Self-Guided Learning to Denoise for Robust Recommendation

Gao, Yunjun; Du, Yuntao; Hu, Yujia; Chen, Lu; Xinjun, Zhu,; Fang, Ziquan; Zheng, Baihua

doi:10.1145/3477495.3532059

Cited by 42 publications

(9 citation statements)

References 23 publications

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“…The data sets were randomly divided into training, validation, and test sets in an 8:1:1 ratio. Meanwhile, we employed two advanced and effective evaluation metrics that are widely used in top conferences [31,33,36,43]-namely Recall@K and NDCG@K (normalized discounted cumulative gain)-to assess the model performance. In our SSGCL model, we stacked three GNN layers for the influence diffusion, and the embedding size for both users and items was set to 64.…”

Section: Methodsmentioning

confidence: 99%

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

Duan,

Wang,

Zhong

2024

Mathematics

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As user–item interaction information is typically limited, collaborative filtering (CF)-based recommender systems often suffer from the data sparsity issue. To address this issue, recent recommender systems have turned to graph neural networks (GNNs) due to their superior performance in capturing high-order relationships. Furthermore, some of these GNN-based recommendation models also attempt to incorporate other information. They either extract self-supervised signals to mitigate the data sparsity problem or employ social information to assist with learning better representations under a social recommendation setting. However, only a few methods can take full advantage of these different aspects of information. Based on some testing, we believe most of these methods are complex and redundantly designed, which may lead to sub-optimal results. In this paper, we propose SSGCL, which is a recommendation system model that utilizes both social information and self-supervised information. We design a GNN-based propagation strategy that integrates social information with interest information in a simple yet effective way to learn user–item representations for recommendations. In addition, a specially designed contrastive learning module is employed to take advantage of the self-supervised signals for a better user–item representation distribution. The contrastive learning module is jointly optimized with the recommendation module to benefit the final recommendation result. Experiments on several benchmark data sets demonstrate the significant improvement in performance achieved by our model when compared with baseline models.

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Section: Methodsmentioning

confidence: 99%

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

Duan,

Wang,

Zhong

2024

Mathematics

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“…There are also some works to solve this problem through causal inference [16]. To deal with this problem of learning from noisy labels [37], existing works adopt techniques like adding graphbased priors [39], cross-model agreements [48] and graph sparsification [17] to denoise observed user-item interaction data. Different from above works, we focus on denoising social network, i.e., useruser relation data, so as to leverage power of social homophily and social influence in a more effective and efficient way.…”

Section: Denoising For Recommendationmentioning

confidence: 99%

Robust Preference-Guided Denoising for Graph based Social Recommendation

Quan

Ding

Gao

et al. 2023

Proceedings of the ACM Web Conference 2023

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Graph Neural Network (GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations. However, in terms of both effectiveness and efficiency of recommendation, a large portion of social relations can be redundant or even noisy, e.g., it is quite normal that friends share no preference in a certain domain. Existing models do not fully solve this problem of relation redundancy and noise, as they directly characterize social influence over the full social network. In this paper, we instead propose to improve graph based social recommendation by only retaining the informative social relations to ensure an efficient and effective influence diffusion, i.e., graph denoising. Our designed denoising method is preference-guided to model social relation confidence and benefits user preference learning in return by providing a denoised but more informative social graph for recommendation models. Moreover, to avoid interference of noisy social relations, it designs a self-correcting curriculum learning module and an adaptive denoising strategy, both favoring highly-confident samples. Experimental results on three public datasets demonstrate its consistent capability of improving two state-of-the-art social recommendation models by robustly removing 10-40% of original relations. We release the source code at https://github.com/tsinghua-fib-lab/Graph-Denoising-SocialRec.

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“…Wang et al [43] assume that predictions on noisy items vary across different recommendation models and propose an ensemble method to minimize the KL-divergence between the two models' predictions. Gao et al [8] argue that the models are prone to memorize easy and clean patterns at the early stage of training, so they collect memorized interactions at the early stage as guidance for the following training.…”

Section: Denoising Recommendationmentioning

confidence: 99%

A Self-Correcting Sequential Recommender

Lin

Wang

Chen

et al. 2023

Proceedings of the ACM Web Conference 2023

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Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user's historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations.To tackle the two issues listed above, we propose STEAM, a Self-correcTing sEquentiAl recoMmender. STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items. It then uses the corrected item sequence to train a recommender and make the next item prediction. We design an item-wise corrector that can adaptively select one type of operation for each item in the sequence. The operation types are 'keep', 'delete' and 'insert. ' In order to train the item-wise corrector without requiring additional labeling, we design two self-supervised learning mechanisms: (i) deletion correction (i.e., deleting randomly inserted items), and (ii) insertion correction (i.e., predicting randomly deleted items). We integrate the corrector with the recommender by sharing the encoder and by training them jointly. We conduct extensive experiments on three real-world datasets and the experimental results demonstrate that STEAM outperforms state-of-the-art sequential recommendation baselines. Our in-depth analyses confirm that STEAM benefits from learning to correct the raw item sequences.

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Self-Guided Learning to Denoise for Robust Recommendation

Cited by 42 publications

References 23 publications

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

Robust Preference-Guided Denoising for Graph based Social Recommendation

A Self-Correcting Sequential Recommender

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