Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision
CoRR(2024)
摘要
In Sequential Recommenders (SR), encoding and utilizing modalities in an
end-to-end manner is costly in terms of modality encoder sizes. Two-stage
approaches can mitigate such concerns, but they suffer from poor performance
due to modality forgetting, where the sequential objective overshadows modality
representation. We propose a lightweight knowledge distillation solution that
preserves both merits: retaining modality information and maintaining high
efficiency. Specifically, we introduce a novel method that enhances the
learning of embeddings in SR through the supervision of modality correlations.
The supervision signals are distilled from the original modality
representations, including both (1) holistic correlations, which quantify their
overall associations, and (2) dissected correlation types, which refine their
relationship facets (honing in on specific aspects like color or shape
consistency). To further address the issue of modality forgetting, we propose
an asynchronous learning step, allowing the original information to be retained
longer for training the representation learning module. Our approach is
compatible with various backbone architectures and outperforms the top
baselines by 6.8
original feature associations from modality encoders significantly boosts
task-specific recommendation adaptation. Additionally, we find that larger
modality encoders (e.g., Large Language Models) contain richer feature sets
which necessitate more fine-grained modeling to reach their full performance
potential.
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