Robin Burke

Recommender systems curate users’ information environments on social media platforms based on the platforms’ objectives, yet user and community input into these objectives remains limited. With the rise of federated protocols such as the AT Protocol and ActivityPub, researchers have begun investigating ways for users to meaningfully influence and design their recommendations. However, these approaches often treat recommender systems as systems designed for a single user and neglect possible community objectives. We present CORGI, an open-source AT Protocol feed generator that makes a social-feed ranking policy inspectable and governable by the community of users it jointly serves. This approach treats the recommender system as a community resource shaped by collective input. Our demo incorporates a set of 24 synthetic users to demonstrate the voting mechanism. The demonstration makes the full governance loop observable: a reviewer proposes a policy, synthetic voters respond, their ballots are aggregated, the fixed post set is reranked, and each post’s score can be inspected.

One of the goals of recommender systems research is to provide insights and methods that can be used by practitioners to build real-world systems that deliver high-quality recommendations to actual people grounded in their genuine interests and needs. We report on our experience trying to apply the news recommendation literature to build POPROX, a live platform for news recommendation research, and reflect on the extent to which the current state of research supports system-building efforts. Our experience highlights several unexpected challenges encountered in building personalization features that are commonly found in products from news aggregators and publishers, and shows how those difficulties are connected to surprising gaps in the literature. Finally, we offer a set of lessons learned from building a live system with a persistent user base and highlight opportunities to make future news recommendation research more applicable and impactful in practice.

Envy-freeness and the relaxation to Envy-freeness up to one item (EF-1) have been used as fairness concepts in the economics, game theory, and social choice literatures since the 1960s, and have recently gained popularity within the recommendation systems communities. In this short position paper we will give an overview of envy-freeness and its use in economics and recommendation systems; and illustrate why envy is not appropriate to measure fairness for use in settings where personalization plays a role.

Recommender systems are usually designed by engineers, researchers, designers, and other members of development teams. These systems are then evaluated based on goals set by the aforementioned teams and other business units of the platforms operating the recommender systems. This design approach emphasizes the designers’ vision for how the system can best serve the interests of users, providers, businesses, and other stakeholders. Although designers may be well-informed about user needs through user experience and market research, they are still the arbiters of the system’s design and evaluation, with other stakeholders’ interests less emphasized in user-centered design and evaluation. When extended to recommender systems for social good, this approach results in systems that reflect the social objectives as envisioned by the designers and evaluated as the designers understand them. Instead, social goals and operationalizations should be developed through participatory and democratic processes that are accountable to their stakeholders. We argue that recommender systems aimed at improving social good should be designed by and with , not just for , the people who will experience their benefits and harms. That is, they should be designed in collaboration with their users, creators, and other stakeholders as full co-designers, not only as user study participants.