Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·21d ago·5 min read
Dual-Interest Sequential Product Recommendation With Multi-Granular SSM
TL;DR
DSRec is a novel dual-interest sequential recommendation model that effectively captures item roles across different user contexts. It outperforms existing methods by utilizing long-term and short-term interest embeddings processed through specialized State Space Models.
✦ Why It Matters
Implement DSRec to enhance your product recommendation systems and improve user engagement today.
Key Takeaways
- Utilize DSRec for improved product recommendations.
- Incorporate long-term and short-term interest embeddings.
- Leverage distinct State Space Models for better context understanding.
- Test DSRec on your datasets to evaluate performance gains.
How It Works
DSRec separates user interests into long-term and short-term components. Long-term interests are captured through historical aggregation, while short-term interests focus on immediate session intent.
The model uses a full-sequence SSM for long-term data and a time-modulated SSM that adapts based on the timing of user clicks. This dual approach allows for a nuanced understanding of user behavior across different contexts.