Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911818442604544 |
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| author | Xu, Kangming Zhou, Huiming Zheng, Haotian Zhu, Mingwei Xin, Qi |
| author_facet | Xu, Kangming Zhou, Huiming Zheng, Haotian Zhu, Mingwei Xin, Qi |
| contents | With the rapid evolution of the Internet and the exponential proliferation of information, users encounter information overload and the conundrum of choice. Personalized recommendation systems play a pivotal role in alleviating this burden by aiding users in filtering and selecting information tailored to their preferences and requirements. Such systems not only enhance user experience and satisfaction but also furnish opportunities for businesses and platforms to augment user engagement, sales, and advertising efficacy.This paper undertakes a comparative analysis between the operational mechanisms of traditional e-commerce commodity classification systems and personalized recommendation systems. It delineates the significance and application of personalized recommendation systems across e-commerce, content information, and media domains. Furthermore, it delves into the challenges confronting personalized recommendation systems in e-commerce, including data privacy, algorithmic bias, scalability, and the cold start problem. Strategies to address these challenges are elucidated.Subsequently, the paper outlines a personalized recommendation system leveraging the BERT model and nearest neighbor algorithm, specifically tailored to address the exigencies of the eBay e-commerce platform. The efficacy of this recommendation system is substantiated through manual evaluation, and a practical application operational guide and structured output recommendation results are furnished to ensure the system's operability and scalability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19345 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning Xu, Kangming Zhou, Huiming Zheng, Haotian Zhu, Mingwei Xin, Qi Information Retrieval Artificial Intelligence With the rapid evolution of the Internet and the exponential proliferation of information, users encounter information overload and the conundrum of choice. Personalized recommendation systems play a pivotal role in alleviating this burden by aiding users in filtering and selecting information tailored to their preferences and requirements. Such systems not only enhance user experience and satisfaction but also furnish opportunities for businesses and platforms to augment user engagement, sales, and advertising efficacy.This paper undertakes a comparative analysis between the operational mechanisms of traditional e-commerce commodity classification systems and personalized recommendation systems. It delineates the significance and application of personalized recommendation systems across e-commerce, content information, and media domains. Furthermore, it delves into the challenges confronting personalized recommendation systems in e-commerce, including data privacy, algorithmic bias, scalability, and the cold start problem. Strategies to address these challenges are elucidated.Subsequently, the paper outlines a personalized recommendation system leveraging the BERT model and nearest neighbor algorithm, specifically tailored to address the exigencies of the eBay e-commerce platform. The efficacy of this recommendation system is substantiated through manual evaluation, and a practical application operational guide and structured output recommendation results are furnished to ensure the system's operability and scalability. |
| title | Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2403.19345 |