Real‐time helpfulness prediction based on voter opinions

This paper studies the problem of designing real‐time helpfulness prediction algorithms. Instead of following the conventional route, in which the fraction of positive votes is used as the measure of helpfulness, we give ‘helpfulness’ a naturally sensible and mathematically precise definition, namely, as the probability that a user will vote ‘helpful’ on the user‐generated content. Building on this definition, we introduce a principled methodology to helpfulness prediction, in which the prediction problem is naturally formulated as an optimization problem. Under this proposed methodology, we first develop a batch (off‐line) algorithm. Experiments on data from Amazon.com suggest that our proposed model in fact outperforms the previously reported prediction algorithm, support vector regression. In some circumstances, an online algorithm that can update the model as additional data arrive is required. In light of this, we proposed an online algorithm that incrementally updates the parameters of the model. Finally, an efficient hybrid algorithm is provided to increase the convergence rate and prediction precision. The final two algorithms are tested on real‐life user‐generated contents, and experimental results illustrate that the hybrid approach efficiently processes incoming data and generates reliable helpfulness predictions for users. Copyright © 2011 John Wiley & Sons, Ltd.