Introduction
The recommender LAB of Martijn Willemsen researches how decisions can be supported by recommender systems, and includes domains such as movies, music, health related decisions and energy-saving measures. The LAB focuses on how insights from decision psychology can improve recommender algorithms, how to best evaluate recommender systems and on novel recommendation methods that help users with developing preferences and goals.
Recommender LAB at JADS
The recommender LAB at JADS is led by Martijn Willemsen, associate professor at TU/e and JADS. The LAB approaches recommender systems from an interdisciplinary perspective, with a strong focus on the application of decision psychology on algorithmic development and on user evaluation.
The recommender LAB host researchers at TU/e and JADS working on different applications of recommender systems in both classical entertainment domains such as movie and music recommendations, as wel as in domains related to behavioral change such as energy conservation and e-coaching and life-style recommendations. Check for details the different topics on the right.
User-centric Evaluation Framework
The recommender LAB recognized that proper evaluation of recommender systems goes beyond offline testing of algorithmic accuracy or behavioral AB tests. The research in our lab typically employes the user-centric evaluation framework developed by Bart Knijnenburg and Martijn Willemsen, in which subjective system aspects (user perceptions) and evaluations (e.g. choice and system satisfaction) are related to objective behavioral measures of user interactions with the system when specific versions of a system are tested against eachother. This approach allows for a more thorough understanding of why and how a particular recommendation approach works (or not).
The User-centric Evaluation Framework by Knijnenburg and Willemsen (2015)
Relevant research papers:
- Knijnenburg, B. P., & Willemsen, M. C. (2015). Evaluating Recommender Systems with User Experiments. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 309–352). Springer US.link to springer
- Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., Newell, C. (2012). Explaining the User Experience of Recommender Systems. User Modeling and User-Adapted Interaction (UMUAI), vol 22, p. 441-504, open access
Music Genre Exploration
In recent years, Ph.D. student Yu Liang and I have worked on building a genre exploration tool that allows you to explore a new music genre in a personalized way. In the tool you select a new genre to explore, and then you are provided with recommendations from that new genre that fit with your current music taste. The tool visualizes how the recommendations relate to your own taste and to the genre you are exploring, and also allows you to balance personalization and representativeness for thew genre with an interactive slider tool.
You can try out the genre exploration tool here (with your own spotify profile): https://music-genre-explore.herokuapp.com/
Relevant research papers:
- Liang, Y., & Willemsen, M. C. (2023). Promoting Music Exploration through Personalized Nudging in a Genre Exploration Recommender. International Journal of Human-Computer Interaction, 39(7), 1495-1518. https://doi.org/10.1080/10447318.2022.2108060
- Liang, Y., & Willemsen, M. C. (2022). Exploring the Longitudinal Effects of Nudging on Users’ Music Genre Exploration Behavior and Listening Preferences. In RecSys 2022 – Proceedings of the 16th ACM Conference on Recommender Systems (pp. 3–13). Association for Computing Machinery, Inc. https://doi.org/10.1145/3523227.3546772
