• Corpus ID: 235755396

SelfCF: A Simple Framework for Self-supervised Collaborative Filtering

@article{Zhou2021SelfCFAS,
  title={SelfCF: A Simple Framework for Self-supervised Collaborative Filtering},
  author={Xin Zhou and Aixin Sun and Yong Liu and Jie Zhang and Chunyan Miao},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.03019}
}
Collaborative filtering (CF) is widely used to learn an informative latent representation of a user or item from observed interactions. Existing CF-based methods commonly adopt negative sampling to discriminate different items. That is, observed user-item pairs are treated as positive instances; unobserved pairs are considered as negative instances and are sampled under a defined distribution for training. Training with negative sampling on large datasets is computationally expensive. Further… 

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Graph Augmentation-Free Contrastive Learning for Recommendation
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  • Computer Science
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A graph augmentation-free CL method to simply adjust the uniformity of the learned user/item representation distributions on the unit hypersphere by adding uniform noises to the original representations for data augmentations, and enhance recommendation from a geometric view is proposed.

References

SHOWING 1-10 OF 49 REFERENCES
Bootstrapping User and Item Representations for One-Class Collaborative Filtering
TLDR
This paper proposes a novel OCCF framework, named as BUIR, which does not require negative sampling, and adopts two distinct encoder networks that learn from each other; the first encoder is trained to predict the output of the secondEncoder as its target, while the second encoder provides the consistent targets by slowly approximating the firstEncoder.
Collaborative Deep Learning for Recommender Systems
TLDR
A hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix is proposed, which can significantly advance the state of the art.
Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation
TLDR
This work proposes a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation that can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data with a rather low time complexity.
Neural Graph Collaborative Filtering
TLDR
This work develops a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it, effectively injecting the collaborative signal into the embedding process in an explicit manner.
Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation
TLDR
This paper designs a new ideal framework named Efficient Non-Sampling Factorization Machines (ENSFM), which not only seamlessly connects the relationship between FM and Matrix Factorization (MF), but also resolves the challenging efficiency issue via novel memorization strategies.
NAIS: Neural Attentive Item Similarity Model for Recommendation
TLDR
This work proposes a neural network model named Neural Attentive Item Similarity model (NAIS), which is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems.
Factorization meets the neighborhood: a multifaceted collaborative filtering model
TLDR
The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model and a new evaluation metric is suggested, which highlights the differences among methods, based on their performance at a top-K recommendation task.
Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation
TLDR
A novel Jointly Non-Sampling learning model for Knowledge graph enhanced Recommendation (JNSKR), which not only models the fine-grained connections among users, items, and entities, but also efficiently learns model parameters from the whole training data with a rather low time complexity.
Collaborative Filtering beyond the User-Item Matrix
TLDR
A comprehensive introduction to a large body of research, more than 200 key references, is provided, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix.
BPR: Bayesian Personalized Ranking from Implicit Feedback
TLDR
This paper presents a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem and provides a generic learning algorithm for optimizing models with respect to B PR-Opt.
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