A systematic study on the recommender systems in the e-commerce


Electronic commerce, sometimes known as e-commerce, is the exchange of goods and services using electronic means such as the Internet. It is extremely important in today's company and user experience. E-commerce platforms also generate a large amount of data.

As a result, Recommender Systems (RSs) are a solution to the problem of information overload. To boost user satisfaction, they make individualized recommendations. The current article depicts a thorough and systematic literature review (SLR) of publications published in the subject of e-commerce recommender systems.

We looked over the articles to see where the holes and flaws were in the RSs' traditional methodologies, and how they could be used to drive future research. As a result, based on the selected publications, we gave traditional methodologies, problems, and outstanding concerns surrounding traditional methods of review.

Content-Based Filtering (CBF), Collaborative Filtering (CF), Demographic-Based Filtering (DBF), Hybrid Filtering, and Knowledge-Based Filtering are among the five categories of RS algorithms covered in this research (KBF). In addition, the key points of each selected work are summarized.

The papers that were chosen were published between 2008 and 2019. We have included a comparison table of the selected papers' key issues, as well as tables of benefits and drawbacks. In addition, for the selected publications, we presented a comparative table of metrics and review problems. Finally, the findings can, to a large part, serve as useful guidance for future research.

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