Augmented Variational Autoencoders for Collaborative Filtering with Auxiliary Information

Lee, Wonsung; Song, Kyungwoo; Moon, Il‐Chul

doi:10.1145/3132847.3132972

Cited by 52 publications

(31 citation statements)

References 11 publications

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“…In this paper, we assume that Y is binarized to capture implicit feedback, which is a common setting for top-N recommendation [12]. Thus we follow Lee et al [7] and assume that the rating of user j over all items follows a Bernoulli distribution:…”

Section: Collective Variational Autoencodermentioning

confidence: 99%

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A Collective Variational Autoencoder for Top-N Recommendation with Side Information

Chen

Rijke

2018

Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems

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Recommender systems have been studied extensively due to their practical use in many real-world scenarios. Despite this, generating effective recommendations with sparse user ratings remains a challenge. Side information associated with items has been widely utilized to address rating sparsity. Existing recommendation models that use side information are linear and, hence, have restricted expressiveness. Deep learning has been used to capture non-linearities by learning deep item representations from side information but as side information is high-dimensional existing deep models tend to have large input dimensionality, which dominates their overall size. This makes them difficult to train, especially with small numbers of inputs.Rather than learning item representations, which is problematic with high-dimensional side information, in this paper, we propose to learn feature representation through deep learning from side information. Learning feature representations, on the other hand, ensures a sufficient number of inputs to train a deep network. To achieve this, we propose to simultaneously recover user ratings and side information, by using a Variational Autoencoder (VAE). Specifically, user ratings and side information are encoded and decoded collectively through the same inference network and generation network. This is possible as both user ratings and side information are data associated with items. To account for the heterogeneity of user rating and side information, the final layer of the generation network follows different distributions depending on the type of information. The proposed model is easy to implement and efficient to optimize and is shown to outperform state-of-the-art top-N recommendation methods that use side information.

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Section: Collective Variational Autoencodermentioning

confidence: 99%

“…A growing body of work generalizes linear model by deep learning to explore non-linearities for large-scale recommendations [3,15,19,24]. State-of-the-art performance is achieved by applying Variational Autoencoders (VAEs) [5] for CF [7,9,10]. These deep models learn item representations from side information.…”

Section: Introductionmentioning

confidence: 99%

A Collective Variational Autoencoder for Top-N Recommendation with Side Information

Chen

Rijke

2018

Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems

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“…Thus, exploring a sequential architecture comes as a natural and reasonable choice to learn data dynamics, especially when data representations tend to be sparse. Bellini et al (2017), Chae et al (2019), He et al (2019), Hu et al (2019), Jhamb et al (2018), Lee et al (2017Lee et al ( , 2018, Liang et al (2018), Liu et al (2017), Nisha and Mohan (2019), Song et al (2019), Wang, Chen, et al (2019), Wang et al (2020) Convolutional neural network (CNN) 9 Chen, Cai, et al (2019), Da Costa and Dolog (2019), Hyun et al (2018), Liu et al (2017Liu et al ( , 2019, Wang, Chen, et al (2019), Zhang, Cheng, and Ren (2019), Zhang, Yao, et al (2017), Zheng et al (2017) Generative adversarial network (GAN) 3 Chae et al 2019, Lee et al (2017), Wang, Chen, et al (2019) Graph neural network (GNN) 2 Wu, Hong, et al (2019), Zheng et al (2018) Multilayer perceptron (MLP) 20 Bai et al (2017), Cao et al, 2018, C. Chen et al (2020, L. Chen, Zheng, et al (2018), W. Chen, Cai, et al (2019) , Zhou et al (2019) Neural attention 13 (Cao et al (2018), L. Chen, Zheng, et al, 2018, Chin et al, 2018, W. Fan et al (2019, Feng & Zeng, 2019, Jhamb et al (2018…”

Section: Discussionmentioning

confidence: 99%

“…In the study conducted by Lee et al (2017), different VAE architectures to deal with side information in the context of CF are explored. As such, conditional and joint distributions, adversarial learning, and latter structures are introduced, as part of VAE architectures.…”

Section: Synthesis Of Main Primary Studiesmentioning

confidence: 99%

Deep learning techniques for recommender systems based on collaborative filtering

2020

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In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well-known CF recommenders suffer from data sparsity, mainly in cold-start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state-of-the-art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low-dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state-of-theart literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non-linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF-based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios.

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“…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%

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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Augmented Variational Autoencoders for Collaborative Filtering with Auxiliary Information

Cited by 52 publications

References 11 publications

A Collective Variational Autoencoder for Top-N Recommendation with Side Information

A Collective Variational Autoencoder for Top-N Recommendation with Side Information

Deep learning techniques for recommender systems based on collaborative filtering

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

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