In this talk, I will present our recent paper at SIGIR 2018: "Structuring Wikipedia Articles with Section Recommendations". Sections are the building blocks of Wikipedia articles. They enhance readability and can be used as a structured entry point for creating and expanding articles. Structuring a new or already existing Wikipedia article with sections is a hard task for humans, especially for newcomers or less experienced editors, as it requires significant knowledge about how a well- written article looks for each possible topic. Inspired by this need, our work defines the problem of section recommendation for Wikipedia articles and proposes several approaches for tackling it. Our systems can help editors by recommending what sections to add to already existing or newly created Wikipedia articles. Our basic paradigm is to generate recommendations by sourcing sections from articles that are similar to the input article. We explore several ways of defining similarity for this purpose (based on topic modeling, collaborative filtering, and Wikipedia's category system). We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category-based approach works best, achieving precision@10 of about 80% in the human evaluation.
Michele Catasta is a Postdoctoral Research Fellow at Stanford University, advised by Prof. Jure Leskovec. His research agenda covers different areas of Data Science, with a recent focus on developing Recommender Systems based on large datasets (e.g., Wikipedia, GitHub, etc.) He graduated at EPFL with a Ph.D. in Computer Science, and worked also for MIT Media Lab, Google and Yahoo Labs. Before his academic journey, Michele was in the founding team of Sindice.com (the largest Semantic Web search engine) which later evolved into an investigative intelligence platform (now Siren.io) In the past years, he received several awards and recognition - among them, focused grants from Amazon and Samsung Research USA.