Scalable Sequential Recommendation under Latency and Memory Constraints
This addresses the challenge of scalable sequential recommendation for real-world applications where computational resources are limited, though it appears incremental as it builds on existing methods like Mamba-style models.
The paper tackles the problem of building sequential recommender systems that can model long-range user behavior under strict memory and latency constraints, resulting in HoloMambaRec, which outperforms SASRec and achieves competitive performance with GRU4Rec under a 10-epoch training budget while maintaining lower memory complexity.
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.