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Leveraging user session for personalized e- commerce recommendation
The advent of the internet has propelled many shopping activities online, leading to the rapid growth of e- commerce. This shift has revolutionized the shopping experience, offering unparalleled convenience with anytime, anywhere access via computers and internet connectivity. Moreover, the vast arr...
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| Format: | Article |
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2024-07
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| LEADER | 00000njm a2000000a 4500 | ||
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| 001 | oai:repository.ui.edu.ng:123456789/11387 | ||
| 042 | |a dc | ||
| 720 | |a Onibonoje, S. |e author | ||
| 720 | |a Ojo, A. |e author | ||
| 260 | |c 2024-07 | ||
| 520 | |a The advent of the internet has propelled many shopping activities online, leading to the rapid growth of e- commerce. This shift has revolutionized the shopping experience, offering unparalleled convenience with anytime, anywhere access via computers and internet connectivity. Moreover, the vast array of easily accessible choices empowers buyers to make well-informed decisions. Numerous websites have emerged to provide e-commerce services, catering either as a complement to physical stores or as standalone businesses. However, the abundance of offerings often leads to information overload for buyers, making product searches time-consuming and frustrating. Personalized e-commerce recommendations alleviate this challenge by guiding users to relevant products swiftly, enhancing the overall shopping experience and ultimately boosting product sales. The study focuses on creating a session-based recommendation system for e-commerce websites, leveraging Recurrent Neural Networks with LSTM architectures to analyze sequential user behavior and browsing context for personalized product recommendations. The research methodology encompasses data collection and preprocessing, where data was splitted into training, testing and validation set. The model was efficiency was evaluated using precision, recall and mean reciprocal rank with the result showing considerable promise for recommendation. This research makes a substantial contribution by suggesting tailored options, users are more likely to find suitable products, leading to increased satisfaction and repeat purchases, thereby benefiting e-commerce platforms. | ||
| 024 | 8 | |a 2454-6194 | |
| 024 | 8 | |a ui_art_ojo_leveraging_2024 | |
| 024 | 8 | |a International Journal of Research and Innovation in Applied Science 9(7), pp. 86-96 | |
| 024 | 8 | |a https://repository.ui.edu.ng/handle/123456789/11387 | |
| 653 | |a E-commerce | ||
| 653 | |a Recurrent Neural Networks | ||
| 653 | |a User behavior analysis | ||
| 653 | |a Session-based recommendation system | ||
| 245 | 0 | 0 | |a Leveraging user session for personalized e- commerce recommendation |