Influence Maximization with Fairness at Scale (Extended Version)

no code implementations • 2 Jun 2023 • Yuting Feng, Ankitkumar Patel, Bogdan Cautis, Hossein Vahabi

In this paper, we revisit the problem of influence maximization with fairness, which aims to select k influential nodes to maximise the spread of information in a network, while ensuring that selected sensitive user attributes are fairly affected, i. e., are proportionally similar between the original network and the affected users.

Decision Making Fairness

IGNiteR: News Recommendation in Microblogging Applications (Extended Version)

no code implementations • 4 Oct 2022 • Yuting Feng, Bogdan Cautis

Similarly, a time-sensitive user encoder enables us to capture the dynamic preferences of users with an attention-based bidirectional LSTM.

News Recommendation Recommendation Systems