Model 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 to our 💻[GitHub Code] and 📖[Paper]. Here is an example code snippet to utilize EasyRec for encoding **text embeddings** based on user and item profiles for recommendations.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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| Source / channel | Input | Output | Billing unit | Collected at |
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。