A case study of behavior-driven conjoint analysis on Yahoo!: front page today module

By Wei Chu, Seung-Taek Park, Todd Beaupre, Nitin Motgi, A. Phadke, S. Chakraborty, J. Zachariah. 2009

83 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A contextual-bandit approach to personalized news article recommendation (2010, methodology): “Here, we intentionally used five users (and articles) groups , which has been shown to be representative in segmentation an alysis [13].”
An unbiased offline evaluation of contextual bandit algorithms with generalized linear models (2011, background): “Common approaches are to construct a lower dimensional subspace of features and then work with the new feature representations; see Chu et al. (2009) for an example.”
Decision Trees for Decision-Making under the Predict-then-Optimize Framework (2020, unknown): “The user and article features were constructed using a conjoint analysis with a bilinear model; see Chu et al. (2009) for more details.”
News recommender systems - Survey and roads ahead (2018, methodology): “For each visit, the timestamp and the displayed article ID is available, as well as a six-dimensional feature vector that was extracted from the item-user interactions via a specific form of regression analysis [32].”
Explore-exploit in top-N recommender systems via Gaussian processes (2014, unknown): “One can ﬁnd detailed information on the dataset, the data collection methodology and an explanation of the unbiased ofﬂine evaluation in [1, 18, 5].”