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A rising topic in computational journalism is how to enhance the diversity innews served to subscribers to foster exploration behavior in news reading.Despite the success of preference learning in personalized news recommendation,their over-exploitation causes filter bubble that isolates readers fromopposing viewpoints and hurts long-term user experiences with lack ofserendipity. Since news providers can recommend neither opposite nordiversified opinions if unpopularity of these articles is surely predicted,they can only bet on the articles whose forecasts of click-through rate involvehigh variability (risks) or high estimation errors (uncertainties). We proposea novel Bayesian model of uncertainty-aware scoring and ranking for newsarticles. The Bayesian binary classifier models probability of success (definedas a news click) as a Beta-distributed random variable conditional on a vectorof the context (user features, article features, and other contextualfeatures). The posterior of the contextual coefficients can be computedefficiently using a low-rank version of Laplace's method via thin SingularValue Decomposition. Efficiencies in personalized targeting of exceptionalarticles, which are chosen by each subscriber in test period, are evaluated onreal-world news datasets. The proposed estimator slightly outperformed existingtraining and scoring algorithms, in terms of efficiency in identifyingsuccessful outliers.

Towards Bursting Filter Bubble via Contextual Risks and Uncertainties