How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
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.
This repo presents implementation of the P5 large language model (LLM) for recommendation:
The main features of jeykigung/p5 are: Fine-Tuned Recommendation Models, Recommendation Models.
Projects with overlapping indexed features include: hestiasky/e4srec — At least 2 A800 is required. Preferable 8 A800. 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.
