LLMRec

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LLMRec is an Academic Research tool that enhances recommendation systems by integrating Large Language Models with graph augmentation strategies. It utilizes LLMs to reinforce user-item interactions, enhance item attributes, and conduct user profiling.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is LLMRec?

LLMRec is a novel framework implemented in PyTorch, designed to significantly improve recommendation systems through the application of three distinct LLM-based graph augmentation strategies. These strategies include reinforcing user-item interactive edges, enhancing item node attributes, and conducting user node profiling, all from a natural language perspective. The tool leverages content within online platforms like Netflix and MovieLens to augment interaction graphs. It provides code, original data, and augmented data, making it a valuable resource for researchers and data scientists working on recommendation systems. LLMRec also offers multi-modal datasets, including textual and visual data, and supports LLM-augmented textual data and embeddings for comprehensive research.

Best used for

Ideal for data scientists and professors who need to develop more accurate and context-rich recommendation systems, augment existing datasets with LLM-generated insights, and explore the integration of large language models with graph-based approaches. Especially valuable for academic research in recommendation systems and data intelligence.

Common actions

augment recommendation systems

enhance graph data

profile users with LLMs

improve item attributes

research recommendation models

github copilot"AI Agents"face swappingdeepfakeworkflowscollaborationopen-sourcelow-code/no-codeautomated workflow

Capabilities

Key features

LLM-based graph augmentation
Reinforce user-item interactions
Enhance item attributes
Conduct user profiling
Multi-modal dataset support
LLM-augmented data

Target Audience

data scientistprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of graph augmentation strategies does LLMRec employ?

LLMRec utilizes three main LLM-based graph augmentation strategies: reinforcing user-item interactive edges, enhancing item node attributes with additional information, and conducting user node profiling to create richer user representations. These strategies are applied from a natural language perspective to leverage content within online platforms.

What datasets are supported by LLMRec?

LLMRec supports datasets such as Netflix and MovieLens. It also provides multi-modal datasets, including textual and visual data, and offers LLM-augmented textual data and embeddings. The Netflix dataset, in particular, is a collected multi-modal dataset compatible with state-of-the-art recommendation models.

How can I get started with LLMRec?

To get started with LLMRec, you need to install the dependencies using `pip install -r requirements.txt`. The process involves two main stages: LLM-based Data Augmentation (running Python scripts for UI augmentation, user profiling, and item attribute generation) and Recommender training with LLM-augmented Data (running `main.py` with your chosen dataset).

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Coding & Development › Open Source & Models Data & Analytics › Predictive Analytics

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