Scalable Sequential Recommendation under Latency and Memory Constraints

• URL: http://arxiv.org/abs/2601.08360v1
• Date: Tue, 13 Jan 2026 09:16:49 GMT
• Title: Scalable Sequential Recommendation under Latency and Memory Constraints
• Authors: Adithya Parthasarathy, Aswathnarayan Muthukrishnan Kirubakaran, Vinoth Punniyamoorthy, Nachiappan Chockalingam, Lokesh Butra, Kabilan Kannan, Abhirup Mazumder, Sumit Saha,
• Abstract summary: Sequential recommender systems must model long-range user behavior while operating under strict memory and latency constraints.<n> Transformer-based approaches achieve strong accuracy but suffer from quadratic attention complexity.<n>This paper presents HoloMambaRec, a lightweight sequential recommendation architecture that combines holographic reduced representations for attribute-aware embedding.
• Score: 0.14053129774629072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential recommender systems must model long-range user behavior while operating under strict memory and latency constraints. Transformer-based approaches achieve strong accuracy but suffer from quadratic attention complexity, forcing aggressive truncation of user histories and limiting their practicality for long-horizon modeling. This paper presents HoloMambaRec, a lightweight sequential recommendation architecture that combines holographic reduced representations for attribute-aware embedding with a selective state space encoder for linear-time sequence processing. Item and attribute information are bound using circular convolution, preserving embedding dimensionality while encoding structured metadata. A shallow selective state space backbone, inspired by recent Mamba-style models, enables efficient training and constant-time recurrent inference. Experiments on Amazon Beauty and MovieLens-1M datasets demonstrate that HoloMambaRec consistently outperforms SASRec and achieves competitive performance with GRU4Rec under a constrained 10-epoch training budget, while maintaining substantially lower memory complexity. The design further incorporates forward-compatible mechanisms for temporal bundling and inference-time compression, positioning HoloMambaRec as a practical and extensible alternative for scalable, metadata-aware sequential recommendation.

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