Building public service recommenders: Logbook of a journey

RecSys 2021 Building public service recommenders: Logbook of a journey

Authors: Christina Boididou, BBC | Di Sheng, BBC | Felix J Mercer Moss, Datalab BBC | Alessandro Piscopo, BBC

Abstract: The BBC is one of the world's leading broadcasters, regularly producing large volumes of content including video, audio and text, spanning topics such as news, sport, education, and entertainment. In order to fulfil its public service remit, the BBC must cater to the full breadth of interests within its audiences, providing each member with the most relevant and engaging content for them.
Up until now, manual curation has been the principal approach used by the organisation to achieve this end. However, this strategy does not scale and is ineffective at tailoring personalised content experiences.
Modern recommender systems appear to represent relevant and appropriate solutions to the problem of scalable algorithmic curation. How can such systems be developed to in a public service organisation?
The core characteristics of public service can be difficult to define, but descriptions invariably include ideas such as independence, impartiality, diversity and inclusiveness.
To support the BBC in developing and deploying responsible recommendations at scale our team, Datalab, has taken an approach centred around the collaboration across teams and professional roles (product, editorial, and data), and around the concept of public service AI.
In this talk, we present this approach and reflect upon the lessons learnt in the years since the creation of our team.

DOI: https://doi.org/10.1145/3460231.3474614

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