The ANU Computational Media lab has a long-lasting interest in understanding the popularity dynamics of online items. The current project examines the popularity of a set of items under the influence of recommendation algorithms.
We are interested in mathematically and empirically analysing the overall attention with self- and mutually- exciting feedback loops 1 and the evolution of attention dynamics when the recommender system is learning about user preferences and item characteristics at the same time, in an active learning or multi-armed bandit setting 2.
We are looking for up to two students to pursue these related but distinct directions.
- Solid knowledge of machine learning or time series models, e.g. COMP4670/8600 or equivalent.
- Comfortable prototyping machine learning algorithms or simulation, python or R.
- Strong ability to critically examine mathematical or empirical results.
- Able to communicate technical ideas clearly, and work effectively in a research team.
Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity, Marian-Andrei Rizoiu, Lexing Xie, Scott Sanner, Manuel Cebrian, Honglin Yu, and Pascal Van Hentenryck, World Wide Web 2017, International Conference on, 2017. See here for accessible introduction. ↩
Jiang, Ray, Silvia Chiappa, Tor Lattimore, András György, and Pushmeet Kohli. “Degenerate feedback loops in recommender systems.” In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 383-390. 2019. See here for accessible introduction. ↩