base_model:
- google/gemma-3-1b-it
pipeline_tag: text-generation
tags:
- recsys
- llm
- rl
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.
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- Developed by: Dat Ngo, Manoj C.
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