---
base_model:
- google/gemma-3-1b-it
pipeline_tag: text-generation
tags:
- recsys
- llm
- rl
---
# Model Card for Model ID

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<u>Re</u>commender by <u>Se</u>mantic-<u>I</u>D

We want to democratize Recommendation Systems. Bottlenecks lie at:
1. 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**.
2. 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**.
3. 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:
1) 1B-sized models achieve Prec@1=30%+/-10% for Beauty sector of the Amazon-2023 dataset. 
2) 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

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- **Developed by:** [Dat Ngo](), [Manoj C.]()
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## Uses

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## How to Get Started with the Model

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## Training Details

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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