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Comment: See WP:CFORK - is there a reason this content can't be incorporated into Recommender system? It looks short enough to be a section or paragraph. Sungodtemple (talk • contribs) 22:35, 17 March 2026 (UTC)
User inconsistency in recommender systems refers to the variability in user-provided feedback, such as ratings or preferences, when interacting with recommender systems. It is commonly described as a form of noise in user data and is considered a limitation in predicting user preferences.[1][2]
Studies have shown that users may give different evaluations for the same item at different points in time. This variation can arise from factors such as context, mood, or changes in preference, and introduces uncertainty into recommender system datasets. As a result, evaluation methods that rely on historical data may be affected by this instability.[3]
User inconsistency has implications for both the design and evaluation of recommender systems. In particular, it has been linked to limits on achievable prediction accuracy and has led to evaluation approaches that explicitly account for variability in user behavior.[4][5]
See also
editReferences
edit- ↑ Amatriain, Xavier; Pujol, Josep M.; Oliver, Nuria (2009). "I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems". User Modeling, Adaptation, and Personalization. Springer: 247–258.
- ↑ Herlocker, Jonathan L.; Konstan, Joseph A.; Terveen, Loren G.; Riedl, John T. (2004). "Evaluating Collaborative Filtering Recommender Systems". ACM Transactions on Information Systems. 22 (1): 5–53. doi:10.1145/963770.963772. ISSN 1046-8188.
- ↑ Said, Alan; Jain, Brijnesh J.; Narr, Sascha; Plumbaum, Till (2012). Users and Noise: The Magic Barrier of Recommender Systems. Proceedings of the 20th International Conference on User Modeling, Adaptation, and Personalization. Lecture Notes in Computer Science. Vol. 7379. Berlin, Heidelberg: Springer. pp. 237–248. doi:10.1007/978-3-642-31454-4_20.
- ↑ Jasberg, K.; Sizov, S. (2017). The Magic Barrier Revisited: Assessing Natural Limitations of Recommender Assessment. Proceedings of the 11th ACM Conference on Recommender Systems. ACM.
- ↑ Al Jurdi, W.; Bou Abdo, J.; Demerjian, J.; Makhoul, A. (2021). "Critique on Natural Noise in Recommender Systems". ACM Transactions on Knowledge Discovery from Data. 15 (5).