Debiased Contrastive Learning for Sequential Recommendation

Yang, Yuhao; Huang, Chao; Xia, Lianghao; Huang, Chunzhen; Luo, Da; Lin, Kangyi

doi:10.1145/3543507.3583361

Cited by 41 publications

(4 citation statements)

References 33 publications

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“…The introduction of contrastive learning in sequence recommendation systems mainly solves the problems of sparse user-item interaction and noise. Scholars improve recommendation performance by designing auxiliary tasks or loss functions [39]. CBiT [40] combines the cloze task mask and the dropout mask to generate high-quality positive samples and perform multi-pair contrastive learning.…”

Section: Contrastive Learningmentioning

confidence: 99%

“…ICLRec [41] models user intentions through clustering of item sequences, maximizing the agreement between a view of the sequence and its corresponding intentions to improve recommendation performance. DCRec [39] employs contrastive learning to learn consistent perception enhancement representations from sequential pattern encoding and global collaborative relationship modeling.…”

Section: Contrastive Learningmentioning

confidence: 99%

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High-level preferences as positive examples in contrastive learning for multi-interest sequential recommendation

Zhu,

Li,

Liu

et al. 2024

World Wide Web

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The sequential recommendation task based on the multi-interest framework aims to model multiple interests of users from different aspects to predict their future interactions. However, researchers rarely consider the differences in features between the interests generated by the model. In extreme cases, all interest capsules have the same meaning, leading to the failure of modeling users with multiple interests. To address this issue, we propose the High-level Preferences as positive examples in Contrastive Learning for multi-interest Sequence Recommendation framework (HPCL4SR), which uses contrastive learning to distinguish differences in interests based on user item interaction information. In order to find high-quality comparative examples, this paper introduces the category information to construct a global graph, learning the association between categories for high-level preference interest of users. Then, a multi-layer perceptron is used to adaptively fuse the low-level preference interest features of the user's items and the high-level preference interest features of the categories. Finally, user multiinterest contrastive samples are obtained through item sequence information and corresponding categories, which are fed into contrastive learning to optimize model parameters and generate multi-interest representations that are more in line with the user sequence. In addition, when modeling the user's item sequence information, in order to increase the differentiation between item representations, 1 the category of the item is used to supervise the learning process. Extensive experiments on three real datasets demonstrate that our method outperforms existing multi-interest recommendation models.

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Section: Contrastive Learningmentioning

confidence: 99%

Section: Contrastive Learningmentioning

confidence: 99%

High-level preferences as positive examples in contrastive learning for multi-interest sequential recommendation

Zhu,

Li,

Liu

et al. 2024

World Wide Web

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“…Self-Supervised Learning for Recommendation. Recently, data augmentation with self-supervised learning (SSL) has emerged as a promising approach for mitigating the label scarcity and noise issue in recommender systems [30,32]. One important SSL paradigm is contrastive learning-based augmentation, where semantic-relevant instances are aligned with sampled positive pairs, while unrelated samples as negative pairs are pushed away.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

Graph Transformer for Recommendation

Xia²,

Ren³

et al. 2023

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

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This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture. We highlight the importance of high-quality data augmentation with relevant selfsupervised pretext tasks for improving performance. Towards this end, we propose a new approach that automates the self-supervision augmentation process through a rationale-aware generative SSL that distills informative user-item interaction patterns. The proposed recommender with Graph TransFormer (GFormer) that offers parameterized collaborative rationale discovery for selective augmentation while preserving global-aware user-item relationships. In GFormer, we allow the rationale-aware SSL to inspire graph collaborative filtering with task-adaptive invariant rationalization in graph transformer. The experimental results reveal that our GFormer has the capability to consistently improve the performance over baselines on different datasets. Several in-depth experiments further investigate the invariant rationale-aware augmentation from various aspects.

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“…Contrastive learning: The DCRec model by Yang Y. et al ( 2023 ) leverages debiased contrastive learning to counteract popularity bias and addressing the challenge of disentangling user conformity from genuine interest, focusing on user fairness. The TAGCL framework also capitalizes on the contrastive learning paradigm, ensuring item fairness by reducing biases in social tagging systems (Xu et al, 2023 ).…”

Section: Fairness In Gnn-based Recommender Systemsmentioning

confidence: 99%

Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks

Duricic,

Kowald,

Lacic

et al. 2023

Front. Big Data

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By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches using Graph Neural Networks (GNNs), have demonstrated great results in terms of recommendation accuracy. However, accuracy may not always be the most important criterion for evaluating recommender systems' performance, since beyond-accuracy aspects such as recommendation diversity, serendipity, and fairness can strongly influence user engagement and satisfaction. This review paper focuses on addressing these dimensions in GNN-based recommender systems, going beyond the conventional accuracy-centric perspective. We begin by reviewing recent developments in approaches that improve not only the accuracy-diversity trade-off but also promote serendipity, and fairness in GNN-based recommender systems. We discuss different stages of model development including data preprocessing, graph construction, embedding initialization, propagation layers, embedding fusion, score computation, and training methodologies. Furthermore, we present a look into the practical difficulties encountered in assuring diversity, serendipity, and fairness, while retaining high accuracy. Finally, we discuss potential future research directions for developing more robust GNN-based recommender systems that go beyond the unidimensional perspective of focusing solely on accuracy. This review aims to provide researchers and practitioners with an in-depth understanding of the multifaceted issues that arise when designing GNN-based recommender systems, setting our work apart by offering a comprehensive exploration of beyond-accuracy dimensions.

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Debiased Contrastive Learning for Sequential Recommendation

Cited by 41 publications

References 33 publications

High-level preferences as positive examples in contrastive learning for multi-interest sequential recommendation

High-level preferences as positive examples in contrastive learning for multi-interest sequential recommendation

Graph Transformer for Recommendation

Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks

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