SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks
arXiv:2606.09888v1 Announce Type: new Abstract: Linear attention provides an efficient backbone for long-sequence recommendation by avoiding the quadratic cost of standard Transformers, but its compressed recurrent state can be dominated by repetitive behavior patterns. We identify this phenomenon as semantic state sink, where recurring semantics over-occupy the recurrent state and bias subsequent readouts. To mitigate semantic state sink, we propose SinkRec, a hybrid memory-transition looped architecture that decouples collaborative behavioral pattern storage from dynamic transition modeling.
The increasing demand for long-sequence information processing in AI, especially for real-world recommendation systems, highlights the current limitations of Transformer architectures.
Improving long-sequence recommendation accuracy by mitigating 'semantic state sink' directly enhances the performance and utility of AI systems in various applications, particularly those requiring nuanced understanding of user behavior over time.
This research introduces a novel architectural approach that allows AI models to better distinguish between repetitive and dynamic behavioral patterns, leading to more accurate and less biased predictions in recommendation systems.
- · E-commerce platforms
- · Content streaming services
- · AI developers
- · Personalized recommendation systems
- · Legacy recommendation algorithms
- · Linear attention models without 'sink' mitigation
More relevant and engaging user experiences across platforms powered by AI recommendations.
Increased user engagement and stickiness on platforms adopting this architectural improvement, potentially leading to higher revenue.
The development of even more sophisticated AI agents that can maintain long-term contextual understanding without losing fidelity due to repetitive inputs.
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Read at arXiv cs.LG