8 Papers
Li Shi is an academic researcher from Beijing University of Chemical Technology. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 1, co-authored 3 publications.
Chat about Author
Papers
An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm
TL;DR: The effectiveness of the method proposed in this paper is supported by improvement results of some key performance indices such as the growth of active customers, total purchase volume, and the total consumption amount.
User Value Identification Based on Improved RFM Model and K -Means++ Algorithm for Complex Data Analysis
TL;DR: Zhang et al. as discussed by the authors used an improved RFM model to extract user features and used the -means++ clustering algorithm to realize user classification to accurately identify the user value of online purchasing on an e-commerce platform, which can not only improve user satisfaction but also reduce platform marketing cost.
Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method
TL;DR: In this article , the authors used eXtreme Gradient Boosting (XGBoost), a machine learning technique, to predict the career choice of college students using a real-world dataset collected in a specific college.
ReRec: A Divide-and-Conquer Approach to Recommendation Based on Repeat Purchase Behaviors of Users in Community E-Commerce
TL;DR: This paper is the first to study recommendations in community e-commerce and proposes a novel approach called ReRec (Repeat purchase Recommender) for real-life applications, to model the repeat purchase behaviors of different types of users.
9
Using the Mathematical Model on Precision Marketing with Online Transaction Data Computing
TL;DR: A decision-making system for precision marketing is presented to deal with real-world problems based on real e-business data collected in a company in Beijing and a series of precision marketing strategies which had been adopted by the data source company and had been proved to be effective in improving the performance are proposed.