Users and Noise: The Magic Barrier of Recommender Systems


Recommender systems are crucial components of most commercial websites to keep users satisfied and to increase revenue. Thus, a lot of effort is made to improve recommendation accuracy. But when is the best possible performance of the recommender reached? The magic barrier, refers to some unknown level of prediction accuracy a recommender system can attain. The magic barrier reveals whether there is still room for improving prediction accuracy or indicates that further improvement is meaningless. In this work, we present a mathematical characterization of the magic barrier based on the assumption that user ratings are afflicted with inconsistencies - noise. In a case study with a commercial movie recommender, we investigate the inconsistencies of the user ratings and estimate the magic barrier in order to assess the actual quality of the recommender system.

   author = {Said, Alan and Jain, Brijnesh and Narr, Sascha and Plumbaum, Till},
   affiliation = {DAI Lab., Technische Universität Berlin, Germany},
   title = {Users and Noise: The Magic Barrier of Recommender Systems},
   booktitle = {User Modeling, Adaptation, and Personalization},
   series = {Lecture Notes in Computer Science},
   editor = {Masthoff, Judith and Mobasher, Bamshad and Desmarais, Michel and Nkambou, Roger},
   publisher = {Springer Berlin / Heidelberg},
   isbn = {978-3-642-31453-7},
   keyword = {Computer Science},
   pages = {237-248},
   volume = {7379},
   url = {},
   note = {10.1007/978-3-642-31454-4_20},
   year = {2012}
Alan Said, Brijnesh Jain, Sascha Narr, Till Plumbaum
Conference Paper
Proceedings of the 20th International Conference on User Modeling, Adaptation, and Personalization (UMAP '12)