Margin-Based Algorithms for Information Filtering

In this work, we study an information filtering model where the relevance labels associated to a sequence of feature vectors are realizations of an unknown probabilistic linear function. Building on the analysis of a re- stricted version of our model, we derive a general filtering rule based on the margin of a ridge regression estimator. While our rule may observe the label of a vector only by classfying the vector as relevant, experiments on a real-world document filtering problem show that the performance of our rule is close to that of the on-line classifier which is allowed to observe all labels. These empirical results are complemented by a theo- retical analysis where we consider a randomized variant of our rule and prove that its expected number of mistakes is never much larger than that of the optimal filtering rule which knows the hidden linear model.

Authors

• Nicolò Cesa-Bianchi Universita degli Studi di Milano
• Alex Conconi
• Claudio Gentile

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