Model Card for Model ID
Recommender by Semantic-ID
We want to democratize Recommendation Systems. Bottlenecks lie at:
- Cold-start problems (new users or new items) deteriorates the system performance due to swift changes of customer's preferences. Current cold-start solutions include of hasing new product ids or frequently re-training models. Instead, we propose to leverage massive prior knowledge and reasoning ability of LLMs.
- Advanced feature engineering techniques are compulsury to convert raw input to preferred signals (e.g., transactions to purchase frequency) and limiting the rec-sys adoption. We attempt to replace feature-engineering with LLM's reasoning over text input.
- Different input types and domains require different feature-engineering techniques. You have to repeat these practices 10 times for 10 differnet projects.
Results show that:
- 1B-sized models achieve Prec@1=30%+/-10% for Beauty sector of the Amazon-2023 dataset.
- Wihout SFT, models accept product titles as raw inputs and yiels sufficient results. This ability eliminates need of advanced feature-engineering, a common practice in recommendation system, and allows anyone to quickly and easily deploy rec-sys.
Model Details
Model Description
- Developed by: Dat Ngo, Manoj C.
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]