AutoRec

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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“…AutoRec [99] AutoRec reconstructs partial user profiles (i.e., item recommendation) based on the reconstruction power of auto-encoders .…”

Section: Cf Models Since Early Yearsmentioning

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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“…Relevant to our work as well, considerable progress has been made using autoencoder models in application to recommender systems. The paper [23] was among the first works incorporating standard autoencoder frameworks to collaborative filtering. More recent architectures such as MultVAE [15] and RecVAE [24] stepped further by moving towards variational autoencoders and introducing specific loss functions tailored to the task of collaborative filtering.…”

Section: Related Workmentioning

confidence: 99%

Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks

Mirvakhabova

Frolov

Khrulkov

et al. 2020

Fourteenth ACM Conference on Recommender Systems

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We introduce a simple autoencoder based on hyperbolic geometry for solving standard collaborative filtering problem. In contrast to many modern deep learning techniques, we build our solution using only a single hidden layer. Remarkably, even with such a minimalistic approach, we not only outperform the Euclidean counterpart but also achieve a competitive performance with respect to the current state-of-the-art. We additionally explore the effects of space curvature on the quality of hyperbolic models and propose an efficient data-driven method for estimating its optimal value. CCS Concepts: • Information systems → Recommender systems; Collaborative filtering; • Mathematics of computing → Geometric topology; • Computing methodologies → Neural networks.

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“…Therefore, we propose encoding the rich contexts first and then combining various encoded contexts as the input of the SCR to speed up behavior prediction. Based on this idea, the linear auto-encoder (LAE) [40] can be improved to achieve our goal.…”

Section: Deep Auto Encodermentioning

confidence: 99%

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

et al. 2020

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Predicting new user behavior has always been a challenging issue in intelligent recommender systems. This challenge is mainly due to the extreme asymmetry of information between new users and old users. Existing factorization models can efficiently process and map asymmetric information, but they are not good at mining deep relationships between contexts when compressing high-dimensional data. In contrast, neural network methods can deeply exploit the relationship between contexts; however, their training cost is much larger than factorization approaches. Therefore, this paper proposes a scalable and efficient recommender to solve the new user problem by filling the gap between factorization models and neural networks. The scalable part is a neural network that can jointly encode, compress, and fuse various types of contexts. The efficient part is a factorization model based on a correlation constraint mechanism and a projection strategy, which enables an asymmetric mapping of information between old and new users. The entire recommender fuses the two parts so that the factorization model and the neural network can complement each other. The experimental results show that our approach can achieve a good balance between performance and training efficiency compared to state-of-the-art methods. INDEX TERMS Recommender systems, new user problems, user behavior, context-aware recommendation, efficient training.

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AutoRec

Abstract: This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRec's compact and efficiently trainable model outperforms stateof-the-art CF techniques (biased matrix factorization, RBM-CF and LLORMA) on the Movielens and Netflix datasets.

Cited by 896 publications

References 4 publications

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

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