CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
A Novel Framework for Repeat Buyers Prediction with Feature Engineering
Zhang B(张斌); Bin, Zhang
Conference NameWorkshop on Social Influence Analysis, IJCAI, 2015
Source PublicationIJCAI
Conference Date20150725
Conference PlaceBuenos Aires, Argentina

In this paper, we report the solution of our team ”Farewell” on the IJCAI 15 competition. This com- petition provides a set of merchants and their cor- responding new buyers acquired during the promo- tion on the ”Double 11” Day and the task is to predict which users will become loyal customers for given merchants in the future. To solve this problem, we firstly do a lot of work in feature engineering. Features are extracted from various aspects and divided into several categories. Be- sides, we adopt some innovative methods to gen- erate latent factor features and user-merchant co- cluster features. In the first stage of the compe- tition, we employ two individual models, Factor- ization Machines(FM) and Gradient Boosting De- cision Tree(GBDT) and we adopt Logistic Regres- sion(LR) model in the second stage. Afterwards, we propose a weighted linear ensemble model to combine results of individual models. Our final re- sult is the blending of FM model and GBDT model in stage 1 and result of LR model in stage 2. 

KeywordFeature Engineering
Document Type会议论文
Corresponding AuthorBin, Zhang
Recommended Citation
GB/T 7714
Zhang B,Bin, Zhang. A Novel Framework for Repeat Buyers Prediction with Feature Engineering[C],2015.
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