The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Evaluating recommender systems from the user’s perspective: survey of the state of the art
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Citations
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
Building a practically useful theory of goal setting and task motivation: A 35-year odyssey.
Locke, E. A., & Latham, G. P. (2002) · American Psychologist · doi:10.1037/0003-066x.57.9.705 · Cited by 7,766 · cited 2×
“2004) investigated whether goal-setting theories (Locke and Latham 2002) could boost members’ contributions to a community or not.”
BackgroundTrust building with explanation interfaces
Pu, P., & Chen, L. (2006) · Proceedings of the 11th international conference on Intelligent user interfaces · doi:10.1145/1111449.1111475 · Cited by 300
Self-citationUnderstanding choice overload in recommender systems
Bollen, D., Knijnenburg, B. P., Willemsen, M. C., & Graus, M. (2010) · Proceedings of the fourth ACM conference on Recommender systems · doi:10.1145/1864708.1864724 · Cited by 232
Layered evaluation of interactive adaptive systems: framework and formative methods
Paramythis, A., Weibelzahl, S., & Masthoff, J. (2010) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-010-9082-4 · Cited by 145 · cited 1×
“This method is called layered evaluation in Paramythis et al. (2010).”
Method
Cited by
Bringing Transparency Design into Practice
Eiband, M., Schneider, H., Bilandzic, M., Fazekas-Con, J., Haug, M., & Hussmann, H. (2018) · 23rd International Conference on Intelligent User Interfaces · doi:10.1145/3172944.3172961 · Cited by 250
Beyond user experience: What constitutes algorithmic experiences?
Shin, D., Zhong, B., & Biocca, F. A. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.102061 · Cited by 174
Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems
Kunkel, J., Donkers, T., Michael, L., Barbu, C.-M., & Ziegler, J. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300717 · Cited by 156
Factors influencing willingness to provide personal information for personalized recommendations
Kim, M. S., & Kim, S. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.06.031 · Cited by 78
Wang, Y.-Y., Luse, A., Townsend, A. M., & Mennecke, B. E. (2015) · Information Systems and e-Business Management · doi:10.1007/s10257-014-0269-9 · Cited by 54
Metadata
- Authors
- Pu, P., Chen, L., & Hu, R.
- Venue
- User Modeling and User-Adapted Interaction
- Published
- 2012-03-10
- Volume
- 22
- Issue
- 4-5
- Pages
- 317-355
- ISSN
- 0924-1868 (ISSN-L)
- 1573-1391
- OpenAlex
- W2050113838
- Cited by
- 377
- References
- 5
- FWCI
- 67.27
- Type
- journal article
- Language
- en
- Open access
- green
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- Pu, P., Chen, L., & Hu, R. (2012). Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Modeling and User-Adapted Interaction. https://doi.org/10.1007/s11257-011-9115-7
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
