Retraction Note to: Implementation and comparison of topic modeling techniques based on user reviews in e-commerce recommendations

Chehal, Dimple; Gupta, Pratiksha; Gulati, Payal

doi:10.1007/s12652-022-04004-7

Cited by 5 publications

(6 citation statements)

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“…The computed ( 1) and (1a) is calculated for every layer l with d dimensional inputs x = (x (1) , x (2) , …x (d) ). Therefore, the normalization (re-scaling and re-centered) should perform on each input individually, Eq.…”

Section: Methodsmentioning

confidence: 99%

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Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

Selvasheela¹,

Abirami²,

Askarunisa³

2023

Computer Systems Science and Engineering

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Nowadays, commercial transactions and customer reviews are part of human life and various business applications. The technologies create a great impact on online user reviews and activities, affecting the business process. Customer reviews and ratings are more helpful to the new customer to purchase the product, but the fake reviews completely affect the business. The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information. Therefore, in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity. Here, Amazon Product Kaggle dataset information is utilized for investigating the customer review. The collected information is analyzed and processed by batch normalized capsule networks (NCN). The network explores the user reviews according to product details, time, price purchasing factors, etc., ensuring product quality and ratings. Then effective recommendation system is developed using a butterfly optimized matrix factorization filtering approach. Then the system's efficiency is evaluated using the Rand Index, Dunn index, accuracy, and error rate.

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Section: Methodsmentioning

confidence: 99%

“…The development of e-commerce and digital advancements [1] causes every product to be directly or indirectly influenced by digital presence. The product user gives feedback [2] through a different medium that helps to improve the organization's function.…”

Section: Introductionmentioning

confidence: 99%

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

Selvasheela¹,

Abirami²,

Askarunisa³

2023

Computer Systems Science and Engineering

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“…Topics refer to important aspects of an item and the ability to extract topics from movie subtitles would assist a great deal in identifying content and discovering similarities between films. There are several methods for identifying topics in documents, including frequency-based, syntax-based, conditional random fields Xia, Yang, Pan, Zhang and An (2020), and thematic modeling approaches such as LDA Zoghbi, Vulić and Moens (2016), and latent semantic analysis (LSA) Chehal, Gupta and Gulati (2021). Review topics can then be used to improve the actual ranking in recommender systems Qiu, Gao, Cheng and Guo (2016).…”

Section: A Review Of Research Literaturementioning

confidence: 99%

A Movie recommender system based on topic modeling using machine learning methods

Kordabadi

Nazari

Mansoorizadeh

2022

Preprint

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In recent years, we have seen an increase in the production of films in a variety of categories and genres. Many of these products contain concepts that are inappropriate for children and adolescents. Hence, parents are concerned that their children may be exposed to these products. As a result, a smart recommendation system that provides appropriate movies based on the user’s age range could be a useful tool for parents. Existing movie recommender systems use quantitative factors and metadata that lead to less attention being paid to the content of the movies. This research is motivated by the need to extract movie features using information retrieval methods in order to provide effective suggestions. The goal of this study is to propose a movie recommender system based on topic modeling and text-based age ratings. The proposed method uses latent Dirichlet allocation (LDA) modelling to identify hidden associations between words, document topics, and the levels of expression of each topic in each document. Machine learning models are then used to recommend age-appropriate movies. It has been demonstrated that the proposed method can determine the user’s age and recommend movies based on the user’s age with 93% accuracy, which is highly satisfactory.

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“…A abordagem de modelagem de tópicos Latent Dirichlet Allocation (LDA) [Blei et al 2003] é um dos modelos para modelagem probabilística de tópicos mais usados para extrac ¸ão de tópicos em documentos [Chehal et al 2021]. Caracteriza-se por atribuir inicialmente probabilidades às palavras do dicionário encontradas na colec ¸ão.…”

Section: Modelagem De Tópicosunclassified

Análise exploratória de tweets do Governo de Santa Catarina utilizando Modelagem de Tópicos

Rocha¹,

Duarte²,

Schreiner³

et al. 2022

Anais Da XVII Escola Regional De Banco De Dados (ERBD 2022)

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Modelagem de tópicos tem sido amplamente utilizada para descobrir tópicos e agrupar documentos de uma coleção de documentos de entrada. O Twitter é uma das plataformas mais utilizadas para divulgação de notícias, sendo uma fonte valiosa de informação para os cidadãos. Utilizando tweets extraídos da conta oficial do Governo de Santa Catarina e LDA para extração de tópicos, este trabalho visa identificar os assuntos recorrentes divulgados nos anos de 2019, 2020 e 2021. Como resultado, é possível identificar os assuntos mais importantes publicados pela conta oficial no período selecionado.

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Retraction Note to: Implementation and comparison of topic modeling techniques based on user reviews in e-commerce recommendations

Cited by 5 publications

References 0 publications

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

A Movie recommender system based on topic modeling using machine learning methods

Análise exploratória de tweets do Governo de Santa Catarina utilizando Modelagem de Tópicos

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