An improved deep forest model for prediction of e-commerce consumers’ repurchase behavior

Zhang, Weiwei; Wang, Mingyan

doi:10.1371/journal.pone.0255906

Cited by 10 publications

(5 citation statements)

References 36 publications

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“…The most powerful predictors of conversion behaviour are page and session stay time, user type, click entropy, and click position. The findings demonstrate that improved metrics and modelling approaches could yield significant gains in search behaviour [8].…”

Section: Literature Reviewmentioning

confidence: 86%

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Retracted: Performance optimization of unstructured E‐commerce log data for activity and pattern evaluation using web analytics

Shukla¹,

Mishra

Sinha

et al. 2022

IET Communications

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The above article from IET Communications, published online on 4 November 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

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Section: Literature Reviewmentioning

confidence: 86%

“…Accuracy: The number of real positive tests compared to the total number of predicted benefits in Equation (8).…”

Section: R E T R a C T E Dmentioning

confidence: 99%

Retracted: Performance optimization of unstructured E‐commerce log data for activity and pattern evaluation using web analytics

Shukla¹,

Mishra

Sinha

et al. 2022

IET Communications

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“…It takes a lot of time and effort. Zhang and Wang [29] developed an enhanced deep forest strategy for predicting consumer behavior, which is crucial for growing a firm. It is among the most crucial element of corporate intelligence.…”

Section: Literature Surveymentioning

confidence: 99%

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Jaharudin

Song

2022

Journal of Sensors

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Consumption behavior prediction reveals customer attributes, personal preferences, and intrinsic laws. Organizations would benefit from knowing further about customer needs and business desires by monitoring client behavior to provide more precise recommendations and boost acquisition rates. The economics of the customer, buyer groupings, and product quality are only a few of the numerous variables that influence customer behavior. The key issue that has to be resolved at this time is how to filter out useful information from these vast amounts of data to forecast customer behavior. For customer consumption behavior prediction and analysis with an advanced quantitative research process, we proposed the multiobjective evolutionary algorithm, which significantly boosts the accuracy of consumption behavior predictions. The dataset is initially gathered based on consumer preferences and behaviors as the essential information for the entire prediction model. Min-max normalization is used as a component of the preprocessing of the data to get the elimination of redundant and superfluous data. The Word2vec model is utilized for feature extraction, and boosted ant colony optimization (BACO) is employed to choose the best features. Utilizing the suggested multiobjective evolutionary algorithm (MOEA), the predictions are made. The suggested system’s performance is assessed, and the metrics are contrasted with more established methods. The findings demonstrate that the suggested MOEA technique performs well than the traditional ML, XGB, AI, and HNB algorithm methods in terms of accuracy (95 percent), quality of prediction (97 percent), precision (99 percent), recall (93 percent), F 1 -score (98 percent), and prediction time (50 seconds). Hence, the outcomes show that the regression model is sustainable. The suggested consumption behavior prediction system has demonstrated its efficiency in boosting profitability.

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“…In line with this, the Global Web Index has reported that a user typically makes use of social media for up to two hours and 42 minutes per day [6]. Therefore, the last decade has underlined social media marketing as an essential marketing tool, emerging as a mainstream research aspect [7].…”

Section: Introductionmentioning

confidence: 99%

“…To compare: consumers having the brand awareness orientation perceive the brand as a symbol of credibility and prestige, whereas those with value awareness usually check and compare the prices and quality of different brands through social media to ensure a best-value purchase [11]. To this end, many companies employ social media in carrying out low-cost and high-efficiency marketing activities for consumers [7,12]. Social media is a marketing tool wielded for four primary purposes: market research and feedback; brand promotion and reputation management; customer service and customer relationship management; and business network [13,14].…”

Section: Introductionmentioning

confidence: 99%

Sustainable customer retention through social media marketing activities using hybrid SEM-neural network approach

et al. 2022

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Social media has changed the marketing phenomenon, as firms use social media to inform, impress, and retain the existing consumers. Social media marketing empowers business firms to generate perceived brand equity activities and build the notion among consumers to continue using the firms’ products and services. The current exploratory study aimed to examine the effects of social media marketing activities on brand equity (brand awareness and brand image) and repurchase intention of high-tech products among Chinese consumers. The study used a cross-sectional design, and the final analysis was performed on 477 valid responses that were collected through an online survey. Partial least squares structural equation modelling (PLS-SEM) and artificial neural network (ANN) analysis were performed. The obtained results revealed positive and significant effects of trendiness, interaction, and word of mouth on brand awareness. Customisation, trendiness, interaction, and word of mouth were found to positively affect brand image. Brand awareness and brand image were found to affect repurchase intention. The results of multilayer ANN analysis suggested trendiness as the most notable factor in developing brand awareness and brand image. Brand awareness was found to be an influential factor that nurtures repurchase intention. The study’s results confirmed the relevance of social media marketing activities in predicting brand equity and brand loyalty by repurchase intention. Marketing professionals need to concentrate on entertainment and customisation aspects of social media marketing that can help to achieve brand awareness and image. The limitations of study and future research opportunities are presented at the end of this article.

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An improved deep forest model for prediction of e-commerce consumers’ repurchase behavior

Cited by 10 publications

References 36 publications

Retracted: Performance optimization of unstructured E‐commerce log data for activity and pattern evaluation using web analytics

Retracted: Performance optimization of unstructured E‐commerce log data for activity and pattern evaluation using web analytics

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Sustainable customer retention through social media marketing activities using hybrid SEM-neural network approach

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