  The last decade has witnessed a tremendous growth in the volume as well as
the diversity of multimedia content generated by a multitude of sources (news
agencies, social media, etc.). Faced with a variety of content choices,
consumers are exhibiting diverse preferences for content; their preferences
often depend on the context in which they consume content as well as various
exogenous events. To satisfy the consumers' demand for such diverse content,
multimedia content aggregators (CAs) have emerged which gather content from
numerous multimedia sources. A key challenge for such systems is to accurately
predict what type of content each of its consumers prefers in a certain
context, and adapt these predictions to the evolving consumers' preferences,
contexts and content characteristics. We propose a novel, distributed, online
multimedia content aggregation framework, which gathers content generated by
multiple heterogeneous producers to fulfill its consumers' demand for content.
Since both the multimedia content characteristics and the consumers'
preferences and contexts are unknown, the optimal content aggregation strategy
is unknown a priori. Our proposed content aggregation algorithm is able to
learn online what content to gather and how to match content and users by
exploiting similarities between consumer types. We prove bounds for our
proposed learning algorithms that guarantee both the accuracy of the
predictions as well as the learning speed. Importantly, our algorithms operate
efficiently even when feedback from consumers is missing or content and
preferences evolve over time. Illustrative results highlight the merits of the
proposed content aggregation system in a variety of settings.
