense knowledge and reasoning.
Recent advances in large language models (LLMs) have showcased their remarkable ability to harness commonsense knowledge and reasoning.
This study introduces a novel approach, coined LLM-Rec, which incorporates four distinct prompting strategies of text enrichment for improving personalized text-based recommendations.
arXiv Detail & Related papers (2023-07-24T18:47:38Z) - Exploring Large Language Model for Graph Data Understanding in Online
Job Recommendations [63.19448893196642]
We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs.
By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
arXiv Detail & Related papers (2023-07-10T11:29:41Z) - Recommender Systems in the Era of Large Language Models (LLMs) [62.0129013439038]
Large Language Models (LLMs) have revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI)
We conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting.
arXiv Detail & Related papers (2023-07-05T06:03:40Z) - How Can Recommender Systems Benefit from Large Language Models: A Survey [82.06729592294322]
Large language models (LLM) have shown impressive general intelligence and human-like capabilities.
We conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.
arXiv Detail & Related papers (2023-06-09T11:31:50Z) - A Survey on Large Language Models for Recommendation [77.91673633328148]
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP)
This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
arXiv Detail & Related papers (2023-05-31T13:51:26Z) - Rethinking the Evaluation for Conversational Recommendation in the Era
of Large Language Models [115.7508325840751]
The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs)
In this paper, we embark on an investigation into the utilization of ChatGPT for conversational recommendation, revealing the inadequacy of the existing evaluation protocol.
We propose an interactive Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user simulators.
arXiv Detail & Related papers (2023-05-22T15:12:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.