Project Overview#

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.

Requirements#

  • 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.

Info#

  1. 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

  2. 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

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