Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field

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Almahmood R. J. K., TEKEREK A.

APPLIED SCIENCES-BASEL, vol.12, no.21, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Review
  • Volume: 12 Issue: 21
  • Publication Date: 2022
  • Doi Number: 10.3390/app122111256
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: recommender systems, e-commerce, deep learning, similarity, product review analysis, sparsity, cold-start, sentiment analysis
  • Gazi University Affiliated: Yes


In recent years, especially with the (COVID-19) pandemic, shopping has been a challenging task. Increased online shopping has increased information available via the World Wide Web. Finding new products or identifying the most suitable products according to customers' personalization trends is the main benefit of E-commerce recommendation systems, which use different techniques such as rating, ranking, or reviewing. These recommendations can be formed using different techniques and approaches, particularly using the technology of intelligent agents, and specific interfaces or personal agents can be used to model this type of system. These agents usually use the techniques and algorithms of Artificial Intelligence internally. A recommendation system is a prediction system that has been created to help the user to select the proper product for them, and to reduce the effort spent in the search process using advanced technology such as deep learning techniques. We investigate all studies using a standard review process for collecting and retrieving data from previous studies and illustrate their relevant accuracy and interpretability along with pros and cons helpful to business firms to adopt the most legitimate approach. The study's findings revealed that recommendation problems are solved better by using deep learning algorithms such as CNN, RNN, and sentiment analysis, especially for popular problems such as cold start and sparsity.