Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Improving top-n recommendations with user consuming profiles
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Learning User Preference Patterns for Top-N Recommendations
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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This paper describes one possible way to solve task "Who rated what?" of the KDD CUP 2007. The proposed solution is a history-based model that predicts whether a user will vote a given movie. Key points to our approach are (1) the estimation of the model baseline, (2) the definition of the explanatory variables and (3) the mathematical model form. Given the binary outcome of the problem, the estimation of the true baseline (ratio of 1's in the test data) is critical in order to correctly make predictions. In parallel, to improve the model predictive power, we have developed a careful construction of the input variables. These explanatory variables can be grouped as: user voting behaviour variables, the movie characteristics and user-movie interactions. Finally, the mathematical model form (linear logistic regression) has been chosen among various model form competitors.