Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches

The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice. This paper describes the solution of team "binghsu & MLRush & BrickMover". We conduct simple feature engineering work and train different models by each individual team member. Afterwards, we use listwise ensemble method to combine each model's output. Besides describing effective model and features, we will discuss about the lessons we learned while using deep learning in this competition.

[1]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[2]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[3]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[4]  Thorsten Joachims,et al.  Training linear SVMs in linear time , 2006, KDD '06.

[5]  Hang Li,et al.  AdaRank: a boosting algorithm for information retrieval , 2007, SIGIR.

[6]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[7]  Qiang Wu,et al.  Adapting boosting for information retrieval measures , 2010, Information Retrieval.

[8]  Christopher J. C. Burges,et al.  From RankNet to LambdaRank to LambdaMART: An Overview , 2010 .

[9]  Tie-Yan Liu,et al.  Learning to rank for information retrieval , 2009, SIGIR.

[10]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[11]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[12]  Wes McKinney,et al.  pandas: a Foundational Python Library for Data Analysis and Statistics , 2011 .

[13]  Gilles Louppe,et al.  Learning to rank with extremely randomized trees , 2010, Yahoo! Learning to Rank Challenge.

[14]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[15]  H. Brendan McMahan,et al.  Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization , 2011, AISTATS.

[16]  Tie-Yan Liu,et al.  Learning to Rank for Information Retrieval , 2011 .

[17]  Steffen Rendle,et al.  Factorization Machines with libFM , 2012, TIST.

[18]  Ian J. Goodfellow,et al.  Pylearn2: a machine learning research library , 2013, ArXiv.

[19]  Chuang Zhang,et al.  Horizontal and Vertical Ensemble with Deep Representation for Classification , 2013, ArXiv.

[20]  Yoshua Bengio,et al.  Maxout Networks , 2013, ICML.