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The paper introduces HoloMambaRec, a sequential recommendation architecture designed to address the limitations of Transformer-based models by reducing computational complexity and memory footprint. HoloMambaRec combines holographic reduced representations for attribute-aware embedding with a selective state space encoder for efficient sequence processing. Experiments on Amazon Beauty and MovieLens-1M demonstrate that HoloMambaRec achieves competitive or state-of-the-art ranking performance compared to SASRec and GRU4Rec, while significantly reducing memory complexity.
HoloMambaRec offers a practical, memory-efficient alternative to Transformers for sequential recommendation, achieving strong accuracy with linear-time sequence processing via a Mamba-inspired architecture.
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 under a 10-epoch budget show that HoloMambaRec surpasses SASRec on both datasets, attains state-of-the-art ranking on MovieLens-1M, and trails only GRU4Rec on Amazon Beauty, all 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.
