Enhancing Collaborative Filtering with Generative Augmentation

Wang, Qinyong; Yin, Hongzhi; Wang, Hao; Nguyen, Quoc Viet Hung; Huang, Zi; Cui, Lizhen

doi:10.1145/3292500.3330873

Cited by 95 publications

(57 citation statements)

References 25 publications

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“…GANs have been shown powerful in generating relevant recommendations -in particular, using the CF approach -and capable of successively competing with state-of-the-art models in the field of RS. We have identified the following reasons for the potential of GANs in RS: (i) they are able to generalize well and learn unknown user preference distributions and thus be able to model user preference in complex settings (e.g., IRGAN [125] and CFGAN [18]); (ii) they are capable of generating more negative samples than random samples in pairwise learning tasks (e.g., APL [108], DASO [39]) and (iii) they can be used for data augmentation (e.g., AugCF [127] and RAGAN [17]).…”

Section: Collaborative Recommendationmentioning

confidence: 99%

“…GraphGAN [122] 2018 ✓ ✓ ✓ GAN-HBNR [11] 2018 ✓ ✓ ✓ VCGAN [145] 2018 ✓ ✓ ✓ UPGAN [48] 2020 ✓ ✓ ✓ Hybrid Collaborative Rec. VAE-AR [66] 2017 ✓ ✓ ✓ RGD-TR [71] 2018 ✓ ✓ ✓ aae-RS [136] 2018 ✓ ✓ ✓ SDNet [26] 2019 ✓ ✓ ✓ ATR [89] 2019 ✓ ✓ ✓ AugCF [127] 2019 ✓ ✓ ✓ RSGAN [138] 2019 ✓ ✓ ✓ RRGAN [24] 2019 ✓ ✓ ✓ UGAN [129] 2019 ✓ ✓ ✓ LARA [107] 2020 ✓ ✓ ✓ CGAN [28] 2020 ✓ ✓ ✓ Context-aware Rec. Temporal-aware RecGAN [8] 2018 ✓ ✓ ✓ NMRN-GAN [126] 2018 ✓ ✓ ✓ AAE [116] 2018 ✓ ✓ ✓ PLASTIC [147] 2018 [25] 2019 ✓ ✓ ✓ Geographical-aware Geo-ALM [75] 2019 ✓ ✓ ✓ APOIR [148] 2019 ✓ ✓ ✓ Cross-domain Rec.…”

Section: Model Namementioning

confidence: 99%

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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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Section: Collaborative Recommendationmentioning

confidence: 99%

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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“…T words and a target sequence Y = (y 1 , y 2 , ..., y T ) whose length is T consist of a source sequence X = (x 1 , x 2 , ..., x T ), which make the generation probability of the model to be the maximized Y under the X : p(y 1 , y 2 , ..., y T |x 1 , x 2 , ..., x T ) condition. In detail, the encoder-decoder framework is a structure of Seq2Seq [8,9]. The encoder word by word reads X, and uses a context vector c produced by RNN to speak with it.…”

Section: Seq2seqmentioning

confidence: 99%

“…Generative adversarial nets [8,10,11] aim to train two competing networks, one of which is to train a generator making the generated results as close to the real ones as possible, and the other aim is to train a discriminator which determines whether the data are from the distribution of the real or the distribution learned by the generator.…”

Section: Generative Adversarial Netsmentioning

confidence: 99%

An Empirical Study on Deep Neural Network Models for Chinese Dialogue Generation

Maimaiti

Sheng

et al. 2020

Symmetry

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The task of dialogue generation has attracted increasing attention due to its diverse downstream applications, such as question-answering systems and chatbots. Recently, the deep neural network (DNN)-based dialogue generation models have achieved superior performance against conventional models utilizing statistical machine learning methods. However, despite that an enormous number of state-of-the-art DNN-based models have been proposed, there lacks detailed empirical comparative analysis for them on the open Chinese corpus. As a result, relevant researchers and engineers might find it hard to get an intuitive understanding of the current research progress. To address this challenge, we conducted an empirical study for state-of-the-art DNN-based dialogue generation models in various Chinese corpora. Specifically, extensive experiments were performed on several well-known single-turn and multi-turn dialogue corpora, including KdConv, Weibo, and Douban, to evaluate a wide range of dialogue generation models that are based on the symmetrical architecture of Seq2Seq, RNNSearch, transformer, generative adversarial nets, and reinforcement learning respectively. Moreover, we paid special attention to the prevalent pre-trained model for the quality of dialogue generation. Their performances were evaluated by four widely-used metrics in this area: BLEU, pseudo, distinct, and rouge. Finally, we report a case study to show example responses generated by these models separately.

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“…Then [41] further extended APR to multimedia recommendation, which also has been shown effective. Besides, [42], [43] investigated a new application of GAN by generating high-level augmented useritem interactions to improve collaborative filtering methods.…”

Section: B Adversarial Training In Recommender Systemsmentioning

confidence: 99%

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

Gao

Yin

et al. 2019

2019 IEEE International Conference on Data Mining (ICDM)

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Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems. However, this assumption is often untenable as the online social networks are quite sparse and a majority of users only have a small number of friends. Besides, explicit friends may not share similar interests because of the randomness in the process of building social networks. Therefore, discovering a number of reliable friends for each user plays an important role in advancing social recommendation. Unlike other studies which focus on extracting valuable explicit social links, our work pays attention to identifying reliable friends in both the observed and unobserved social networks. Concretely, in this paper, we propose an end-to-end social recommendation framework based on Generative Adversarial Nets (GAN). The framework is composed of two blocks: a generator that is used to produce friends that can possibly enhance the social recommendation model, and a discriminator that is responsible for assessing these generated friends and ranking the items according to both the current user and her friends' preferences. With the competition between the generator and the discriminator, our framework can dynamically and adaptively generate reliable friends who can perfectly predict the current user' preference at a specific time. As a result, the sparsity and unreliability problems of explicit social relations can be mitigated and the social recommendation performance is significantly improved. Experimental studies on real-world datasets demonstrate the superiority of our framework and verify the positive effects of the generated reliable friends.

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Enhancing Collaborative Filtering with Generative Augmentation

Cited by 95 publications

References 25 publications

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

An Empirical Study on Deep Neural Network Models for Chinese Dialogue Generation

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

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