With the rapid development of internet technology, the field of e-commerce has experienced unprecedented growth. The rapid expansion of the user base, as well as the explosive increase in the variety and quantity of products, has greatly driven the development of the internet economy. However, this growth has also brought about the issue of "information overload," where users find it difficult to make effective choices and decisions when faced with a vast amount of information. As a result, how to efficiently extract valuable information from users' historical behavior data and, combined with their current context, accurately recommend products that match their needs and preferences has become a significant technical challenge and research topic of interest to both academia and industry. This challenge not only involves providing personalized services and improving user experience but also concerns the enhancement of business conversion rates. In this paper, we combine manually engineered features with location information, apply feature sparsification, and use a large-scale logistic regression model to compare the performance of a basic logistic regression model, random forest model, and GBDT model. The research results demonstrate that the logistic regression model, incorporating manually crafted cross-features and location information, significantly improves the F1 score.

Guo Jiajie. Research on Mobile E-commerce Recommendation Algorithms Based on Logistic Regression Improved Model Features. Academic Journal of Engineering and Technology Science (2024) Vol. 7, Issue 5: 111-115. https://doi.org/10.25236/AJETS.2024.070514.

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