Feature based informative model for discriminating favorite items from unrated ones

Authors:
Bing Cheng;Tianqi Chen;Diyi Yang;Weinan Zhang;Yongqiang Wang;Yong Yu
Affiliations:
Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Year:
2012

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Abstract

In this paper, we describe a feature based informative model to the second track of this year's KDD Cup Challenge. The goal is to discriminate songs rated highly by the user from ones never rated by him/her. The informative model is used to incorporate different kinds of information, such as taxonomy of items, item neighborhoods, user specific features and implicit feedback, into a single model. Additionally, we also adopt ranking oriented SVD and negative sampling to improve prediction accuracy. Our final model achieves an error rate of 3.10% on the test set with a single predictor, which is the best result of single predictors in all the publicized results on this task, even better than many ensemble models.