A Comprehensive Recommendation System for Online Shopping Based on the Google Dataset
- DOI
- 10.2991/978-94-6463-370-2_33How to use a DOI?
- Keywords
- Recommendation system; Natural language processing; Singular Value Decomposition
- Abstract
With the upgrading of online shopping software, intelligent recommendation systems have emerged in various software and become the main recommendation engine of contemporary times. This article outlines the construction of an all-encompassing recommendation system, drawing inspiration from prior collaborative filtering concepts. The system undergoes training and testing using the Google Shopping dataset. Scoring and evaluation texts are treated as distinct components, later combined to create a versatile recommendation system. Post-testing, the system is capable of furnishing recommendations based on all available data without user input. Alternatively, it can offer personalized recommendations derived from users’ purchase histories, providing a selection of the top 15 recommendations to align with contemporary shopping software prerequisites. The purchase index with a test result of 63.0% for the entire dataset indicates that it fully meets user purchasing needs, but the average purchase rate is only 7.99%, and there is still room for improvement in this case.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Dongming Chen PY - 2024 DA - 2024/02/14 TI - A Comprehensive Recommendation System for Online Shopping Based on the Google Dataset BT - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023) PB - Atlantis Press SP - 305 EP - 312 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-370-2_33 DO - 10.2991/978-94-6463-370-2_33 ID - Chen2024 ER -