Seminar Data-Driven Decision Making @ School of Business and Economics & Institute of Data Science
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Slides of my 15 minute presentation on adopting a more user-centric approach in data-driven personalization. Presented during the joint seminar organized by the data-driven decision making (D3M) research theme at School of Business and Economics and the Institute of Data Science (IDS) at Maastricht University.
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Seminar Data-Driven Decision Making @ School of Business and Economics & Institute of Data Science
- 1. Mark Graus Data-Driven Personalization: Adopting a User-Centric Approach Assistant Professor Data Science in Marketing Department of Marketing and Supply Chain Management, School of Business and Economics BISS Institute, Heerlen
- 2. Mark Graus Academics • BSc, MSc, PhD @ Eindhoven, University of Technology • Human-Technology Interaction Industry • Data Scientist Online Customer Decision Making • PhD in Collaboration with Philips
- 4. Personalization in Practice Predictive Modeling Problem Formulation Ru,i | Xu where Xu is usage history Content-Based Collaborative Filtering Evaluation Offline Cross-Validation Online A/B Testing Multi-Armed Bandits
- 5. User-Centric Evaluation Framework (Knijnenburg et al., 2012) 5
- 6. Problem Statement Can we show articles for new parents in order of relevance? Normal data-driven approaches are available Normal Complication: Cold-Start Additional complication: Possible mismatch between reading and actual interests Solution: Measure and include psychometrics in relevance predictions
- 8. Parenting Styles (Cognitive Part) • Combined 5 surveys into a new 70-item survey to measure parenting style. • Survey considers: Behavior, Cognition and Mother’s Perception of Child. • SEM Study With 200+ mothers
- 9. Study Design (Graus, Willemsen, Snijders, 2018) Initial Data Collection (N=181) • Parenting Styles • Reading Behavior Offline Relevance Predictions • Baseline • Reading Behavior • Parenting Styles • Both Revisit the (now personalized) system (N=121) • Reading Behavior • User Experience General Top-N BPRMF Segment-Based Top-N BPRMF + User Characteristics
- 10. Survey-Based Predictions were Least Accurate If we were to look at cross-validation, survey-based performs the worst, hybrid predictions perform (expectedly) the best.
- 11. Survey-Based Predictions Lead to Best User Experience We measured ‘Perceived level of Personalization’ and ‘Satisfaction’ Survey-based were the only predictions that scored significantly higher on both constructs.
- 12. Conclusions Psychometric Surveys can improve personalization. Performance Metrics and User Experience do not necessarily correlate.
- 13. Current Research Direction Conceptual Introduction, Personalization in Music, Personalization in Pension Communication
- 14. 1 A User interacting with a website results in Behavior and a User Experience. Based on Behavior we can deduce User Characteristics External Data can provide additional information on User Characteristics Based on predicted Characteristics, an Adaptation can be implemented. The Adaptation will influence Behavior and User Experience User Experience can be used to evaluate Characteristic Predictions and Adaptation Success 2 2 3 3 4 4 UX 1 1 UX 5 5 5 6 6 6 Personalization – User-Centric Approach
- 15. Personalization in the Music Domain (in collaboration with TU/e and JADS) Personalization in Music • Affective Playlists • Context-Aware Playlist • Group Playlists Role of Engagement • Different types of listeners have different needs, preferences.
- 16. Personalization in Pension Communication Increase Engagement with Pension through Personalized Communication
- 17. Different Types of Research Questions Fundamental What characteristics of our users are relevant to our goal? Methodological How to incorporate characteristics in Predictive Models? Inferring characteristics from interaction behaviour? Societal How does policy (GDPR) affect the possibilities and perception of personalization?
- 18. Interested? Call for Papers
- 19. Questions/remarks? Mark Graus School of Business and Economics, Department of Marketing and Supply Chain Management BISS Institute mp.graus@maastrichtuniversity.nl twitter.com/newmarrk medium.com/newmarrk linkedin.com/in/markgraus www.markgraus.net
- 20. References Graus, M. P., Willemsen, M. C., & Snijders, C. C. (2018, March). Personalizing an Online Parenting Library: Parenting-Style Surveys Outperform Behavioral Reading-Based Models. In CEUR Workshop Proceedings (Vol. 2068). Knijnenburg, B., Willemsen, M., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4–5), 441–504. https://doi.org/10.1007/s11257-011-9118-4
Editor's Notes
- We wanted to measure the effects on both behavior and user experience
- Who is a parent?
- Parents differ on many aspects, such as how warm they are, how strict they are, how worried they are. We developed a survey that when answered, tells us what type of parent a person is.
- Parents are hard to contact, so we used Facebook ads.
- We predicted relevance in four different ways. The reading was used similar to how ratings were used in matrix factorization. The survey data was done by median splits on the attunement and structure dimensions. This is where data science quite often stops. “We can better predict preferences using reading behavior and surveys.” But we are interested in user experience.
- This is already an interesting finding. Subjective experience and objective performance do often not correlate.