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.
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Cheng, B., Chen, T., Yang, D., Zhang, W., Wang, Y., Yu, Y. (2012). Feature Based Informative Model for Discriminating Favorite Items from Unrated Ones. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_13
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DOI: https://doi.org/10.1007/978-3-642-29253-8_13
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