| _version_ | 1866902123537498112 |
|---|---|
| author | Sujata Helonde Pooja Mhaske Arpan Pandey Daksh Raut Somya Binekar |
| author_facet | Sujata Helonde Pooja Mhaske Arpan Pandey Daksh Raut Somya Binekar |
| contents | <p>The rapid advancement of technology has enabled retailers to leverage predictive analytics and machine learning to enhance the shopping experience. This paper proposes a conceptual framework for a prediction tool for Amazon Go, which would suggest complementary or related items to customers during their shopping journey. For instance, if a customer purchases a toothbrush, the tool may recommend toothpaste, mouthwash, or dental floss. While the tool has not yet been implemented, this study explores its potential benefits, challenges, and customer expectations based on hypothetical scenarios and existing literature. The findings suggest that the prediction tool has the potential to revolutionize the shopping experience by making it more personalized and efficient. However, privacy concerns and the need for accurate suggestions must be addressed to ensure widespread adoption.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15077182 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | REVIEW PAPER ON AMAZON GO PREDECTION USING MACHINE LEARNING Sujata Helonde Pooja Mhaske Arpan Pandey Daksh Raut Somya Binekar <p>The rapid advancement of technology has enabled retailers to leverage predictive analytics and machine learning to enhance the shopping experience. This paper proposes a conceptual framework for a prediction tool for Amazon Go, which would suggest complementary or related items to customers during their shopping journey. For instance, if a customer purchases a toothbrush, the tool may recommend toothpaste, mouthwash, or dental floss. While the tool has not yet been implemented, this study explores its potential benefits, challenges, and customer expectations based on hypothetical scenarios and existing literature. The findings suggest that the prediction tool has the potential to revolutionize the shopping experience by making it more personalized and efficient. However, privacy concerns and the need for accurate suggestions must be addressed to ensure widespread adoption.</p> |
| title | REVIEW PAPER ON AMAZON GO PREDECTION USING MACHINE LEARNING |
| url | https://doi.org/10.5281/zenodo.15077182 |