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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)…
This is the implementatino of our work A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
The main features of sai990323/grounding4rec are: Fine-Tuned Recommendation Models.
Open-source alternatives to sai990323/grounding4rec include: hestiasky/e4srec — At least 2 A800 is required. Preferable 8 A800. 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. anord-wang/llm4rec — This ReadMe file contains the Python codes for the paper.
