Adversarial Training-Based Mean Bayesian Personalized Ranking for Recommender System

Wang, Jianfang; Han, Pengfei

doi:10.1109/access.2019.2963316

Cited by 12 publications

(2 citation statements)

References 40 publications

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“…Because the three types of data are more closely related, they are more suitable for data feedback using the converted MBPR method. The data processing of this method is well adapted to this application [19]. In this application, it is mainly to recommend an ideal port selection plan from a network element.…”

Section: Network Element Optimizationmentioning

confidence: 99%

A Method of Optimizing Network Topology Structure Combining Viterbi Algorithm and Bayesian Algorithm

Shi

2021

Wireless Communications and Mobile Computing

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With Internet entering all walks of life, development of internet and usage expansion demand better performance, especially the application of 5G network that adopts NAS networking mode. Some of the network bandwidth cannot fully support the current network demand, which causes network fluctuations and other concerns. In this paper, a method for optimizing the topological structure of the bottom layer of the communication network is proposed that has outage performance close to optimal routing scheme. In specific, path in areas with poor network conditions is first optimized using Viterbi algorithm. Then, network element nodes on the path are optimized using Bayes recommendation algorithm for reasonable flow distribution. Dual planning of improved Viterbi algorithm is used to realize the main and standby path planning, and then, Bayesian recommendation algorithm based on the average value is used to optimize the network elements. Therefore, it is very efficient to realize overall performance optimization.

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Section: Network Element Optimizationmentioning

confidence: 99%

A Method of Optimizing Network Topology Structure Combining Viterbi Algorithm and Bayesian Algorithm

Shi

2021

Wireless Communications and Mobile Computing

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“…Given the gained performances obtained in both robustness and accuracy dimensions, we have recently witnessed the application of APR in a growing number of research works. More than 15 articles present novel recommendation algorithms incorporating the APR as the core optimization framework [8,11,14,17,21,22,28,[31][32][33][34][36][37][38][39]. These examples underline the popularity of the adversarial ranking-based procedure, i.e., APR, for various item recommendation tasks.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality

Anelli,

Deldjoo,

Di Noia

et al. 2021

Preprint

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Recommender systems (RSs) employ user-item feedback, e.g., ratings, to match customers to personalized lists of products. Approaches to top-k recommendation mainly rely on Learning-To-Rank algorithms and, among them, the most widely adopted is Bayesian Personalized Ranking (BPR), which bases on a pair-wise optimization approach. Recently, BPR has been found vulnerable against adversarial perturbations of its model parameters. Adversarial Personalized Ranking (APR) mitigates this issue by robustifying BPR via an adversarial training procedure. The empirical improvements of APR's accuracy performance on BPR have led to its wide use in several recommender models. However, a key overlooked aspect has been the beyond-accuracy performance of APR, i.e., novelty, coverage, and amplification of popularity bias, considering that recent results suggest that BPR, the building block of APR, is sensitive to the intensification of biases and reduction of recommendation novelty. In this work, we model the learning characteristics of the BPR and APR optimization frameworks to give mathematical evidence that, when the feedback data have a tailed distribution, APR amplifies the popularity bias more than BPR due to an unbalanced number of received positive updates from short-head items. Using matrix factorization (MF), we empirically validate the theoretical results by performing preliminary experiments on two public datasets to compare BPR-MF and APR-MF performance on accuracy and beyond-accuracy metrics. The experimental results consistently show the degradation of novelty and coverage measures and a worrying amplification of bias.

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Adversarial Recommender Systems: Attack, Defense, and Advances

Anelli

Deldjoo

DiNoia

et al. 2021

Recommender Systems Handbook

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Adversarial Training-Based Mean Bayesian Personalized Ranking for Recommender System

Cited by 12 publications

References 40 publications

A Method of Optimizing Network Topology Structure Combining Viterbi Algorithm and Bayesian Algorithm

A Method of Optimizing Network Topology Structure Combining Viterbi Algorithm and Bayesian Algorithm

Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality

Adversarial Recommender Systems: Attack, Defense, and Advances

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