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Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
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Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. At their core, such approaches model transition probabilities between items in a sequence, whether through Markov chains, recurrent networks, or more recently, Transformers. However both old and new issues remain, including data-sparsity and noisy data; such issues can impair the performance, especially in complex, parameter-hungry models. In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues. Contrastive SSL constructs augmentations from unlabelled instances, where agreements among positive pairs are maximized. It is challenging to devise a contrastive SSL framework for a sequential recommendation, due to its discrete nature, correlations among items, and skewness of length distributions. To this end, we propose a novel framework, Contrastive Self-supervised Learning for sequential Recommendation (CoSeRec). We introduce two informative augmentation operators leveraging item correlations to create high-quality views for contrastive learning. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed method on improving model performance and the robustness against sparse and noisy data. Our implementation is available online at \url{https://github.com/YChen1993/CoSeRec}
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Cited by 9 Pith papers
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Quality-Aware Collaborative Multi-Positive Contrastive Learning for Sequential Recommendation
QCMP-CL introduces learnable collaborative sequence augmentation from same-target and similar sequences plus a quality-aware weighting mechanism based on augmentation confidence, outperforming prior CL-based sequentia...
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TSPORec: Token Selection via Preference Optimization for LLM-Based Sequential Recommendation
TSPORec learns to select informative tokens from item text for LLM-based sequential recommendation, improving accuracy slightly and reducing input length.
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Intent-aware Diffusion with Contrastive Learning for Sequential Recommendation
InDiRec generates intent-aligned augmented views with a conditional diffusion model and uses them in contrastive learning, outperforming prior sequential recommenders on five public datasets.
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Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation
IDCLRec disentangles user behaviors into interests and intents and applies intent-item contrastive learning to improve sequential recommendation accuracy beyond current baselines.
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Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRec
FENRec adds time-decayed future-item soft labels and anchor-mixed enduring hard negatives to contrastive sequential recommenders, improving next-item prediction on four datasets.
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MARS: Modality-Aligned Retrieval for Sequence Augmented CTR Prediction
MARS aligns text and image features of items, then augments sparse user sequences with retrieved sequences from similar heavy users, improving CTR prediction offline and online.
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SimDiffRec: Semantic Similarity-Guided Diffusion for Contrastive Sequential Recommendation
SimDiffRec augments user sequences by replacing items at high-confidence diffusion positions with the model's top prediction and using averaged similar-item embeddings as noise, reporting consistent but unverified gai...
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Scaling Sequential Recommendation Models with Transformers
Transformer-based sequential recommenders exhibit power-law and saturating NDCG scaling with model size and training interactions, enabling compute-aware model selection and effective pre-train/fine-tune transfer.
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Augmenting Sequential Recommendation with Balanced Relevance and Diversity
BASRec improves sequential recommendation by mixing original and edited sequences in representation space, plus cross-user mixing, reporting average gains up to 72% on GRU4Rec.
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