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.
At least 2 A800 is required. Preferable 8 A800.
The main features of hestiasky/e4srec are: Fine-Tuned Recommendation Models.
Open-source alternatives to hestiasky/e4srec include: jeykigung/p5 — This repo presents implementation of the P5 large language model (LLM) for recommendation:. jyonn/once — All the recommendation experiments are conducted under our content based recommendation repository Legommenders. It… lileipisces/pepler — Lei Li, Yongfeng Zhang, Li Chen. Personalized Prompt Learning for Explainable Recommendation. ACM Transactions on… ljy0ustc/llara — 2024.7: We have resolved several bugs within our code. Below are the most recent results of LLaRA. rutgerswiselab/genrec — pip install -r requirements.txt. anord-wang/llm4rec — This ReadMe file contains the Python codes for the paper.
