CoFiGAN: Collaborative filtering by generative and discriminative training for one-class recommendation

Liu, Jixiong; Pan, Weike; Ming, Zhong

doi:10.1016/j.knosys.2019.105255

Cited by 25 publications

(7 citation statements)

References 25 publications

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“…Alternatively, implicit useritem interactions, formally defined as a binary state using 1 to indicate the existence of the interaction (such as click, browse, or watch) between a user and an item, and 0 otherwise, occur more frequently and are easier to be accessed than explicit interactions due to the no requirement for user's additional operations like giving ratings for implicit ones. However, implicit user-item interactions can't directly carry user's preferences for items; thus brings the so-called one-class problem [85], which has been tried to surmount by such as converting implicit user-item interactions to explicit ones [86,87].…”

Section: Informationmentioning

confidence: 99%

Recommender systems based on graph embedding techniques: A comprehensive review

Deng

2021

Preprint

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Recommender systems, a pivotal tool to alleviate the information overload problem, aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for tackling the sparsity and cold start problems encountered by recommender systems, uncovering hidden (indirect) user-item relations by employing side information and knowledge to enrich observed information for the recommendation has been proven promising recently; and its performance is largely determined by the scalability of recommendation models in the face of the high complexity and large scale of side information and knowledge. Making great strides towards efficiently utilizing complex and large-scale data, research into graph embedding techniques is a major topic. Equipping recommender systems with graph embedding techniques contributes to outperforming the conventional recommendation implementing directly based on graph topology analysis and has been widely studied these years. This article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs, and knowledge graphs, and proposes a general design pipeline of that. In addition, comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models, on simulations, manifests that the conventional models overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the relative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and the conventional recommendation in different tasks as well as some open questions.

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Section: Informationmentioning

confidence: 99%

Recommender systems based on graph embedding techniques: A comprehensive review

Deng

2021

Preprint

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“…For personalised recommendation tasks, the generator model is mainly regarded as the negative sampler for the ranker. Hence, GAN-based methods have often been explored in the context of the negative sampling strategies [23,26,40,60,62,70]. On the other hand, since GANs originally aim to estimate the generative distribution of observed data, when using the generator as the ranker model, GANbased methods are more like the DE approach; the softmax model is widely adopted for the generator.…”

Section: Negative Sampling and Generative Adversarial Networkmentioning

confidence: 99%

Scalable Personalised Item Ranking through Parametric Density Estimation

Togashi,

Kato,

Otani

et al. 2021

Preprint

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Learning from implicit feedback is challenging because of the difficult nature of the one-class problem: we can observe only positive examples. Most conventional methods use a pairwise ranking approach and negative samplers to cope with the one-class problem. However, such methods have two main drawbacks particularly in large-scale applications; (1) the pairwise approach is severely inefficient due to the quadratic computational cost; and (2) even recent model-based samplers (e.g. IRGAN) cannot achieve practical efficiency due to the training of an extra model.In this paper, we propose a learning-to-rank approach, which achieves convergence speed comparable to the pointwise counterpart while performing similarly to the pairwise counterpart in terms of ranking effectiveness. Our approach estimates the probability densities of positive items for each user within a rich class of distributions, viz. exponential family. In our formulation, we derive a loss function and the appropriate negative sampling distribution based on maximum likelihood estimation. We also develop a practical technique for risk approximation and a regularisation scheme. We then discuss that our single-model approach is equivalent to an IRGAN variant under a certain condition. Through experiments on real-world datasets, our approach outperforms the pointwise and pairwise counterparts in terms of effectiveness and efficiency. CCS CONCEPTS• Information systems → Recommender systems; Learning to rank; • Computing methodologies → Learning from implicit feedback.

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“…Collaborative Rec. IRGAN [125] 2017 ✓ ✓ ✓ ✓ CFGAN [18] 2018 ✓ ✓ ✓ Chae et al [19] 2018 ✓ ✓ ✓ AVAE [143] 2018 ✓ ✓ ✓ CAAE [20] 2019 ✓ ✓ ✓ CGAN [113] 2019 ✓ ✓ ✓ CALF [30] 2019 ✓ ✓ ✓ PD-GAN [132] 2019 ✓ ✓ ✓ LambdaGAN [128] 2019 ✓ ✓ ✓ VAEGAN [139] 2019 ✓ ✓ ✓ APL [108] 2019 ✓ ✓ ✓ RsyGAN [137] 2019 ✓ ✓ ✓ GAN-PW/LSTM [24] 2019 ✓ ✓ ✓ CoFiGAN [73] 2020 ✓ ✓ ✓ ✓ Graph-based Collaborative Rec. GraphGAN [122] 2018 ✓ ✓ ✓ GAN-HBNR [11] 2018 ✓ ✓ ✓ VCGAN [145] 2018 ✓ ✓ ✓ UPGAN [48] 2020 ✓ ✓ ✓ Hybrid Collaborative Rec.…”

Section: Model Namementioning

confidence: 99%

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Deldjoo

Noia

Merra

2020

Proceedings of the 13th International Conference on Web Search and Data Mining

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Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. Notwithstanding their great success, in recent years, it has been shown that these methods are vulnerable to adversarial examples, i.e., subtle but non-random perturbations designed to force recommendation models to produce erroneous outputs. The main reason for this behavior is that user interaction data used for training of LFM can be contaminated by malicious activities or users' misoperation that can induce an unpredictable amount of natural noise and harm recommendation outcomes. On the other side, it has been shown that these systems, conceived originally to attack machine learning applications, can be successfully adopted to strengthen their robustness against attacks as well as to train more precise recommendation engines.In this respect, the goal of this survey is two-fold: (i) to present recent advances on AML-RS for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs), which use the core concept of learning in AML (i.e., the min-max game) for generative applications.In this survey, we provide an exhaustive literature review of 60 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS and recommendation models leveraging generative models to improve their quality.

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CoFiGAN: Collaborative filtering by generative and discriminative training for one-class recommendation

Cited by 25 publications

References 25 publications

Recommender systems based on graph embedding techniques: A comprehensive review

Recommender systems based on graph embedding techniques: A comprehensive review

Scalable Personalised Item Ranking through Parametric Density Estimation

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

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