Bid Farewell to Seesaw: Towards Accurate Long-tail Session-based Recommendation via Dual Constraints of Hybrid Intents

Researchers propose HID, a machine learning framework that resolves the long-standing accuracy-versus-diversity trade-off in session-based recommendation systems by using hybrid intent learning and dual constraint losses. The approach identifies and filters session-irrelevant noise in long-tail items, enabling systems to boost both recommendation accuracy and diversity simultaneously rather than sacrificing one for the other.

Session-based recommendation systems face a fundamental challenge: recommending niche items improves diversity but degrades prediction accuracy, creating what researchers call a 'see-saw effect.' This paper addresses a core machine learning problem that affects e-commerce platforms, content streaming services, and personalization engines across industries. The HID framework tackles this by distinguishing between genuine user preferences and noise within tail items through attribute-aware spectral clustering, enabling more intelligent constraint design during model training.

The research builds on growing recognition that recommendation systems must balance multiple objectives. Prior approaches attempted promoting tail items through weighting or sampling strategies, but these crude methods treated all tail items equally, contaminating recommendations with irrelevant items. By explicitly modeling intent at the session level—separating target intents from noise intents—HID enables more surgical optimization of both accuracy and diversity metrics.

For practitioners, this work matters because deployed recommendation systems often struggle with cold-start problems and under-serving niche content, while simultaneously disappointing users with inaccurate suggestions. The proposed dual-constraint loss function theoretically unifies diversity and accuracy objectives, offering a path for engineers to implement these improvements in existing models without architectural overhauls. The framework's plug-and-play design means it can enhance multiple underlying recommendation architectures.

The practical validation across multiple datasets and models suggests the approach transfers effectively across contexts. Future development should focus on computational efficiency at scale and real-world A/B testing in production systems where user satisfaction and business metrics ultimately determine success.

• →HID framework resolves the accuracy-diversity trade-off by identifying and constraining session-irrelevant noise in long-tail recommendations.
• →Hybrid intent learning using spectral clustering improves item-to-intent mapping accuracy and enables better discrimination of relevant versus irrelevant suggestions.
• →Dual constraint loss unifies diversity and accuracy objectives into a single training framework, eliminating the need for manual metric trade-offs.
• →The plug-and-play design allows integration with existing session-based recommendation models without requiring architectural changes.
• →Experimental validation across multiple datasets demonstrates state-of-the-art performance gains in both long-tail promotion and overall recommendation accuracy.