Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation
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Abstract (arXiv)
Sequential Recommendation aims to predict the next item based on user behaviour. Recently, Self-Supervised Learning (SSL) has been proposed to improve recommendation performance. However, most of existing SSL methods use a uniform data augmentation scheme, which loses the sequence correlation of an original sequence. To this end, in this paper, we propose a Learnable Model Augmentation self-supervised learning for sequential Recommendation (LMA4Rec). Specifically, LMA4Rec first takes model augmentation as a supplementary method for data augmentation to generate views. Then, LMA4Rec uses learnable Bernoulli dropout to implement model augmentation learnable operations. Next, self-supervised learning is used between the contrastive views to extract self-supervised signals from an original sequence. Finally, experiments on three public datasets show that the LMA4Rec method effectively improves sequential recommendation performance compared with baseline methods.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 25 references that are in the map. First posted on arXiv 21 Apr 2022.
Builds on
18 of 25 references.
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6 more
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