EasyRec-Base
Overview
- Description: EasyRec is a series of language models designed for recommendations, trained to match the textual profiles of users and items with collaborative signals.
- Usage: You can use EasyRec to encode user and item text embeddings based on the textual profiles that reflect their preferences for various recommendation scenarios.
- Evaluation: We evaluate the performance of EasyRec in: (i) Text-based zero-shot recommendation and (ii) Text-enhanced collaborative filtering.
- Finetuned from model: EasyRec is finetuned from RoBERTa within English.
For details please refer [💻GitHub Code] and [📖Paper].
Model List
We release a series of EasyRec checkpoints with varying sizes. You can easily load these models from Hugging Face by replacing the model name.
| Model | Size | Parameters | Recall@10 on Movies |
|---|---|---|---|
| jibala-1022/easyrec-small | 243 MB | 121,364,313 | 0.0086 |
| jibala-1022/easyrec-base | 328 MB | 163,891,545 | 0.0166 |
| jibala-1022/easyrec-large | 816 MB | 407,933,017 | 0.0166 |
🌟 Citation
@article{ren2024easyrec,
title={EasyRec: Simple yet Effective Language Models for Recommendation},
author={Ren, Xubin and Huang, Chao},
journal={arXiv preprint arXiv:2408.08821},
year={2024}
}