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Bidirectional LSTM for Electronic Product Recommendation |
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PP: 1443-1453 |
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doi:10.18576/amis/180621
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Author(s) |
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Asokan Vasudevan,
Albinaa T. A.,
Suleiman Ibrahim Mohammad,
Sharmila E.,
N. Raja,
Eddie Eu Hui Soon,
Muhammad Turki Alshuridehi,
Ahmad Samed Al-Adwan,
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Abstract |
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| In today’s retail landscape, the surge of online e-commerce platforms, especially in the electronics sector, has become ubiquitous, presenting a significant challenge of guiding customers towards relevant items. The proposed system addresses this challenge by leveraging Bidirectional LSTM neural network models, which offer more accuracy than traditional collaborative and content-based filtering methods, to deliver precise recommendations tailored to individual user preferences. Integration of speech technology enhances user interaction by vocalizing recommended products, thereby enhancing the overall user experience. The system’s use of advanced algorithms such as Bidirectional LSTM for recommendation not only enables businesses to make informed decisions but also enhances their product offerings, ultimately helping them to stay competitive in the e-commerce landscape. Overall, Recommendation Systems for User Satisfaction revolutionize e-commerce by simplifying decision-making, enhancing satisfaction, and driving sales through personalized product suggestions and seamless user interaction. Having recommendation systems that are focused on user satisfaction in a way that they not only completely change the e-commerce setting but also show changes in the online business’ approach to their customers is an interesting way to look at it. One of the most critical aspects of this system is that it not only caters to the user’s needs but also uses advanced algorithms to get them better services, thus, the system makes it possible for us to improve our decision-making skills and better the customer experience in the quickly changing online retail.
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