Online Ranking/Collaborative Filtering Using the Perceptron Algorithm

In this paper we present a simple to implement truly online large margin version of the Perceptron ranking (PRank) algorithm, called the OAP-BPM (Online Aggregate Prank-Bayes Point Machine) algorithm, which finds a rule that correctly ranks a given training sequence of instance and target rank pairs. PRank maintains a weight vector and a set of thresholds to define a ranking rule that maps each instance to its respective rank. The OAP-BPM algorithm is an extension of this algorithm by approximating the Bayes point, thus giving a good generalization performance. The Bayes point is approximated by averaging the weights and thresholds associated with several PRank algorithms run in parallel. In order to ensure diversity amongst the solutions of the PRank algorithms we randomly subsample the stream of incoming training examples. We also introduce two new online versions of Bagging and the voted Perceptron using the same randomization trick as OAP-BPM, hence are referred to as OAP with ext...

• Type: Conference
• Year: 2003
• Where: ICML
• Authors: Edward F. Harrington
• Keywords: ICML 2003, Machine Learning, Online Regression Algorithm, PRank Algorithms, Target Rank Pairs