Empowering Few-Shot Recommender Systems with Large Language Models --
Enhanced Representations
- URL: http://arxiv.org/abs/2312.13557v1
- Date: Thu, 21 Dec 2023 03:50:09 GMT
- Title: Empowering Few-Shot Recommender Systems with Large Language Models --
Enhanced Representations
- Authors: Zhoumeng Wang
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recommender systems utilizing explicit feedback have witnessed significant
advancements and widespread applications over the past years. However,
generating recommendations in few-shot scenarios remains a persistent
challenge. Recently, large language models (LLMs) have emerged as a promising
solution for addressing natural language processing (NLP) tasks, thereby
offering novel insights into tackling the few-shot scenarios encountered by
explicit feedback-based recommender systems. To bridge recommender systems and
LLMs, we devise a prompting template that generates user and item
representations based on explicit feedback. Subsequently, we integrate these
LLM-processed representations into various recommendation models to evaluate
their significance across diverse recommendation tasks. Our ablation
experiments and case study analysis collectively demonstrate the effectiveness
of LLMs in processing explicit feedback, highlighting that LLMs equipped with
generative and logical reasoning capabilities can effectively serve as a
component of recommender systems to enhance their performance in few-shot
scenarios. Furthermore, the broad adaptability of LLMs augments the
generalization potential of recommender models, despite certain inherent
constraints. We anticipate that our study can inspire researchers to delve
deeper into the multifaceted dimensions of LLMs's involvement in recommender
systems and contribute to the advancement of the explicit feedback-based
recommender systems field.
Related papers
- LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation [15.972926854420619]
Leveraging large language models (LLMs) offers new opportunities for comprehensive recommendation logic generation.
Fine-tuning LLM models for recommendation tasks incurs high computational costs and alignment issues with existing systems.
In this work, our proposed effective strategy LANE aligns LLMs with online recommendation systems without additional LLMs tuning.
arXiv Detail & Related papers (2024-07-03T06:20:31Z) - RAGSys: Item-Cold-Start Recommender as RAG System [0.0]
Large Language Models (LLM) hold immense promise for real-world applications, but their generic knowledge often falls short of domain-specific needs.
In-Context Learning (ICL) offers an alternative, which can leverage Retrieval-Augmented Generation (RAG) to provide LLMs with relevant demonstrations for few-shot learning tasks.
We argue that ICL retrieval in this context resembles item-cold-start recommender systems, prioritizing discovery and maximizing information gain over strict relevance.
arXiv Detail & Related papers (2024-05-27T18:40:49Z) - Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review [2.780460221321639]
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.
arXiv Detail & Related papers (2024-02-11T00:24:17Z) - DRDT: Dynamic Reflection with Divergent Thinking for LLM-based
Sequential Recommendation [53.62727171363384]
We introduce a novel reasoning principle: Dynamic Reflection with Divergent Thinking.
Our methodology is dynamic reflection, a process that emulates human learning through probing, critiquing, and reflecting.
We evaluate our approach on three datasets using six pre-trained LLMs.
arXiv Detail & Related papers (2023-12-18T16:41:22Z) - LLMRec: Benchmarking Large Language Models on Recommendation Task [54.48899723591296]
The application of Large Language Models (LLMs) in the recommendation domain has not been thoroughly investigated.
We benchmark several popular off-the-shelf LLMs on five recommendation tasks, including rating prediction, sequential recommendation, direct recommendation, explanation generation, and review summarization.
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
arXiv Detail & Related papers (2023-08-23T16:32:54Z) - 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) - Unlocking the Potential of User Feedback: Leveraging Large Language
Model as User Simulator to Enhance Dialogue System [65.93577256431125]
We propose an alternative approach called User-Guided Response Optimization (UGRO) to combine it with a smaller task-oriented dialogue model.
This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models.
Our approach outperforms previous state-of-the-art (SOTA) results.
arXiv Detail & Related papers (2023-06-16T13:04:56Z) - 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.