The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A user-centric evaluation framework for recommender systems
Open accessAbstract
This research was motivated by our interest in understanding the criteria for measuring the success of a recommender system from users' point view. Even though existing work has suggested a wide range of criteria, the consistency and validity of the combined criteria have not been tested. In this paper, we describe a unifying evaluation framework, called ResQue (Recommender systems' Quality of user experience), which aimed at measuring the qualities of the recommended items, the system's usability, usefulness, interface and interaction qualities, users' satisfaction with the systems, and the influence of these qualities on users' behavioral intentions, including their intention to purchase the products recommended to them and return to the system. We also show the results of applying psychometric methods to validate the combined criteria using data collected from a large user survey. The outcomes of the validation are able to 1) support the consistency, validity and reliability of the selected criteria; and 2) explain the quality of user experience and the key determinants motivating users to adopt the recommender technology. The final model consists of thirty two questions and fifteen constructs, defining the essential qualities of an effective and satisfying recommender system, as well as providing practitioners and scholars with a cost-effective way to evaluate the success of a recommender system and identify important areas in which to invest development resources.
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Cites
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Self-citationOzok, A. A., Fan, Q., & Norcio, A. F. (2010) · Behaviour & Information Technology · doi:10.1080/01449290903004012 · Cited by 60 · cited 4×
“[16] explored the usability and user preferences of recommender interfaces in a large-scale user study involving college students.”
BackgroundInfluential
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Exploring and Promoting Diagnostic Transparency and Explainability in Online Symptom Checkers
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Bruckert, S., Finzel, B., & Schmid, U. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.507973 · Cited by 66
Carrasco-Hernandez, L., Jódar-Sánchez, F., Núñez-Benjumea, F., Moreno Conde, J., Mesa González, M., Civit-Balcells, A., Hors-Fraile, S., Parra-Calderón, C. L., Bamidis, P. D., & Ortega-Ruiz, F. (2020) · JMIR mHealth and uHealth · doi:10.2196/17530 · Cited by 65
Interactive Intent Modeling for Exploratory Search
Ruotsalo, T., Peltonen, J., Eugster, M. J. A., Głowacka, D., Floréen, P., Myllymäki, P., Jacucci, G., & Kaski, S. (2018) · ACM Transactions on Information Systems · doi:10.1145/3231593 · Cited by 57
Swarm Intelligence Techniques in Recommender Systems-a Review of Recent Research
Peška, L., Tashu, T. M., & Horváth, T. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.04.003 · Cited by 54
What's in a User? Towards Personalising Transparency for Music Recommender Interfaces
Millecamp, M., Htun, N. N., Conati, C., & Verbert, K. (2020) · Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization · doi:10.1145/3340631.3394844 · Cited by 50
Testing the performance of online recommendation agents: A meta-analysis
Blut, M., Ghiassaleh, A., & Wang, C. (2023) · Journal of Retailing · doi:10.1016/j.jretai.2023.08.001 · Cited by 31
Carrera-Rivera, A., Larrinaga, F., Lasa, G., Martinez-Arellano, G., & Unamuno, G. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09414-0 · Cited by 30
Be Friendly, Not Friends: How LLM Sycophancy Shapes User Trust
Sun, Y., & Wang, T. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791079 · Cited by 16
“After each response, briefly self-validate that both sides are represented; if not, adjust the next reply accordingly.”
HABOS: Towards a platform of haptic-audio based online shopping for the visually impaired
Wong, E. J., Yap, K. M., Alexander, J., & Karnik, A. (2015) · 2015 IEEE Conference on Open Systems (ICOS) · doi:10.1109/icos.2015.7377279 · Cited by 10
Metadata
- Authors
- Pu, P., Chen, L., & Hu, R.
- Venue
- Proceedings of the fifth ACM conference on Recommender systems
- Publisher
- ACM
- Published
- 2011-10-23
- Pages
- 157-164
- OpenAlex
- W2162111811
- Cited by
- 850
- References
- 6
- FWCI
- 26.68
- Type
- conference paper
- Language
- en
- Open access
- green
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