IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v23y2023i1d10.1007_s10660-022-09630-z.html
   My bibliography  Save this article

RETRACTED ARTICLE: Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis

Author

Listed:
  • Arodh Lal Karn

    (Xi’an Jiaotong-Liverpool University)

  • Rakshha Kumari Karna

    (Harbin Institute of Technology)

  • Bhavana Raj Kondamudi

    (Institute of Public Enterprise)

  • Girish Bagale

    (SVKM’s NMIMS University)

  • Denis A. Pustokhin

    (State University of Management)

  • Irina V. Pustokhina

    (Plekhanov Russian University of Economics)

  • Sudhakar Sengan

    (PSN College of Engineering and Technology)

Abstract

Internet applications such as Online Social Networking and Electronic commerce are becoming incredibly common, resulting in more content being available. Recommender systems (RS) assist users in identifying appropriate information out of a large pool of options. In today’s internet applications, RS are extremely important. To increase user satisfaction, this type of system supports personalized recommendations based on a massive volume of data. These suggestions assist clients in selecting products, while concerns can increase product utilization. We discovered that much research work is going on in the field of recommendation and that there are some effective systems out there. In the context of social information, sentimental analysis (SA) can aid in increasing the knowledge of a user’s behaviour, views, and reactions, which is helpful for incorporating into RS to improve recommendation accuracy. RS has been found to resolve information overload challenges in information retrieval, but they still have issues with cold-start and data sparsity. SA, on the other hand, is well-known for interpreting text and conveying user choices. It’s frequently used to assist E-Commerce in tracking customer feedback on their products and attempting to comprehend customer needs and preferences. To improve the accuracy and correctness of any RS, this paper proposes a recommendation model based on a Hybrid Recommendation Model (HRM) and hybrid SA. In the proposed method, we first generate a preliminary recommendation list using a HRM. To generate the final recommendation list, the HRM with SA is used. In terms of various evaluation criteria, the HRM with SA outperforms traditional models.

Suggested Citation

  • Arodh Lal Karn & Rakshha Kumari Karna & Bhavana Raj Kondamudi & Girish Bagale & Denis A. Pustokhin & Irina V. Pustokhina & Sudhakar Sengan, 2023. "RETRACTED ARTICLE: Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis," Electronic Commerce Research, Springer, vol. 23(1), pages 279-314, March.
    • Handle: RePEc:spr:elcore:v:23:y:2023:i:1:d:10.1007_s10660-022-09630-z
    DOI: 10.1007/s10660-022-09630-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-022-09630-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-022-09630-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Laisong Kang & Shifeng Liu & Daqing Gong & Mincong Tang, 2021. "A personalized point-of-interest recommendation system for O2O commerce," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 253-267, June.
    2. Supratim Kundu & Swapnajit Chakraborti, 2022. "A comparative study of online consumer reviews of Apple iPhone across Amazon, Twitter and MouthShut platforms," Electronic Commerce Research, Springer, vol. 22(3), pages 925-950, September.
    3. Xiao-qiang Wu & Lei Zhang & Song-ling Tian & Lan Wu, 2021. "Scenario based e-commerce recommendation algorithm based on customer interest in Internet of things environment," Electronic Commerce Research, Springer, vol. 21(3), pages 689-705, September.
    4. Yuanyuan Zhuang & Jaekyeong Kim, 2021. "A BERT-Based Multi-Criteria Recommender System for Hotel Promotion Management," Sustainability, MDPI, vol. 13(14), pages 1-18, July.
    5. Mojtaba Salehi, 2013. "An effective recommendation based on user behaviour: a hybrid of sequential pattern of user and attributes of product," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 14(4), pages 480-496.
    6. Wafa Shafqat & Yung-Cheol Byun, 2020. "A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model," Sustainability, MDPI, vol. 12(10), pages 1-23, May.
    7. Lichun Zhou, 2020. "Product advertising recommendation in e-commerce based on deep learning and distributed expression," Electronic Commerce Research, Springer, vol. 20(2), pages 321-342, June.
    8. Payam Hanafizadeh & Mahdi Barkhordari Firouzabadi & Khuong Minh Vu, 2021. "Insight monetization intermediary platform using recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 269-293, June.
    9. Nurul Aida Osman & Shahrul Azman Mohd Noah & Mohammad Darwich & Masnizah Mohd, 2021. "Integrating contextual sentiment analysis in collaborative recommender systems," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-21, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Farah Tawfiq Abdul Hussien & Abdul Monem S. Rahma & Hala B. Abdulwahab, 2021. "An E-Commerce Recommendation System Based on Dynamic Analysis of Customer Behavior," Sustainability, MDPI, vol. 13(19), pages 1-21, September.
    2. Ravi S. Sharma & Aijaz A. Shaikh & Eldon Li, 2021. "Designing Recommendation or Suggestion Systems: looking to the future," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 243-252, June.
    3. Saravanan Thirumuruganathan & Soon-gyo Jung & Dianne Ramirez Robillos & Joni Salminen & Bernard J. Jansen, 2021. "Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?," Electronic Commerce Research, Springer, vol. 21(1), pages 73-100, March.
    4. Robert RUSU & Constantin AVRAM, 2022. "Deep Learning Systems Integrated into the Digital Strategy of a Company Involved in e-commerce," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 1, pages 5-10.
    5. Maela Madel L. Cahigas & Ardvin Kester S. Ong & Yogi Tri Prasetyo, 2023. "Super Typhoon Rai’s Impacts on Siargao Tourism: Deciphering Tourists’ Revisit Intentions through Machine-Learning Algorithms," Sustainability, MDPI, vol. 15(11), pages 1-29, May.
    6. Aitor Goti & Leire Querejeta-Lomas & Aitor Almeida & José Gaviria de la Puerta & Diego López-de-Ipiña, 2023. "Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review," Mathematics, MDPI, vol. 11(13), pages 1-32, June.
    7. Mozhu Wang & Jianming Yao, 2023. "A reliable location design of unmanned vending machines based on customer satisfaction," Electronic Commerce Research, Springer, vol. 23(1), pages 541-575, March.
    8. Luis Filipe Lages & Nuno Catarino & Emanuel Gomes & Peter Toh & Carlos Reis-Marques & Mario Mohr & Sebastian Max Borde & Omid Asgari & Ronnie Figueiredo & Nuno Grosso & David Perez & Ana Ponte & Sílvi, 2023. "Solutions for the commercialization challenges of Horizon Europe and earth observation consortia: co-creation, innovation, decision-making, tech-transfer, and sustainability actions," Electronic Commerce Research, Springer, vol. 23(3), pages 1621-1663, September.
    9. Miao, Ruomu & Li, Benqian, 2022. "A user-portraits-based recommendation algorithm for traditional short video industry and security management of user privacy in social networks," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    10. Jaewoong Choi & Jiho Lee & Janghyeok Yoon & Sion Jang & Jaeyoung Kim & Sungchul Choi, 2022. "A two-stage deep learning-based system for patent citation recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6615-6636, November.
    11. Maria Cristina ENACHE, 2023. "Python for e-Commerce," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 122-127.
    12. Weiwei Deng, 2022. "Leveraging consumer behaviors for product recommendation: an approach based on heterogeneous network," Electronic Commerce Research, Springer, vol. 22(4), pages 1079-1105, December.
    13. Bernd Heinrich & Marcus Hopf & Daniel Lohninger & Alexander Schiller & Michael Szubartowicz, 2022. "Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems," Information Systems Frontiers, Springer, vol. 24(1), pages 267-286, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elcore:v:23:y:2023:i:1:d:10.1007_s10660-022-09630-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.