09:00-09:10 Opening/intro (Marijn)
09:10-09:50 (Keynote) Online food recommendations: A complex problem? Christoph Trattner (University of Bergen)
09:50-10:10 The Seven Layers of Complexity of Recommender Systems for Children. Emiliana Murgia, Monica Landoni, Theo Huibers, Jerry Fails and Maria Soledad Pera
10:10-10:30 Feature-Driven Interactive Recommendations and Explanations with Collaborative Filtering Approach. Sidra Naveed and Jürgen Ziegler
10:30-11:00 Coffee break
11:00-11:20 Multi-Criteria Knowledge-Based Recommender System for Decision Support in Complex Business Processes. Aleksandra Revina and Nina Rizun
11:20-11:40 Review-Based Cross-Domain Collaborative Filtering: A Neural Framework. Thanh-Nam Doan and Shaghayegh Sahebi
11:40-12:00 Meta-Learning for Scholarly-Article Recommendation. Andrew Collins and Joeran Beel
12:00-12:20 An Advice Recommender System Based on Complaint Data Analysis. Liang Yang, Daisuke Kitayama and Kazutoshi Sumiya
Title: Online food recommendations: A complex problem?
Abstract: The problem of recommending food to people has recently become an active field of research. While there is growing body of work investigating how online food recommender systems could potentially be designed to better meet the users’ preferences, to date less research has tried to understand the nature of online food choices and their complexity. How do people make their food choices online? To what extent can we model and predict this behavior, and can we actually change it through recommender technology?
Why might we want to change behavior? According to the World Health Organization around 80% of cases of heart disease, strokes and type 2 diabetes could be avoided if people would implement a healthier diet. Health-aware food recommender technologies have been touted as a valuable asset in achieving the ambitious goal of developing systems, which positively impact on the food choices people make. For example, they may help people to implement a healthier diet by suggesting healthier versions of a similar meal they typically like.
In this talk, I will present our latest research on the online food recommender problem. I will reveal the complex nature of online food choices and how this knowledge can be used to build novel food recommender systems. To conclude, I will present some preliminary work aiming to nudge people towards healthier food choices.
Speaker: Christoph Trattner is an Associate Professor at the University of Bergen in the Information Science & Media Studies Department. Previously, he was an Asst. Prof. at MODUL University Vienna in the New Media Technology Department. He also founded and led the Social Computing department at the Know-Center, Austria’s research competence for data-driven business and big data analytics. He holds a Ph.D. in Computer Science and Telematics from Graz University of Technology (Austria). Christoph’s research background includes Applied Machine Learning, Predictive Modeling, Recommender Systems, Social Networks Analysis, Human Computer Interaction and Data Science in particular. He is leading an international research effort that tries to understand, predict and change online food preferences to tackle health-related food issues such as diabetes or obesity. Since 2010, he published two books and over 90 scientific articles in top conferences and journals including, e.g., JASIST, UMUAI, TiiS, ComCom, EPJ Data Science, WWW, ICWSM. He holds several Best Paper/Poster Awards and Nominations, including, the Best Paper Award Honorable Mention in 2017 at the prestigious WWW conference series.
The proceedings of ComplexRec 2019 have been published on CEUR at http://ceur-ws.org/Vol-2449/.
- Emiliana Murgia, Monica Landoni, Theo Huibers, Jerry Fails and Maria Soledad Pera. The Seven Layers of Complexity of Recommender Systems for Children
- Aleksandra Revina and Nina Rizun. Multi-Criteria Knowledge-Based Recommender System for Decision Support in Complex Business Processes
- Thanh-Nam Doan and Shaghayegh Sahebi. Review-Based Cross-Domain Collaborative Filtering: A Neural Framework
- Andrew Collins and Joeran Beel. Meta-Learning for Scholarly-Article Recommendation
- Sidra Naveed and Jürgen Ziegler. Feature-Driven Interactive Recommendations and Explanations with Collaborative Filtering Approach
- Liang Yang, Daisuke Kitayama and Kazutoshi Sumiya. An Advice Recommender System Based on Complaint Data Analysis