Attenuated sentiment-aware sequential recommendation

Zhou, Donglin; Zheng, Yangxin; Zou, Zhenting; Zheng, Lin

doi:10.1007/s41060-022-00374-5

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…They propose a novel approach that treats similarity as conditional probability, aiming to provide insights into the recommendation process and address the issue of popularity bias. Another paper by Zhou et al [75] explores the incorporation of attenuated sentiment information into sequential recommender systems. They investigate how users' sentiments can influence their purchase behaviors and preferences, and propose a method to capture and utilize this sentiment information in the recommendation process.…”

Section: Implicit and Sequential Recommendationmentioning

confidence: 99%

Data science for next-generation recommender systems

Wang

Sivrikaya

et al. 2023

Int J Data Sci Anal

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Data science has been the foundation of recommender systems for a long time. Over the past few decades, various recommender systems have been developed using different data science and machine learning methodologies and techniques. However, no existing work systematically discusses the significant relationships between data science and recommender systems. To bridge this gap, this paper aims to systematically investigate recommender systems from the perspective of data science. Firstly, we introduce the various types of data used for recommendations and the corresponding machine learning models and methods that effectively represent each type. Next, we provide a brief outline of the representative data science and machine learning models utilized in building recommender systems. Subsequently, we share some preliminary thoughts on next-generation recommender systems. Finally, we summarize this special issue on data science for next-generation recommender systems.

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Section: Implicit and Sequential Recommendationmentioning

confidence: 99%