An Approach to Utilize E-commerce Product Reviews to Remove Irrelevant Recommendations

Chehal, Dimple; Gupta, Pratiksha; Gulati, Payal

doi:10.1109/delcon54057.2022.9753277

Cited by 2 publications

(1 citation statement)

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“…The experimental results revealed that the convolutional neural network recommendation algorithm based on deep learning outperformed the other algorithms. In another study [16], Chehal et al proposed an approach to improve recommendations by taking advantage of the product evaluation feature. This method involves extracting product features from user-provided feedback and not recommending products with irrelevant features in order to improve the recommendation list.…”

Section: Related Workmentioning

confidence: 99%

Machine learning based recommender system for e-commerce

Loukili

Messaoudi

Ghazi

2023

IJ-AI

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<span>Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.</span>

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Section: Related Workmentioning

confidence: 99%