Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models
arxiv(2024)
摘要
Sequential recommendation aims to estimate the dynamic user preferences and
sequential dependencies among historical user behaviors. Although
Transformer-based models have proven to be effective for sequential
recommendation, they suffer from the inference inefficiency problem stemming
from the quadratic computational complexity of attention operators, especially
for long-range behavior sequences. Inspired by the recent success of state
space models (SSMs), we propose Mamba4Rec, which is the first work to explore
the potential of selective SSMs for efficient sequential recommendation. Built
upon the basic Mamba block which is a selective SSM with an efficient
hardware-aware parallel algorithm, we incorporate a series of sequential
modeling techniques to further promote the model performance and meanwhile
maintain the inference efficiency. Experiments on two public datasets
demonstrate that Mamba4Rec is able to well address the effectiveness-efficiency
dilemma, and defeat both RNN- and attention-based baselines in terms of both
effectiveness and efficiency.
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