Semantic IDs for Recommender LLMs

Introduction

Traditional recommender systems use random hash IDs for items, limiting interpretability. Semantic IDs embed meaningful representations directly into LLMs, enabling natural language queries and explanations.

Key Concepts

• Hash IDs: Opaque identifiers (e.g., UUIDs) for lookup efficiency but poor human readability.
• Semantic Embeddings: Vector representations capturing item semantics (e.g., via BERT or Sentence Transformers).
• LLM Integration: Tokenize embeddings as inputs to LLMs for generation-based recommendations.

Implementation Steps

1. Generate Embeddings: Use a pre-trained model to embed item descriptions.

   from sentence_transformers import SentenceTransformer
   model = SentenceTransformer('all-MiniLM-L6-v2')
   embeddings = model.encode(['item description 1', 'item description 2'])
   

2. Tokenize for LLM: Convert embeddings to tokens compatible with your LLM (e.g., via projection layers).
3. Query Processing: Input natural language query + semantic tokens to LLM.

   import openai
   

Or any LLM API

response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Recommend movies like Inception"}],

Append semantic tokens here

)

4. **Generate Recommendations**: LLM outputs ranked items with explanations.

## Example

For a movie recommender: Embed plot summaries. Query: "Sci-fi thrillers?quot; LLM generates: "Based on semantic similarity to Blade Runner's dystopian themes, try Inception."

## Evaluation
- Metrics: Precision@K, NDCG for accuracy; user studies for interpretability.
- Trade-offs: Higher compute for embeddings vs. better UX.

## Conclusion

Semantic IDs bridge LLMs and recommenders for intuitive, explainable systems. Experiment with open-source embedding models for your domain.