Position-Aware Sequential Attention for Accurate Next Item Recommendations
#Position-Aware Sequential Attention #Next Item Recommendations #Self-Attention Mechanism #Positional Embeddings #Temporal Order #Sequential Data Processing #Kernelized Attention #Information Retrieval
📌 Key Takeaways
- Researchers developed a novel kernelized self-attention mechanism for sequential data processing
- Current models struggle with effectively capturing temporal order due to additive positional embeddings
- The new approach disentangles positional information from item semantics
- Experiments showed consistent improvement over existing baselines
- The method enables adaptive multi-scale sequential modeling
📖 Full Retelling
Researchers Timur Nabiev and Evgeny Frolov introduced a novel kernelized self-attention mechanism for sequential data processing in their paper 'Position-Aware Sequential Attention for Accurate Next Item Recommendations' submitted to arXiv on February 24, 2026, addressing limitations in current attention models that struggle with effectively capturing temporal order in sequence data. The researchers identified that conventional sequential self-attention models typically rely on additive positional embeddings, which inject positional information into item representations at the input stage. However, they argue this approach makes attention mechanisms only superficially sensitive to sequence order, as positional information becomes entangled with item embedding semantics, propagates weakly in deep architectures, and limits the ability to capture rich sequential patterns. To overcome these limitations, the researchers developed a kernelized self-attention mechanism where a learnable positional kernel operates purely in position space, disentangled from semantic similarity, and directly modulates attention weights. When applied per attention block, this kernel enables adaptive multi-scale sequential modeling. The researchers demonstrated through experiments on standard next-item prediction benchmarks that their positional kernel attention consistently outperformed strong competing baselines.
🏷️ Themes
Information Retrieval, Artificial Intelligence, Machine Learning, Sequential Modeling
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Original Source
--> Computer Science > Information Retrieval arXiv:2602.21052 [Submitted on 24 Feb 2026] Title: Position-Aware Sequential Attention for Accurate Next Item Recommendations Authors: Timur Nabiev , Evgeny Frolov View a PDF of the paper titled Position-Aware Sequential Attention for Accurate Next Item Recommendations, by Timur Nabiev and 1 other authors View PDF HTML Abstract: Sequential self-attention models usually rely on additive positional embeddings, which inject positional information into item representations at the input. In the absence of positional signals, the attention block is permutation-equivariant over sequence positions and thus has no intrinsic notion of temporal order beyond causal masking. We argue that additive positional embeddings make the attention mechanism only superficially sensitive to sequence order: positional information is entangled with item embedding semantics, propagates weakly in deep architectures, and limits the ability to capture rich sequential patterns. To address these limitations, we introduce a kernelized self-attention mechanism, where a learnable positional kernel operates purely in the position space, disentangled from semantic similarity, and directly modulates attention weights. When applied per attention block, this kernel enables adaptive multi-scale sequential modeling. Experiments on standard next-item prediction benchmarks show that our positional kernel attention consistently improves over strong competing baselines. Subjects: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2602.21052 [cs.IR] (or arXiv:2602.21052v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2602.21052 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Timur Nabiev [ view email ] [v1] Tue, 24 Feb 2026 16:09:47 UTC (82 KB) Full-text links: Access Paper: View a PDF of the paper titled Position-Aware Sequential Attention for Accurate Ne...
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