Correction to “Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites”

Bai, Ting; Zhao, Wanye Xin; He, Yulan; Nie, Jian‐Yun; Wen, Ji-Rong

doi:10.1109/tkde.2019.2894055

Cited by 5 publications

(3 citation statements)

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“…The implementation of an e-commerce application for the selling of many sorts of products with similar qualities is the subject of this study. Using Transfer Learning and then cosine similarity [15],…”

Section: Recommendations Engines Have Beenmentioning

confidence: 99%

Untitled

2023

IJIEMR

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E-commerce has gradually become one of the most important driving forces for the sustained and vigorous development of country's consumer market. As a result of the rapid development of network and informatization, the application and maturity of technologies like the Internet, terminal equipment, logistics, and payment, and the continuous improvement of people's consumption concepts is growing. The typical marketing methodology does not allow organizations to completely comprehend the needs of their customers. Selling products to customers is the only concern of the e-commerce platforms.They can only concentrate on the product because they don't know what the customer wants.As a result of these limitations, a model for precision marketing that employs integrated convolution neural networks with Bi LSTM (Bidirectional Long Short Term Memory) to create detailed portraits of the target customers. Users' customized items and services are becoming increasingly more dependent on personalized suggestion services as the ecommerce business expands globally. They have the potential to lower the price users pay to gather information while also increasing sales for the company. The use of user reviews to address classic product recommender system research issues has become increasingly common in recent years. Using a convolution and bidirectional long short-term memory hybrid neural network, the review semantics extractor learns how to represent product reviews. Product Recommendations Generator models a user's preference for items based on their prior interactions. In this research a Predictive Integrated Convolution Neural Network with Bi LSTM (PICNN-BiLSTM) is proposed for predictive marketing of e-commerce applications. The proposed model considers Amazon dataset for market prediction and the results represent that the proposed model performance is higher.

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Section: Recommendations Engines Have Beenmentioning

confidence: 99%

Untitled

2023

IJIEMR

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“…To answer RQ1, following existing studies [6,9,29], we conduct the 5-fold cross validation on each project, record each model's performance and treat the average value as the final performance of the project to avoid bias. To reduce the impact of data distribution on model performance, we use stratified k-folds to ensure consistent data distribution between the training data and the testing data.…”

Section: Experiments Setupmentioning

confidence: 99%

“…Of all the responses, 10 issues are confirmed as GFIs, while 6 issues are denied. Due to the space limit, we put the detailed information on our website 6 , and present few examples below.…”

Section: User Studymentioning

confidence: 99%

Characterizing and Predicting Good First Issues

Huang

Wang

et al. 2021

Proceedings of the 15th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Background. Where to start contributing to a project is a critical challenge for newcomers of open source projects. To support newcomers, GitHub utilizes the Good First Issue (GFI) label, with which project members can manually tag issues in an open source project that are suitable for the newcomers. However, manually labeling GFIs is time-and effort-consuming given the large number of candidate issues. In addition, project members need to have a close understanding of the project to label GFIs accurately.Aims. This paper aims at providing a thorough understanding of the characteristics of GFIs and an automatic approach in GFIs prediction, to reduce the burden of project members and help newcomers easily onboard.Method. We first define 79 features to characterize the GFIs and further analyze the correlation between each feature and GFIs. We then build machine learning models to predict GFIs with the proposed features.Results. Experiments are conducted with 74,780 issues from 10 open source projects from GitHub. Results show that features related to the semantics, readability, and text richness of issues can be used to effectively characterize GFIs. Our prediction model achieves a median AUC of 0.88. Results from our user study further prove its potential practical value.Conclusions. This paper provides new insights and practical guidelines to facilitate the understanding of GFIs and the automation of GFIs labeling. CCS CONCEPTS• Software and its engineering → Open source model.

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