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This repo presents implementation of the P5 large language model (LLM) for recommendation:
All the recommendation experiments are conducted under our content based recommendation repository Legommenders. It involves a set of news recommenders and click-through rate prediction models. It is a modular-design framework, supporting the integration with pretrained language models (PLMs)…
Lei Li, Yongfeng Zhang, Li Chen. Personalized Prompt Learning for Explainable Recommendation. ACM Transactions on Information Systems (TOIS), 2023.
2024.7: We have resolved several bugs within our code. Below are the most recent results of LLaRA.
This is the implementatino of our work A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
This repository releases the code of paper UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation (ACL-2023 Short Paper).
Quick links: 📋Talk | 🗃️Dataset | 📭Citation | 🛠️Reproduce |
The code of AAAI'24 paper GLRec. Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
These codes are associated with the following paper [pdf](https://arxiv.org/abs/2311.01343):
This repository is the PyTorch impelementation for the PGAI@CIKM 2023 paper LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking Paper.
This repository is constructed based on CoLLM! Read CoLLM "readme.md" to understand the code structure!
Yang Zhang, Fuli Feng, Jizhi Zhang, Keqin Bao, Qifan Wang and Xiangnan He.