A Two-Stage Ensemble of Diverse Models for Advertisement Ranking in KDD Cup 2012

Abstract

This paper describes the solution of National Taiwan University for track 2 of KDD Cup 2012. Track 2 of KDD Cup 2012 aims to predict the click-through rate of ads on Tencent proprietary search engine. We exploit classication, regression, ranking, and factorization models to utilize a variety of dierent signatures captured from the dataset. We then blend our individual models to boost the performance through two stages, one on an internal validation set and one on the external test set. Our solution achieves 0.8069 AUC on the public test set and 0.8089 AUC on the private test set.

Keywords

Ranking (information retrieval)Computer scienceTest setSet (abstract data type)ExploitData miningMachine learningArtificial intelligenceInformation retrieval

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Publication Info

Year: 2012
Type: article
Citations: 22
Access: Closed

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APA Style

                            
                                    Kuan-Wei Wu, 
                                
                                    Chun-Sung Ferng, 
                                
                                    Chia-Hua Ho
                                
                                et al.
                            
                            (2012). 
                            A Two-Stage Ensemble of Diverse Models for Advertisement Ranking in KDD Cup 2012. 
                            
                            .