Jun Yu

TL;DR: The eBird project as mentioned in this paper has become a major source of biodiversity data, increasing our knowledge of the dynamics of species distributions, and having a direct impact on the conservation of birds and their habitats.

TL;DR: A method for indexing observer variability based on the data routinely submitted by observers participating in the citizen science project eBird, a broad-scale monitoring project in which observers collect and submit lists of the bird species observed while birding, finds that differences in species accumulation curves among observers equates to higher rates of species accumulation.

TL;DR: It is shown that modeling the expertise of birders can improve the accuracy of predicting observations of a bird species at a site and two other tasks: predicting birder expertise given their history of eBird checklists and identifying bird species that are difficult for novices to detect.

TL;DR: This work proposes a Latent Dirichlet Allocation (LDA) based diversified retrieval approach that selects diverse items based on the hidden user intents and shows that the LDA-based approach provides significantly higher user satisfaction than the eBay production ranker and three other diversification retrieval approaches.

TL;DR: In this paper, a neural architecture called GraFRank is proposed to learn expressive user representations from multiple feature modalities and user-user interactions, which can be used for friend recommendation on large-scale social platforms.