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Self-supervised Learning for Large-scale Item Recommendations [article]

Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi Kang, Evan Ettinger
2021 arXiv   pre-print
for large-scale item recommendations.  ...  Large scale recommender models find most relevant items from huge catalogs, and they play a critical role in modern search and recommendation systems.  ...  CONCLUSION In this paper, we proposed a model architecture agnostic self-supervised learning (SSL) framework for large-scale neural recommender models.  ... 
arXiv:2007.12865v4 fatcat:euu7phtharckdbwki3cfceqmq4

Bootstrap Latent Representations for Multi-modal Recommendation [article]

Xin Zhou, Hongyu Zhou, Yong Liu, Zhiwei Zeng, Chunyan Miao, Pengwei Wang, Yuan You, Feijun Jiang
2022 arXiv   pre-print
To tackle the above issues, we propose a novel self-supervised multi-modal recommendation model, dubbed BM3, which requires neither augmentations from auxiliary graphs nor negative samples.  ...  which increases the computational cost on large graphs and may also bring noisy supervision signals into the training process.  ...  The authors would like to express thanks to Professor Aixin Sun from Nanyang Technological University for his valuable suggestions.  ... 
arXiv:2207.05969v1 fatcat:kzsbdqc2oncvrlvuz22o64wecq

Efficient Graph Collaborative Filtering via Contrastive Learning

Zhiqiang Pan, Honghui Chen
2021 Sensors  
Moreover, we introduce contrastive learning to enhance the representation learning of users and items by deriving the self-supervisions, which is jointly trained with the supervised learning.  ...  Collaborative filtering (CF) aims to make recommendations for users by detecting user's preference from the historical user–item interactions.  ...  Acknowledgments: The authors thank the editor, and the anonymous reviewers for their valuable suggestions that have significantly improved this study.  ... 
doi:10.3390/s21144666 fatcat:2c72yc4pvnem7lsosy3thdqohe

Self-Supervised Learning for Recommender System

Chao Huang, Xiang Wang, Xiangnan He, Dawei Yin
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
overload and suggest items for users.  ...  In this tutorial, we aim to provide a systemic review of existing self-supervised learning frameworks and analyze the corresponding challenges for various recommendation scenarios, such as general collaborative  ...  Furthermore, we have published several papers on the topic of self-supervised learning for recommendation.  ... 
doi:10.1145/3477495.3532684 fatcat:ifyz3k66nzalvnlr5ltbf2q3cu

Learning Discrete Hashing Towards Efficient Fashion Recommendation

Luyao Liu, Xingzhong Du, Lei Zhu, Fumin Shen, Zi Huang
2018 Data Science and Engineering  
In detail, this learning process is supervised by a clothing matching matrix, which is initially constructed based on limited known matching pairs and subsequently on the self-augmented ones.  ...  The binary representation learning significantly reduces the memory cost and accelerates the recommendation speed.  ...  , and thus support large-scale fashion recommendation.  ... 
doi:10.1007/s41019-018-0079-z fatcat:6i73qjgdovdddnyff3vnsgjbbu

Self-Supervised Graph Co-Training for Session-based Recommendation [article]

Xin Xia, Hongzhi Yin, Junliang Yu, Yingxia Shao, Lizhen Cui
2021 arXiv   pre-print
However, existing self-supervised recommendation models mainly rely on item/segment dropout to augment data, which are not fit for session-based recommendation because the dropout leads to sparser data  ...  In this paper, for informative session-based data augmentation, we combine self-supervised learning with co-training, and then develop a framework to enhance session-based recommendation.  ...  Self-Supervised Graph Co-Training In this section, the proposed self-supervised graph CO-Training framework for session-based RECommendation (COTREC) is presented.  ... 
arXiv:2108.10560v1 fatcat:ueds2mnk6be3pd5x3eujuyb6qe

Semi-Supervised Visual Representation Learning for Fashion Compatibility [article]

Ambareesh Revanur, Vijay Kumar, Deepthi Sharma
2021 arXiv   pre-print
For each labeled outfit in a training batch, we obtain a pseudo-outfit by matching each item in the labeled outfit with unlabeled items.  ...  In this work, we propose a semi-supervised learning approach where we leverage large unlabeled fashion corpus to create pseudo-positive and pseudo-negative outfits on the fly during training.  ...  As image attributes such as colour, shape and texture play a big role for compatibility, we additionally introduce self-supervised consistency regularization [2] on unlabeled images to explicitly learn  ... 
arXiv:2109.08052v1 fatcat:eg7h26gx2vfqln3l726lgsrabu

MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction [article]

Wei Guo, Can Zhang, Zhicheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Ruiming Tang, Xiuqiang He, Rui Zhang
2022 arXiv   pre-print
CTR prediction is essential for modern recommender systems.  ...  To address these challenging problems, we propose a novel Multi-Interest Self-Supervised learning (MISS) framework which enhances the feature embeddings with interest-level self-supervision signals.  ...  In large-scale item recommendations, an auxiliary SSL task is employed to explore feature correlations by applying different feature masking patterns [36] .  ... 
arXiv:2111.15068v2 fatcat:blu7sutjcze7tn5xb7mhkm46za

Self-supervised Graph Learning for Recommendation [article]

Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, Xing Xie
2020 arXiv   pre-print
In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation.  ...  The idea is to supplement the classical supervised task of recommendation with an auxiliary self-supervised task, which reinforces node representation learning via self-discrimination.  ...  This work represents an initial attempt to exploit self-supervised learning for recommendation and opens up new research possibilities.  ... 
arXiv:2010.10783v1 fatcat:vaujjxnjdndcvcca4icqjdprye

A Text-based Deep Reinforcement Learning Framework Using Self-supervised Graph Representation for Interactive Recommendation

Chaoyang Wang, Zhiqiang Guo, Jianjun Li, Guohui Li, Peng Pan
2021 ACM/IMS Transactions on Data Science  
To address these two problems, in this article, we propose a T ext-based deep R einforcement learning framework using self-supervised G raph representation for I nteractive R ecommendation (TRGIR).  ...  Specifically, we leverage textual information to map items and users into a same feature space by a self-supervised embedding method based on the graph convolutional network, which greatly alleviates data  ...  For Top-k recommendation, TRGIR-DQN has to calculate Q-values for each item to decide whether to recommend or not, which is still inefficient when the scale of the candidate set is large.  ... 
doi:10.1145/3522596 fatcat:k5fajvd3frey7fxzasz3h2pwdi

Socially-Aware Self-Supervised Tri-Training for Recommendation

Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, Nguyen Quoc Viet Hung
2021 Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining  
Self-supervised learning (SSL), which can automatically generate ground-truth samples from raw data, holds vast potential to improve recommender systems.  ...  Since the tri-training operates on the augmented views of the same data sources for self-supervision signals, we name it self-supervised tri-training.  ...  [37] propose a two-tower DNN architecture with uniform feature masking and dropout for self-supervised item recommendation. Ma et al.  ... 
doi:10.1145/3447548.3467340 fatcat:bria3dcw4jf5pb6ktqp5kmqeay

Socially-Aware Self-Supervised Tri-Training for Recommendation [article]

Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, Nguyen Quoc Viet Hung
2021 arXiv   pre-print
Self-supervised learning (SSL), which can automatically generate ground-truth samples from raw data, holds vast potential to improve recommender systems.  ...  Since the tri-training operates on the augmented views of the same data sources for self-supervision signals, we name it self-supervised tri-training.  ...  DP190101985) and ARC Training Centre for Information Resilience (Grant No. IC200100022).  ... 
arXiv:2106.03569v4 fatcat:aflejgtdi5gmlei7e25urp6bse

An improved competence rating scale for CBT Supervision: Short-SAGE

Robert P. Reiser, Tom Cliffe, Derek L. Milne
2018 The Cognitive Behaviour Therapist  
These conceptual and practical improvements strengthen the role of SAGE as a promising observational instrument for evaluating CBT supervision, complementing self-report assessments of competent CBT supervision  ...  This paper reports the factor analysis of a promising 23-item instrument for observing competence in CBT supervision (Supervision: Adherence and Guidance Evaluation: SAGE).  ...  Authors also confirm that ethical approval was granted by the Palo Alto University IRB for this research. Financial support The authors received no financial support for preparing this manuscript.  ... 
doi:10.1017/s1754470x18000065 fatcat:jrev4ioss5cepkytwl4rucbede

Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation

Mingyue Cheng, Zhiding Liu, Qi Liu, Shenyang Ge, Enhong Chen
2022 Proceedings of the ACM Web Conference 2022  
Recent years have witnessed great success in deep learning-based sequential recommendation (SR), which can provide more timely and accurate recommendations.  ...  ., prevalent self-attentive and convolutional operations, for capturing long-and short-range dependence of sequential behaviors.  ...  Thus their speed is limited in both training and evaluating in large-scale recommendation scenarios.  ... 
doi:10.1145/3485447.3512066 fatcat:ovatst6xejhfxcnp5uuqykjisy

Contrastive Learning for Sequential Recommendation [article]

Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Bolin Ding, Bin Cui
2021 arXiv   pre-print
CL4SRec not only takes advantage of the traditional next item prediction task but also utilizes the contrastive learning framework to derive self-supervision signals from the original user behavior sequences  ...  To tackle that, inspired by recent advances of contrastive learning techniques in the computer version, we propose a novel multi-task model called Contrastive Learning for Sequential Recommendation (CL4SRec  ...  When it comes to the field of recommendation systems, some works apply self-supervised learning to improve recommendation performance. For example, Xin et al.  ... 
arXiv:2010.14395v2 fatcat:2pissecqs5dopo5nderaunptyq
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