@inproceedings{cremonesi2010performance,
  abstract = {In many commercial systems, the 'best bet' recommendations are shown, but the predicted rating values are not. This is usually referred to as a top-N recommendation task, where the goal of the recommender system is to find a few specific items which are supposed to be most appealing to the user. Common methodologies based on error metrics (such as RMSE) are not a natural fit for evaluating the top-N recommendation task. Rather, top-N performance can be directly measured by alternative methodologies based on accuracy metrics (such as precision/recall). An extensive evaluation of several state-of-the art recommender algorithms suggests that algorithms optimized for minimizing RMSE do not necessarily perform as expected in terms of top-N recommendation task. Results show that improvements in RMSE often do not translate into accuracy improvements. In particular, a naive non-personalized algorithm can outperform some common recommendation approaches and almost match the accuracy of sophisticated algorithms. Another finding is that the very few top popular items can skew the top-N performance. The analysis points out that when evaluating a recommender algorithm on the top-N recommendation task, the test set should be chosen carefully in order to not bias accuracy metrics towards non-personalized solutions. Finally, we offer practitioners new variants of two collaborative filtering algorithms that, regardless of their RMSE, significantly outperform other recommender algorithms in pursuing the top-N recommendation task, with offering additional practical advantages. This comes at surprise given the simplicity of these two methods.},
  acmid = {1864721},
  address = {New York, NY, USA},
  author = {Cremonesi, Paolo and Koren, Yehuda and Turrin, Roberto},
  booktitle = {Proceedings of the Fourth ACM Conference on Recommender Systems},
  doi = {10.1145/1864708.1864721},
  interhash = {04cb3373b65b03e03225f447250e7873},
  intrahash = {aeab7f02942cfeb97ccc7ae0a1d60801},
  isbn = {978-1-60558-906-0},
  location = {Barcelona, Spain},
  numpages = {8},
  pages = {39--46},
  publisher = {ACM},
  series = {RecSys '10},
  title = {Performance of Recommender Algorithms on Top-n Recommendation Tasks},
  url = {http://doi.acm.org/10.1145/1864708.1864721},
  year = 2010
}

@inproceedings{koren2009collaborative,
  abstract = {Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset.},
  acmid = {1557072},
  address = {New York, NY, USA},
  author = {Koren, Yehuda},
  booktitle = {Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {10.1145/1557019.1557072},
  interhash = {ca14b78afaf26db8dd7eb13d7986830a},
  intrahash = {dad3f9050f58acf0551924e537e84e45},
  isbn = {978-1-60558-495-9},
  location = {Paris, France},
  numpages = {10},
  pages = {447--456},
  publisher = {ACM},
  title = {Collaborative filtering with temporal dynamics},
  url = {http://doi.acm.org/10.1145/1557019.1557072},
  year = 2009
}

@article{bell2007lessons,
  abstract = {This article outlines the overall strategy and summarizes a few key innovations of the team that won the first Netflix progress prize.},
  address = {New York, NY, USA},
  author = {Bell, Robert M. and Koren, Yehuda},
  doi = {10.1145/1345448.1345465},
  interhash = {e060fc1209b2dc19d58cecfc5563986b},
  intrahash = {16ae86f12fc8496399bfb3b6f3181113},
  issn = {1931-0145},
  journal = {SIGKDD Explorations Newsletter},
  month = dec,
  number = 2,
  pages = {75--79},
  publisher = {ACM},
  title = {Lessons from the Netflix prize challenge},
  url = {http://doi.acm.org/10.1145/1345448.1345465},
  volume = 9,
  year = 2007
}