Title: Towards Bursting Filter Bubble via Contextual Risks and Uncertainties,
Abstract: A rising topic in computational journalism is how to enhance the diversity in
news 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 from
opposing viewpoints and hurts long-term user experiences with lack of
serendipity. Since news providers can recommend neither opposite nor
diversified opinions if unpopularity of these articles is surely predicted,
they can only bet on the articles whose forecasts of click-through rate involve
high variability (risks) or high estimation errors (uncertainties). We propose
a novel Bayesian model of uncertainty-aware scoring and ranking for news
articles. The Bayesian binary classifier models probability of success (defined
as a news click) as a Beta-distributed random variable conditional on a vector
of the context (user features, article features, and other contextual
features). The posterior of the contextual coefficients can be computed
efficiently using a low-rank version of Laplace's method via thin Singular
Value Decomposition. Efficiencies in personalized targeting of exceptional
articles, which are chosen by each subscriber in test period, are evaluated on
real-world news datasets. The proposed estimator slightly outperformed existing
training and scoring algorithms, in terms of efficiency in identifying
successful outliers.