
LLMRec
[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"
Stars: 269

LLMRec is a PyTorch implementation for the WSDM 2024 paper 'Large Language Models with Graph Augmentation for Recommendation'. It is a novel framework that enhances recommenders by applying LLM-based graph augmentation strategies to recommendation systems. The tool aims to make the most of content within online platforms to augment interaction graphs by reinforcing u-i interactive edges, enhancing item node attributes, and conducting user node profiling from a natural language perspective.
README:
PyTorch implementation for WSDM 2024 paper LLMRec: Large Language Models with Graph Augmentation for Recommendation.
Wei Wei, Xubin Ren, Jiabin Tang, Qingyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin and Chao Huang*. (*Correspondence)
Data Intelligence Lab@University of Hong Kong, Baidu Inc.
This repository hosts the code, original data and augmented data of LLMRec.
LLMRec is a novel framework that enhances recommenders by applying three simple yet effective LLM-based graph augmentation strategies to recommendation system. LLMRec is to make the most of the content within online platforms (e.g., Netflix, MovieLens) to augment interaction graph by i) reinforcing u-i interactive edges, ii) enhancing item node attributes, and iii) conducting user node profiling, intuitively from the natural language perspective.
-
[x] [2024.3.20] 🚀🚀 📢📢📢📢🌹🔥🔥🚀🚀 Because baselines
LATTICE
andMMSSL
require some minor modifications, we provide code that can be easily run by simply modifying the dataset path. -
[x] [2023.11.3] 🚀🚀 Release the script for constructing the prompt.
-
[x] [2023.11.1] 🔥🔥 Release the multi-modal datasets (Netflix, MovieLens), including textual data and visual data.
-
[x] [2023.11.1] 🚀🚀 Release LLM-augmented textual data(by gpt-3.5-turbo-0613), and LLM-augmented embedding(by text-embedding-ada-002).
-
[x] [2023.10.28] 🔥🔥 The full paper of our LLMRec is available at LLMRec: Large Language Models with Graph Augmentation for Recommendation.
-
[x] [2023.10.28] 🚀🚀 Release the code of LLMRec.
- [ ] Provide different larger version of the datasets.
- [ ] ...
pip install -r requirements.txt
cd LLMRec/LLM_augmentation/
python ./gpt_ui_aug.py
python ./gpt_user_profiling.py
python ./gpt_i_attribute_generate_aug.py
cd LLMRec/
python ./main.py --dataset {DATASET}
Supported datasets: netflix
, movielens
Specific code execution example on 'netflix':
# LLMRec
python ./main.py
# w/o-u-i
python ./main.py --aug_sample_rate=0.0
# w/o-u
python ./main.py --user_cat_rate=0
# w/o-u&i
python ./main.py --user_cat_rate=0 --item_cat_rate=0
# w/o-prune
python ./main.py --prune_loss_drop_rate=0
├─ LLMRec/
├── data/
├── netflix/
...
🌹🌹 Please cite our paper if you use the 'netflix' dataset~ ❤️
We collected a multi-modal dataset using the original Netflix Prize Data released on the Kaggle website. The data format is directly compatible with state-of-the-art multi-modal recommendation models like LLMRec, MMSSL, LATTICE, MICRO, and others, without requiring any additional data preprocessing.
Textual Modality:
We have released the item information curated from the original dataset in the "item_attribute.csv" file. Additionally, we have incorporated textual information enhanced by LLM into the "augmented_item_attribute_agg.csv" file. (The following three images represent (1) information about Netflix as described on the Kaggle website, (2) textual information from the original Netflix Prize Data, and (3) textual information augmented by LLMs.)
Visual Modality:
We have released the visual information obtained from web crawling in the "Netflix_Posters" folder. (The following image displays the poster acquired by web crawling using item information from the Netflix Prize Data.)
🚀🚀 We provide the processed data (i.e., CF training data & basic user-item interactions, original multi-modal data including images and text of items, encoded visual/textual features and LLM-augmented text/embeddings). 🌹 We hope to contribute to our community and facilitate your research 🚀🚀 ~
-
netflix
: Google Drive Netflix. 🌟(Image&Text)
We use CLIP-ViT and Sentence-BERT separately as encoders for visual side information and textual side information.
Prompt
Recommend user with movies based on user history that each movie with title, year, genre. History: [332] Heart and Souls (1993), Comedy|Fantasy [364] Men with Brooms(2002), Comedy|Drama|Romance Candidate: [121]The Vampire Lovers (1970), Horror [155] Billabong Odyssey (2003),Documentary [248]The Invisible Guest 2016, Crime, Drama, Mystery Output index of user's favorite and dislike movie from candidate.Please just give the index in [].
Completion
248 121
Prompt
Generate user profile based on the history of user, that each movie with title, year, genre. History: [332] Heart and Souls (1993), Comedy|Fantasy [364] Men with Brooms (2002), Comedy|Drama|Romance Please output the following infomation of user, output format: {age: , gender: , liked genre: , disliked genre: , liked directors: , country: , language: }
Completion
{age: 50, gender: female, liked genre: Comedy|Fantasy, Comedy|Drama|Romance, disliked genre: Thriller, Horror, liked directors: Ron Underwood, country: Canada, United States, language: English}
Prompt
Provide the inquired information of the given movie. [332] Heart and Souls (1993), Comedy|Fantasy The inquired information is: director, country, language. And please output them in form of: director, country, language
For each user, 0 represents a positive sample, and 1 represents a negative sample. For each user, the dictionary stores augmented information such as 'age,' 'gender,' 'liked genre,' 'disliked genre,' 'liked directors,' 'country,' and 'language.'Completion
Ron Underwood, USA, English
For each item, the dictionary stores augmented information such as 'director,' 'country,' and 'language.'
step 1: select base model such as MMSSL or LATTICE
step 2: obtain user embedding and item embedding
step 3: generate candidate
_, candidate_indices = torch.topk(torch.mm(G_ua_embeddings, G_ia_embeddings.T), k=10)
pickle.dump(candidate_indices.cpu(), open('./data/' + args.datasets + '/candidate_indices','wb'))
Example of specific candidate data.
In [3]: candidate_indices
Out[3]:
tensor([[ 9765, 2930, 6646, ..., 11513, 12747, 13503],
[ 3665, 8999, 2587, ..., 1559, 2975, 3759],
[ 2266, 8999, 1559, ..., 8639, 465, 8287],
...,
[11905, 10195, 8063, ..., 12945, 12568, 10428],
[ 9063, 6736, 6938, ..., 5526, 12747, 11110],
[ 9584, 4163, 4154, ..., 2266, 543, 7610]])
In [4]: candidate_indices.shape
Out[4]: torch.Size([13187, 10])
If you find this work helpful to your research, please kindly consider citing our paper.
@article{wei2023llmrec,
title={LLMRec: Large Language Models with Graph Augmentation for Recommendation},
author={Wei, Wei and Ren, Xubin and Tang, Jiabin and Wang, Qinyong and Su, Lixin and Cheng, Suqi and Wang, Junfeng and Yin, Dawei and Huang, Chao},
journal={arXiv preprint arXiv:2311.00423},
year={2023}
}
The structure of this code is largely based on MMSSL, LATTICE, MICRO. Thank them for their work.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for LLMRec
Similar Open Source Tools

LLMRec
LLMRec is a PyTorch implementation for the WSDM 2024 paper 'Large Language Models with Graph Augmentation for Recommendation'. It is a novel framework that enhances recommenders by applying LLM-based graph augmentation strategies to recommendation systems. The tool aims to make the most of content within online platforms to augment interaction graphs by reinforcing u-i interactive edges, enhancing item node attributes, and conducting user node profiling from a natural language perspective.

dom-to-semantic-markdown
DOM to Semantic Markdown is a tool that converts HTML DOM to Semantic Markdown for use in Large Language Models (LLMs). It maximizes semantic information, token efficiency, and preserves metadata to enhance LLMs' processing capabilities. The tool captures rich web content structure, including semantic tags, image metadata, table structures, and link destinations. It offers customizable conversion options and supports both browser and Node.js environments.

vecs
vecs is a Python client for managing and querying vector stores in PostgreSQL with the pgvector extension. It allows users to create collections of vectors with associated metadata, index the collections for fast search performance, and query the collections based on specified filters. The tool simplifies the process of working with vector data in a PostgreSQL database, making it easier to store, retrieve, and analyze vector information.

continuous-eval
Open-Source Evaluation for LLM Applications. `continuous-eval` is an open-source package created for granular and holistic evaluation of GenAI application pipelines. It offers modularized evaluation, a comprehensive metric library covering various LLM use cases, the ability to leverage user feedback in evaluation, and synthetic dataset generation for testing pipelines. Users can define their own metrics by extending the Metric class. The tool allows running evaluation on a pipeline defined with modules and corresponding metrics. Additionally, it provides synthetic data generation capabilities to create user interaction data for evaluation or training purposes.

lmnr
Laminar is an all-in-one open-source platform designed for engineering AI products. It allows users to trace, evaluate, label, and analyze LLM data efficiently. The platform offers features such as automatic tracing of common AI frameworks and SDKs, local and online evaluations, simple UI for data labeling, dataset management, and scalability with gRPC communication. Laminar is built with a modern open-source stack including RabbitMQ, Postgres, Clickhouse, and Qdrant for semantic similarity search. It provides fast and beautiful dashboards for traces, evaluations, and labels, making it a comprehensive tool for AI product development.

ModelCache
Codefuse-ModelCache is a semantic cache for large language models (LLMs) that aims to optimize services by introducing a caching mechanism. It helps reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models. The project facilitates sharing and exchanging technologies related to large model semantic cache through open-source collaboration.

lancedb
LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering, and management of embeddings. The key features of LanceDB include: Production-scale vector search with no servers to manage. Store, query, and filter vectors, metadata, and multi-modal data (text, images, videos, point clouds, and more). Support for vector similarity search, full-text search, and SQL. Native Python and Javascript/Typescript support. Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index(*). Ecosystem integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB, and more on the way. LanceDB's core is written in Rust 🦀 and is built using Lance, an open-source columnar format designed for performant ML workloads.

ChatRex
ChatRex is a Multimodal Large Language Model (MLLM) designed to seamlessly integrate fine-grained object perception and robust language understanding. By adopting a decoupled architecture with a retrieval-based approach for object detection and leveraging high-resolution visual inputs, ChatRex addresses key challenges in perception tasks. It is powered by the Rexverse-2M dataset with diverse image-region-text annotations. ChatRex can be applied to various scenarios requiring fine-grained perception, such as object detection, grounded conversation, grounded image captioning, and region understanding.

lionagi
LionAGI is a powerful intelligent workflow automation framework that introduces advanced ML models into any existing workflows and data infrastructure. It can interact with almost any model, run interactions in parallel for most models, produce structured pydantic outputs with flexible usage, automate workflow via graph based agents, use advanced prompting techniques, and more. LionAGI aims to provide a centralized agent-managed framework for "ML-powered tools coordination" and to dramatically lower the barrier of entries for creating use-case/domain specific tools. It is designed to be asynchronous only and requires Python 3.10 or higher.

beyondllm
Beyond LLM offers an all-in-one toolkit for experimentation, evaluation, and deployment of Retrieval-Augmented Generation (RAG) systems. It simplifies the process with automated integration, customizable evaluation metrics, and support for various Large Language Models (LLMs) tailored to specific needs. The aim is to reduce LLM hallucination risks and enhance reliability.

langfair
LangFair is a Python library for bias and fairness assessments of large language models (LLMs). It offers a comprehensive framework for choosing bias and fairness metrics, demo notebooks, and a technical playbook. Users can tailor evaluations to their use cases with a Bring Your Own Prompts approach. The focus is on output-based metrics practical for governance audits and real-world testing.

modelfusion
ModelFusion is an abstraction layer for integrating AI models into JavaScript and TypeScript applications, unifying the API for common operations such as text streaming, object generation, and tool usage. It provides features to support production environments, including observability hooks, logging, and automatic retries. You can use ModelFusion to build AI applications, chatbots, and agents. ModelFusion is a non-commercial open source project that is community-driven. You can use it with any supported provider. ModelFusion supports a wide range of models including text generation, image generation, vision, text-to-speech, speech-to-text, and embedding models. ModelFusion infers TypeScript types wherever possible and validates model responses. ModelFusion provides an observer framework and logging support. ModelFusion ensures seamless operation through automatic retries, throttling, and error handling mechanisms. ModelFusion is fully tree-shakeable, can be used in serverless environments, and only uses a minimal set of dependencies.

mlx-llm
mlx-llm is a library that allows you to run Large Language Models (LLMs) on Apple Silicon devices in real-time using Apple's MLX framework. It provides a simple and easy-to-use API for creating, loading, and using LLM models, as well as a variety of applications such as chatbots, fine-tuning, and retrieval-augmented generation.

rl
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and **python-first** , low and high level abstractions for RL that are intended to be **efficient** , **modular** , **documented** and properly **tested**. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort.

StepWise
StepWise is a code-first, event-driven workflow framework for .NET designed to help users build complex workflows in a simple and efficient way. It allows users to define workflows using C# code, visualize and execute workflows from a browser, execute steps in parallel, and resolve dependencies automatically. StepWise also features an AI assistant called `Geeno` in its WebUI to help users run and analyze workflows with ease.

cappr
CAPPr is a tool for text classification that does not require training or post-processing. It allows users to have their language models pick from a list of choices or compute the probability of a completion given a prompt. The tool aims to help users get more out of open source language models by simplifying the text classification process. CAPPr can be used with GGUF models, Hugging Face models, models from the OpenAI API, and for tasks like caching instructions, extracting final answers from step-by-step completions, and running predictions in batches with different sets of completions.
For similar tasks

LLMRec
LLMRec is a PyTorch implementation for the WSDM 2024 paper 'Large Language Models with Graph Augmentation for Recommendation'. It is a novel framework that enhances recommenders by applying LLM-based graph augmentation strategies to recommendation systems. The tool aims to make the most of content within online platforms to augment interaction graphs by reinforcing u-i interactive edges, enhancing item node attributes, and conducting user node profiling from a natural language perspective.
For similar jobs

sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.