Adversarial Point-of-Interest Recommendation

Zhang, Fan; Yin, Ruiyang; Zhang, Kunpeng; Trajcevski, Goce; Zhong, Ting; Wu, Jin

doi:10.1145/3308558.3313609

Cited by 102 publications

(52 citation statements)

References 26 publications

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“…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. VAE-GAN-CC [82] 2018 ✓ ✓ ✓ RecSys-DAN [121] 2019 ✓ ✓ ✓ FR-DiscoGAN [59] 2019 ✓ ✓ ✓ DASO [39] 2019 ✓ ✓ ✓ CnGAN [88] 2019 ✓ ✓ ✓ Fashion Rec.…”

Section: Model Namementioning

confidence: 99%

“…[APOIR] Inspired by the advances of POI recommendation performance under GAN-based framework, Zhou et al propose adversarial point-of-interest recommendation (APOIR) [148] to learn user-latent representations in a generative manner. The main novelty of the proposed framework is the use of POIs' geographical features and the users' social relations into the reward function used to optimize the G. The reward function acts like a contextual-aware regularizer of G, that is the component of APOIR in the proposed POI recommendation model.…”

Section: Context-aware Recommendationmentioning

confidence: 99%

See 1 more Smart Citation

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: Model Namementioning

confidence: 99%

Section: Context-aware Recommendationmentioning

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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“…Thereafter, He et al [14] contributed a new learning method for optimizing recommender models, which enhances the pairwise ranking method by performing adversarial training. In the domain of pointof-interest (POI) recommendation, Zhou et al [43] unified RL and matrix factorization methods into an adversarial learning framework. Furthermore, Wang et al [38] proposed an adversarial learning framework for collaborative ranking to learn with a dynamic scenario.…”

Section: Adversarial Learning For Retrievalmentioning

confidence: 99%

Adversarial Video Moment Retrieval by Jointly Modeling Ranking and Localization

Cao

Zeng

Wei

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Retrieving video moments from an untrimmed video given a natural language as the query is a challenging task in both academia and industry. Although much effort has been made to address this issue, traditional video moment ranking methods are unable to generate reasonable video moment candidates and video moment localization approaches are not applicable to large-scale retrieval scenario. How to combine ranking and localization into a unified framework to overcome their drawbacks and reinforce each other is rarely considered. Toward this end, we contribute a novel solution to thoroughly investigate the video moment retrieval issue under the adversarial learning paradigm. The key of our solution is to formulate the video moment retrieval task as an adversarial learning problem with two tightly connected components. Specifically, a reinforcement learning is employed as a generator to produce a set of possible video moments. Meanwhile, a pairwise ranking model is utilized as a discriminator to rank the generated video moments and the ground truth. Finally, the generator and the discriminator are mutually reinforced in the adversarial learning framework, which is able to jointly optimize the performance of both video moment ranking and video moment localization. Extensive experiments on two well-known datasets have well verified the effectiveness and rationality of our proposed solution. CCS CONCEPTS • Information systems → Multimedia and multimodal retrieval; • Theory of computation → Adversarial learning; Reinforcement learning.

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“…SAE-NAD [18] applies auto-encoder to learn POI recommendations. APOIR [35] employs a generative model to make POI recommendations. The generator suggests POIs based on the learned distribution by maximizing the probabilities over these POIs and the discriminator distinguishes the recommended POIs from the true check-ins.…”

Section: Related Workmentioning

confidence: 99%

Adversarial Learning to Compare

Wang

2020

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

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Recommendation systems tend to suffer severely from the sparse training data. A large portion of users and items usually have a very limited number of training instances. The data sparsity issue prevents us from accurately understanding users' preferences and items' characteristics and jeopardize the recommendation performance eventually. In addition, models, trained with sparse data, lack abundant training supports and tend to be vulnerable to adversarial perturbations, which implies possibly large errors in generalization.In this work, we investigate the recommendation task in the context of prospective customer recommendation in location based social networks. To comprehensively utilize the training data, we explicitly learn to compare users' historical check-in businesses utilizing self-attention mechanisms. To enhance the robustness of a recommender system and improve its generalization performance, we perform adversarial training. Adversarial perturbations are dynamically constructed during training and models are trained to be tolerant of such nuisance perturbations. In a nutshell, we introduce a Self-Attentive prospective Customer RecommendAtion framework, SACRA, which learns to recommend by making comparisons among users' historical check-ins with adversarial training. To evaluate the proposed model, we conduct a series of experiments to extensively compare with 12 existing methods using two realworld datasets. The results demonstrate that SACRA significantly outperforms all baselines.

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Adversarial Point-of-Interest Recommendation

Cited by 102 publications

References 26 publications

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Adversarial Video Moment Retrieval by Jointly Modeling Ranking and Localization

Adversarial Learning to Compare

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