11th Recommender systems Netherlands (RecSysNL) meetup


We look forward to welcoming you to the 11th RecSys Amsterdam meetup. Hosted by SPARQUE, Utrecht with two interesting talks from industry and academia.

Doors open at 18:00h, talks start at 18:30h.
Drinks and pizza will be provided.

1. Chi Shing Chang, SPARQUE, "Managing evolving and sprawling recommendations models"

What are good recommendations? Data, domain experts and users may not necessarily be in agreement. Recommendations may best be based on methods that integrate the important aspects of all three sources. Warmteservice, a wholesaler of installation materials with physical branches and a webshop, needed high quality recommendations for complementary products. By presenting these recommendations, customers would not forget to order essential materials while Warmteservice sells more items. SPARQUE developed a model for Warmteservice that combines aspects from existing data, from experts and user behavior. I’ll talk about how the recommendations evolved and the tooling we used to manage the model and to give Warmteservice a performant, interactive and personalized recommendation engine.

Chi’s Bio: Chi Shing Chang is an ICT-entrepreneur. Co-founder of SPARQUE; a technology scale-up specialized in personalized E-commerce. Chi Shing is a graduate of the IMD MBA program and the Industrial Engineering program from University of Twente. He worked for several employers: Deli Maatschappij, Shell, Orange and McKinsey, before taking the leap to start a company. A generalist and an omnivore, he takes interest in a broad range of topics, including business, economics and human and organizational psychology.

2. Martijn Willemsen, Eindhoven University of Technology (TU/e), "Recommender systems to help people move forward"

Many real-life recommender systems are evaluated mostly on (implicit) behavioral data such as clicks streams and viewing times. However, such an approach has limitations and I will show how a user-centric approach can help better understand why users are satisfied or not, for example why users prefer diversification over prediction accuracy as it reduces choice difficulty. The behaviorist approach to evaluation also misses that users’ short term goals (i.e. their current behavior) might not be representative of the goals they want to attain (i.e. their desired behavior). What we like today might not be what we like tomorrow, because our tastes evolve or because our prior preferences and past behavior might not represent our current needs and goals. This is especially relevant in health and sustainability domains where people are in need of support while changing their current behavior. I will elaborate on an example from the energy recommendation domain, and show how a different type of recommender approach and interface might help users to save more energy. I will also discuss our recent work using Spotify to help people explore new tastes for unknown genres.

Martijn's bio:
Dr. Martijn Willemsen is an expert on human decision making in interactive systems. He is working as an associate professor in the Human-Technology Interaction group of Eindhoven University of Technology (The Netherlands) and is the principle investigator of the recommender LAB at JADS Den Bosch (www.jads.nl). His primary interests lie in the understanding of cognitive processes of decision making by means of process tracing and in the application of decision making theory in interactive systems such as recommender systems. He is also an expert on user-centric evaluation of adaptive systems. He is part of the core team of the Customer Journey Research Program in the Data Science Center Eindhoven (DSC/e) and is teaching in the joint BSc and MSc data science programs of the Jheronimus Academy of Data Science (jads.nl). www.martijnwillemsen.nl