KDD
2010
ACM
13 years 8 months ago
2010
ACM
We analyze the application of ensemble learning to recommender systems on the Netflix Prize dataset. For our analysis we use a set of diverse state-of-the-art collaborative filt...
KDD
2010
ACM
13 years 8 months ago
2010
ACM
In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing...
KDD
2010
ACM
13 years 8 months ago
2010
ACM
KDD
2010
ACM
13 years 8 months ago
2010
ACM
Social tagging systems have become increasingly popular for sharing and organizing web resources. Tag recommendation is a common feature of social tagging systems. Social tagging ...
KDD
2010
ACM
13 years 8 months ago
2010
ACM
Classification is one of the most essential tasks in data mining. Unlike other methods, associative classification tries to find all the frequent patterns existing in the input...
KDD
2010
ACM
13 years 8 months ago
2010
ACM
In the traditional link prediction problem, a snapshot of a social network is used as a starting point to predict, by means of graph-theoretic measures, the links that are likely ...
KDD
2010
ACM
13 years 8 months ago
2010
ACM
Compressing social networks can substantially facilitate mining and advanced analysis of large social networks. Preferably, social networks should be compressed in a way that they...
KDD
2010
ACM
13 years 8 months ago
2010
ACM
Users’ behaviors (actions) in a social network are influenced by various factors such as personal interests, social influence, and global trends. However, few publications sys...
KDD
2010
ACM
13 years 8 months ago
2010
ACM
We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, so...
KDD
2010
ACM
13 years 8 months ago
2010
ACM
In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (ex...