Hemn Barzan Abdalla, Mehdi Gheisari, Ardalan Hussein Awlla
{"title":"用于电子商务推荐系统的混合自注意 BiLSTM 和基于激励学习的协同过滤技术","authors":"Hemn Barzan Abdalla, Mehdi Gheisari, Ardalan Hussein Awlla","doi":"10.1007/s10660-024-09888-5","DOIUrl":null,"url":null,"abstract":"<p>Sentiment prediction means analyzing the emotional tone or opinion contained in textual data such as reviews or posts on social media. Recommendation systems use this sentiment analysis to recommend appropriate products or content to the users. The limitations of the existing model are related to data quality and quantity issues and dealing with different scenes and languages. Hence, to overcome all these challenges, the Hybrid Self-Attention Layer Optimized Incentive Learning–based Collaborative Filtering-BiLSTM (Hybrid AT-IN based CF-BiLSTM) model was developed for sentiment prediction and recommendation based on e-commerce platforms. The usage of CF, BiLSTM networks, and a hybrid self-attention mechanism ensure the model's unrivaled precision and performance in the domain of sentiment analysis and consumer preferences. Relying on the CF, the model accumulates valuable data about user-item interactions, and BiLSTM networks process the text, employing information from the surrounding context. The model utilizes a hybrid self-attention mechanism that automatically assigns weights based on the importance of words in user reviews; this allows it to focus on the main features and improve understanding of sentiments. Moreover, applying incentive learning lets the model adapt and optimize recommendations based on changing user behaviors, leading to greater user satisfaction and engagement. In particular, the CCO-TLI model showcases significantly superior values with 98.03% accuracy, the lowest mean square error value of 1.42, 98.68% precision, 97.04% recall, and the smallest root mean squared error of 1.19 compared to existing models.</p>","PeriodicalId":47264,"journal":{"name":"Electronic Commerce Research","volume":"173 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid self-attention BiLSTM and incentive learning-based collaborative filtering for e-commerce recommendation systems\",\"authors\":\"Hemn Barzan Abdalla, Mehdi Gheisari, Ardalan Hussein Awlla\",\"doi\":\"10.1007/s10660-024-09888-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Sentiment prediction means analyzing the emotional tone or opinion contained in textual data such as reviews or posts on social media. Recommendation systems use this sentiment analysis to recommend appropriate products or content to the users. The limitations of the existing model are related to data quality and quantity issues and dealing with different scenes and languages. Hence, to overcome all these challenges, the Hybrid Self-Attention Layer Optimized Incentive Learning–based Collaborative Filtering-BiLSTM (Hybrid AT-IN based CF-BiLSTM) model was developed for sentiment prediction and recommendation based on e-commerce platforms. The usage of CF, BiLSTM networks, and a hybrid self-attention mechanism ensure the model's unrivaled precision and performance in the domain of sentiment analysis and consumer preferences. Relying on the CF, the model accumulates valuable data about user-item interactions, and BiLSTM networks process the text, employing information from the surrounding context. The model utilizes a hybrid self-attention mechanism that automatically assigns weights based on the importance of words in user reviews; this allows it to focus on the main features and improve understanding of sentiments. Moreover, applying incentive learning lets the model adapt and optimize recommendations based on changing user behaviors, leading to greater user satisfaction and engagement. In particular, the CCO-TLI model showcases significantly superior values with 98.03% accuracy, the lowest mean square error value of 1.42, 98.68% precision, 97.04% recall, and the smallest root mean squared error of 1.19 compared to existing models.</p>\",\"PeriodicalId\":47264,\"journal\":{\"name\":\"Electronic Commerce Research\",\"volume\":\"173 1\",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electronic Commerce Research\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1007/s10660-024-09888-5\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronic Commerce Research","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1007/s10660-024-09888-5","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
Hybrid self-attention BiLSTM and incentive learning-based collaborative filtering for e-commerce recommendation systems
Sentiment prediction means analyzing the emotional tone or opinion contained in textual data such as reviews or posts on social media. Recommendation systems use this sentiment analysis to recommend appropriate products or content to the users. The limitations of the existing model are related to data quality and quantity issues and dealing with different scenes and languages. Hence, to overcome all these challenges, the Hybrid Self-Attention Layer Optimized Incentive Learning–based Collaborative Filtering-BiLSTM (Hybrid AT-IN based CF-BiLSTM) model was developed for sentiment prediction and recommendation based on e-commerce platforms. The usage of CF, BiLSTM networks, and a hybrid self-attention mechanism ensure the model's unrivaled precision and performance in the domain of sentiment analysis and consumer preferences. Relying on the CF, the model accumulates valuable data about user-item interactions, and BiLSTM networks process the text, employing information from the surrounding context. The model utilizes a hybrid self-attention mechanism that automatically assigns weights based on the importance of words in user reviews; this allows it to focus on the main features and improve understanding of sentiments. Moreover, applying incentive learning lets the model adapt and optimize recommendations based on changing user behaviors, leading to greater user satisfaction and engagement. In particular, the CCO-TLI model showcases significantly superior values with 98.03% accuracy, the lowest mean square error value of 1.42, 98.68% precision, 97.04% recall, and the smallest root mean squared error of 1.19 compared to existing models.
期刊介绍:
The Internet and the World Wide Web have brought a fundamental change in the way that individuals access data, information and services. Individuals have access to vast amounts of data, to experts and services that are not limited in time or space. This has forced business to change the way in which they conduct their commercial transactions with their end customers and with other businesses, resulting in the development of a global market through the Internet. The emergence of the Internet and electronic commerce raises many new research issues. The Electronic Commerce Research journal will serve as a forum for stimulating and disseminating research into all facets of electronic commerce - from research into core enabling technologies to work on assessing and understanding the implications of these technologies on societies, economies, businesses and individuals. The journal concentrates on theoretical as well as empirical research that leads to better understanding of electronic commerce and its implications. Topics covered by the journal include, but are not restricted to the following subjects as they relate to the Internet and electronic commerce: Dissemination of services through the Internet;Intelligent agents technologies and their impact;The global impact of electronic commerce;The economics of electronic commerce;Fraud reduction on the Internet;Mobile electronic commerce;Virtual electronic commerce systems;Application of computer and communication technologies to electronic commerce;Electronic market mechanisms and their impact;Auctioning over the Internet;Business models of Internet based companies;Service creation and provisioning;The job market created by the Internet and electronic commerce;Security, privacy, authorization and authentication of users and transactions on the Internet;Electronic data interc hange over the Internet;Electronic payment systems and electronic funds transfer;The impact of electronic commerce on organizational structures and processes;Supply chain management through the Internet;Marketing on the Internet;User adaptive advertisement;Standards in electronic commerce and their analysis;Metrics, measurement and prediction of user activity;On-line stock markets and financial trading;User devices for accessing the Internet and conducting electronic transactions;Efficient search techniques and engines on the WWW;Web based languages (e.g., HTML, XML, VRML, Java);Multimedia storage and distribution;Internet;Collaborative learning, gaming and work;Presentation page design techniques and tools;Virtual reality on the net and 3D visualization;Browsers and user interfaces;Web site management techniques and tools;Managing middleware to support electronic commerce;Web based education, and training;Electronic journals and publishing on the Internet;Legal issues, taxation and property rights;Modeling and design of networks to support Internet applications;Modeling, design and sizing of web site servers;Reliability of intensive on-line applications;Pervasive devices and pervasive computing in electronic commerce;Workflow for electronic commerce applications;Coordination technologies for electronic commerce;Personalization and mass customization technologies;Marketing and customer relationship management in electronic commerce;Service creation and provisioning. Audience: Academics and professionals involved in electronic commerce research and the application and use of the Internet. Managers, consultants, decision-makers and developers who value the use of electronic com merce research results. Special Issues: Electronic Commerce Research publishes from time to time a special issue of the devoted to a single subject area. If interested in serving as a guest editor for a special issue, please contact the Editor-in-Chief J. Christopher Westland at westland@uic.edu with a proposal for the special issue. Officially cited as: Electron Commer Res