Generating Realistic Users Using Generative Adversarial Network With Recommendation-Based Embedding

Chonwiharnphan, Parichat; Thienprapasith, Pipop; Chuangsuwanich, Ekapol

doi:10.1109/access.2020.2976491

Cited by 15 publications

(14 citation statements)

References 13 publications

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“…Collaborative filtering [19][20][21][22][23][24][25] Content-based filtering [26][27][28][29] Knowledge-based [18,[30][31][32]] Multi criteria decision making [32][33][34][35][36] Reinforcement learning [37,38] Hybrid approach [39,40] Other approaches [34,41,42] 4.1. Collaborative Filtering CF is widely used as an effective recommendation approach in various applications.…”

Section: Model Studiesmentioning

confidence: 99%

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Recommender Systems in the Real Estate Market—A Survey

2021

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The shift to e-commerce has changed many business areas. Real estate is one of the applications that has been affected by this modern technological wave. Recommender systems are intelligent models that assist users of real estate platforms in finding the best possible properties that fulfill their needs. However, the recommendation task is substantially more challenging in the real estate domain due to the many domain-specific limitations that impair typical recommender systems. For instance, real estate recommender systems usually face the clod-start problem where there are no historical logs for new users or new items, and the recommender system should provide recommendations for these new entities. Therefore, the recommender systems in the real estate market are different and substantially less studied than in other domains. In this article, we aim at providing a comprehensive and systematic literature review on applications of recommender systems in the real estate market. We evaluate a set of research articles (13 journal and 13 conference papers) which represent the majority of research and commercial solutions proposed in the field of real estate recommender systems. These papers have been reviewed and categorized based on their methodological approaches, the main challenges that they addressed, and their evaluation procedures. Based on these categorizations, we outlined some possible directions for future research.

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Section: Model Studiesmentioning

confidence: 99%

“…There are some other approaches that do no fit into the provided categorization. Chonwiharnphan et al [42] proposed a method to generate realistic logs of users for a new real estate item. They used a neural network based model to learn item embeddings.…”

Section: Other Approachesmentioning

confidence: 99%

Recommender Systems in the Real Estate Market—A Survey

2021

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“…The work in [16] brings forth a framework to produce real-world consumption behavior data that can be employed to analyze hidden user consumption behavior habits or preferences. The proposal utilizes Conditional Generative Adversarial Network (CGAN) and Straight-Through Gumbel estimator that is suitable for processing the outputs with discrete data.…”

Section: A Real Estate Recommendationmentioning

confidence: 99%

“…Experimental comparison analyses are made based on a real-world dataset including user logs from a real estate official link. According to the solution in [16], the authors could produce web logs that are further combined for predicting the property or profiles of target users towards real estate. Existing researches show a lack of deep investigation on real-estate recommendation based on past consumption records as well as the contexts.…”

Section: A Real Estate Recommendationmentioning

confidence: 99%

Privacy-Aware Real Estate Recommendation in Cloud for Elderly Care Based on Historical Consumption Behaviors

Liu

Zhang²,

Huang

et al. 2021

IEEE Access

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With the continuous progress of world economy and human society, people's life span is increasingly growing and as a result, the whole world is stepping into the aging society. In the aging society, the elder people generally call for specialized healthcare services that are often provisioned by their residential areas or houses or communities. In this situation, recommending appropriate houses or real estates to the elder people becomes a necessity to help create a healthy elderly care service community, which contribute much to the stability and health of the whole nation even all the world. However, the personalized preferences of the elder people are often hard to capture and profile as the requirements from the elder people are generally vague and undetermined. Moreover, the profiles of the elder people stored in different cloud platforms are often sensitive enough, which block the rational and full use of the valuable elder people profiles for better elder people preference prediction. Considering the above two challenging issues, in this research work, we propose a privacy-aware real estate recommendation method for elderly care based on the historical consumption behaviors of the elder people stored in cloud platforms. At last, the effectiveness and efficiency of the proposal are validated by a series of experiments based on a real-world dataset. Comparison results show the feasibility of the proposal in this research work.

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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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Generating Realistic Users Using Generative Adversarial Network With Recommendation-Based Embedding

Cited by 15 publications

References 13 publications

Recommender Systems in the Real Estate Market—A Survey

Recommender Systems in the Real Estate Market—A Survey

Privacy-Aware Real Estate Recommendation in Cloud for Elderly Care Based on Historical Consumption Behaviors

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

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