Seo, Eugene; Hutchinson, Rebecca A. 2018. Predicting Links in Plant-Pollinator Interaction Networks Using Latent Factor Models with Implicit Feedback. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) ; February 2–7, 2018; New Orleans: 808-815.
Plant-pollinator interaction networks are bipartite networks representing the mutualistic interactions between a set of plant species and a set of pollinator species. Data on these networks are collected by field biologists, who count visits from pollinators to flowers. Ecologists study the structure and function of these networks for scientific, conservation, and agricultural purposes. However, little research has been done to understand the underlying mechanisms that determine pairwise interactions or to predict new links from networks describing the species community. This paper explores the use of latent factor models to predict interactions that will occur in new contexts (e.g. a different distribution of the set of plant species) based on an observed network. The analysis draws on algorithms and evaluation strategies developed for recommendation systems and introduces them to this new domain. The matrix factorization methods compare favorably against several baselines on a pollination dataset collected in montane meadows over several years. Incorporating both positive and negative implicit feedback into the matrix factorization methods is particularly promising.