ADS Drinks and Data: Machine Learning & Recommendation Systems
Join us on Thursday, November 23rd at 16:00!
Recommender systems are an important class of machine learning algorithms that offer “relevant” suggestions to users. Categorized as either collaborative filtering or a content-based system, we will be talking about different projects and perspectives with Evangelos Kanoulas (IvI, UvA), Makoto Miyazaki (Dataiku) and Marthe Möller (DSC, UvA).
Location
Science Park 904, 1098 XH Amsterdam
Room: A1.28
Programme
15:55 Walk-in
16:00 Introduction & Welcome
16:05 Talk #1: Evangelos Kanoulas (IvI, UvA)
16:20 Discussion
16:25 Talk #2: Makoto Miyazaki (Dataiku)
16:40 Discussion
16:45 Talk #3: Marthe Möller (DSC, UvA)
17:00 Discussion
17:30 End and Networking!
Talk #1 by Evangelos Kanoulas
TBA
Talk #2 by Makoto Miyazaki
Ramen is a Japanese delicacy but has become pretty much an international thing. Here in the Netherlands as well, you must have your favorite ramen restaurant. What if I can suggest to you a ramen restaurant in Tokyo that best matches your taste? In this presentation I will introduce a recommendation system of the ramen restaurant based on the analysis of customer reviews using NLP technique.
Talk #3 by Marthe Möller
Amongst others, communication scholars study social media comments to learn more about computer-mediated communication. Whereas previous research has developed algorithms that automatically detect spam among social media comments, such algorithms are not always suitable to select comments that are relevant for the projects of communication scholars. Therefore, the present project investigates how supervised machine learning can be used to detect those social media comments that communication scholars can use to advance their theoretical understanding of the antecedents and consequences of digital media usage. In doing so, it discusses the various decisions that scholars need to make when using supervised machine learning and the consequences that these decisions have for the results generated by supervised machine learning models.