Commodity Recommendation for Users Based on E-commerce Data
| Autor: | Lei Zhang, Jingchang Pan, Jiying Lang, Fei Yang, Weigang Lu, Lei Liu, Xudong Han |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Online and offline
Gradient boosting decision tree Database business.industry Computer science 05 social sciences 0102 computer and information sciences E-commerce Python (programming language) computer.software_genre 01 natural sciences Popularity Open data 010201 computation theory & mathematics 0502 economics and business 050211 marketing business Mobile device computer computer.programming_language |
| Zdroj: | ICBDR |
| DOI: | 10.1145/3291801.3291803 |
| Popis: | With the popularity of mobile devices and the development of e-commerce, more and more people choose to buy items in the mobile terminal. Therefore the mobile terminal commodity recommendation services and commodity recommendation algorithms are more and more important. Aim at this problem, this paper conducts a study of predicting the user's purchase behavior based on the online distribution platform and the desensitization data sets provided by the Chinese largest electricity platform Alibaba. Based on the GBDT (Gradient Boosting Decision Tree) model, by using ODPS (Open Data Processing Service) and Python to simultaneously implement machine learning and training online and offline respectively, and combining with the user behavior sequence recorded over a period of time, the user purchase behavior at a later time will be properly predicted. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|