Online Learning From Incomplete and Imbalanced Data Streams

You, Dianlong; Xiao, Jiawei; Yang, Weijia; Yan, Huigui; Wu, Di; Chen, Zhen; Shen, Limin; Wu, Xindong

doi:10.1109/tkde.2023.3250472

Cited by 15 publications

(1 citation statement)

References 34 publications

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“…In the current era characterized by abundant information, individuals are confronted with a deluge of extensive data [1][2][3][4]. Notable examples include the colossal amount of data generated by Google, reaching the scale of petabytes, and Flickr, which produces terabytes of data on a daily basis [5,6]. The challenge at hand is to devise an intelligent system capable of extracting relevant information from these vast datasets [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis

Guo

Liao

et al. 2023

Entropy

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A recommender system (RS) is highly efficient in extracting valuable information from a deluge of big data. The key issue of implementing an RS lies in uncovering users’ latent preferences on different items. Latent Feature Analysis (LFA) and deep neural networks (DNNs) are two of the most popular and successful approaches to addressing this issue. However, both the LFA-based and the DNNs-based models have their own distinct advantages and disadvantages. Consequently, relying solely on either the LFA or DNN-based models cannot ensure optimal recommendation performance across diverse real-world application scenarios. To address this issue, this paper proposes a novel hybrid recommendation model that combines Autoencoder and LFA techniques, termed AutoLFA. The main idea of AutoLFA is two-fold: (1) It leverages an Autoencoder and an LFA model separately to construct two distinct recommendation models, each residing in a unique metric representation space with its own set of strengths; and (2) it integrates the Autoencoder and LFA model using a customized self-adaptive weighting strategy, thereby capitalizing on the merits of both approaches. To evaluate the proposed AutoLFA model, extensive experiments on five real recommendation datasets are conducted. The results demonstrate that AutoLFA achieves significantly better recommendation performance than the seven related state-of-the-art models.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis

Guo

Liao

et al. 2023

Entropy

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Online Learning in Varying Feature Spaces with Informative Variation

Qin,

Song

2024

IFIP Advances in Information and Communication Technology

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A survey on imbalanced learning: latest research, applications and future directions

Chen,

Yang,

et al. 2024

Artif Intell Rev

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Imbalanced learning constitutes one of the most formidable challenges within data mining and machine learning. Despite continuous research advancement over the past decades, learning from data with an imbalanced class distribution remains a compelling research area. Imbalanced class distributions commonly constrain the practical utility of machine learning and even deep learning models in tangible applications. Numerous recent studies have made substantial progress in the field of imbalanced learning, deepening our understanding of its nature while concurrently unearthing new challenges. Given the field’s rapid evolution, this paper aims to encapsulate the recent breakthroughs in imbalanced learning by providing an in-depth review of extant strategies to confront this issue. Unlike most surveys that primarily address classification tasks in machine learning, we also delve into techniques addressing regression tasks and facets of deep long-tail learning. Furthermore, we explore real-world applications of imbalanced learning, devising a broad spectrum of research applications from management science to engineering, and lastly, discuss newly-emerging issues and challenges necessitating further exploration in the realm of imbalanced learning.

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Online Learning From Incomplete and Imbalanced Data Streams

Cited by 15 publications

References 34 publications

A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis

A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis

Online Learning in Varying Feature Spaces with Informative Variation

A survey on imbalanced learning: latest research, applications and future directions

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