We present a conversational recommendation system based on a Bayesian approach. A probability mass function over the items is updated after any interaction with the user, with information-theoretic criteria optimally shaping the interaction and deciding when the conversation should be terminated and the most probable item consequently recommended. Dedicated elicitation techniques for the prior probabilities of the parameters modeling the interactions are derived from basic structural judgements. Such prior information can be combined with historical data to discriminate items with different recommendation histories. A case study based on the application of this approach to \emph{stagend.com}, an online platform for booking entertainers, is finally discussed together with an empirical analysis showing the advantages in terms of recommendation quality and efficiency.

A humanoid robot named Yangyang is the latest to be revealed in China. Dressed in a full-length coat, the android can display a wide range of facial expressions and can speak, move its head, and raise its hands to greet people convincingly. And the machine bears an uncanny - yet apparently accidental - resemblance to former Governor of Alaska, Sarah Palin. Yangyang is one of a number of impressively realistic robots to be unveiled at the Global Mobile Internet Conference (GMIC) 2015 in Beijing, China. The android was produced jointly by China's Shanghai Yangyang Intelligent Robot Science Service Centre and renowned Japanese robotics professor Hiroshi Ishiguro, who has created some of the world's most eerily lifelike androids.