Session-based recommendation via flow-based deep generative networks and Bayesian inference

Zhong, Ting; Wen, Zijing; Zhang, Fan; Trajcevski, Goce; Zhang, Xiaoying

doi:10.1016/j.neucom.2020.01.096

Cited by 14 publications

(3 citation statements)

References 6 publications

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“…In addition to the previously mentioned generative models, RS also draw upon other types of generative models. For instance, VASER [180] leverages normalizing flows [125] (and VAEs [79]) for session-based recommendation. GFN4Rec [101], on the other hand, adapts generative flow networks [10,116] for listwise recommendation.…”

Section: Other Generative Modelsmentioning

confidence: 99%

A Review of Modern Fashion Recommender Systems

Deldjoo,

Nazary,

Ramisa

et al. 2023

ACM Comput. Surv.

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The textile and apparel industries have grown tremendously over the last few years. Customers no longer have to visit many stores, stand in long queues, or try on garments in dressing rooms as millions of products are now available in online catalogs. However, given the plethora of options available, an effective recommendation system is necessary to properly sort, order, and communicate relevant product material or information to users. Effective fashion RS can have a noticeable impact on billions of customers’ shopping experiences and increase sales and revenues on the provider side. The goal of this survey is to provide a review of recommender systems that operate in the specific vertical domain of garment and fashion products. We have identified the most pressing challenges in fashion RS research and created a taxonomy that categorizes the literature according to the objective they are trying to accomplish (e.g., item or outfit recommendation, size recommendation, explainability, among others) and type of side-information (users, items, context). We have also identified the most important evaluation goals and perspectives (outfit generation, outfit recommendation, pairing recommendation, and fill-in-the-blank outfit compatibility prediction) and the most commonly used datasets and evaluation metrics.

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Section: Other Generative Modelsmentioning

confidence: 99%

A Review of Modern Fashion Recommender Systems

Deldjoo,

Nazary,

Ramisa

et al. 2023

ACM Comput. Surv.

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“…Generative models, such as Variational Autoencoders (VAEs) [134], [135] and Generative Adversarial Networks (GANs) [136], constitute powerful deep-learning techniques with substantial applicability in recommendation systems. They offer a distinct perspective by enabling the system to generate fresh recommendations based on learned data patterns.…”

Section: ) Generative Modelsmentioning

confidence: 99%

Deep Learning-Based Recommendation System: Systematic Review and Classification

Li,

Ishak,

Ibrahim

et al. 2023

IEEE Access

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In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature on classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains.To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification. We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems. INDEX TERMSDeep learning, term classification, recommendation system, systematic review, state-ofthe-art techniques I. INTRODUCTION

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“…O S H HK + [33], where S, H, and K are the number of items, hidden units, and output units, respectively.…”

Section: ) Complexity Of the Grumentioning

confidence: 99%

A Learning System Integrating Temporal Convolution and Deep Learning for Predictive Modeling of Crude Oil Price

Niu

Wang

et al. 2021

IEEE Trans. Ind. Inf.

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Accurately crude oil price prediction remains challenging so far. Despite the abundant research achievements of crude oil price prediction, most of them emphasize the linear and deterministic modeling, which cannot adequately capture the complex nonlinear characteristics and uncertainties involved, thus impeding further developments in the field. In this study, a novel learning system with the aim of obtaining deterministic and probabilistic predictions is presented to model the nonlinear dynamics in crude oil price, composed by the modules of recurrence analysis, outlier detection, data preprocessing, feature selection, predictive modeling based on deep learning, and system evaluation. In particular, the temporal convolution is developed to perform the feature selection, thus improving the generalization of the system. Additionally, the extensions, including the predictive performance test evaluation, convergence investigation, and sensitivity analysis, are carried out. The experimental simulations show that the proposed system can yield the deterministic and probabilistic predictions with higher accuracy and feasibility compared with the benchmarks considered, further indicating its effectiveness.

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Session-based recommendation via flow-based deep generative networks and Bayesian inference

Cited by 14 publications

References 6 publications

A Review of Modern Fashion Recommender Systems

A Review of Modern Fashion Recommender Systems

Deep Learning-Based Recommendation System: Systematic Review and Classification

A Learning System Integrating Temporal Convolution and Deep Learning for Predictive Modeling of Crude Oil Price

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