Bid Farewell to Seesaw: Towards Accurate Long-tail Session-based Recommendation via Dual Constraints of Hybrid Intents
arXiv:2511.08378v4 Announce Type: replace-cross Abstract: Session-based recommendation (SBR) aims to predict anonymous users' next interaction based on their interaction sessions. In the practical recommendation scenario, low-exposure items constitute the majority of interactions, creating a long-tail distribution that severely compromises recommendation diversity. Existing approaches attempt to address this issue by promoting tail items but incur accuracy degradation, exhibiting a "see-saw" effect between long-tail and accuracy performance. We attribute such conflict to session-irrelevant noi
The continuous evolution of recommendation systems, particularly with long-tail item challenges, necessitates ongoing research to improve practical applications.
Improving recommendation accuracy for long-tail items is crucial for enhancing user experience and promoting content diversity on platforms, affecting creator economies and consumer engagement.
This research proposes a method to mitigate the 'see-saw' effect in session-based recommendation, potentially leading to more balanced and effective algorithms.
- · E-commerce platforms
- · Content creators
- · Users of recommendation systems
- · Platforms with poor long-tail recommendation
Recommendation systems will become more adept at surfacing niche content and products that align with user interests.
Increased discoverability of diverse items could lead to more robust and equitable creator economies.
Improved user satisfaction and engagement might drive greater platform loyalty and reduced churn.
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Read at arXiv cs.AI