Ranking with non-random missing ratings: influence of popularity and positivity on evaluation metrics
By Bruno Pradel, Nicolas Usunier, Patrick Gallinari. 2012
66 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Information Systems, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- Tat-seng Chua
- Manik Varma
- Thorsten Joachims
- Himanshu Jain
- Harald Steck
- Ashudeep Singh
- Wenqiang Lei
- Yashoteja Prabhu
In Information Systems
- Xiangnan He
- Thorsten Joachims
- Tat-seng Chua
- Maarten de Rijke
- Adith Swaminathan
- Xiang Wang
- Tobias Schnabel
- Wenqiang Lei
In Management Science and Operations Research
- Thorsten Joachims
- Xiangnan He
- Adith Swaminathan
- Tobias Schnabel
- Maarten de Rijke
- Tat-seng Chua
- Manik Varma
- Himanshu Jain
Citations in the corpus
- Recommendations as Treatments: Debiasing Learning and Evaluation (2016, background): “However, this and similar estimators are generally biased for R ( ˆ Y ) (Pradel et al., 2012; Steck, 2013).”
- Evaluation of recommendations: rating-prediction and ranking (2013, background): “For instance, on the Netflix web site, a personalized rating value is predicted for any video in the collection, as to provide the user with an indication of enjoyment if s/he watches it.”
- Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications (2016, methodology): “Recommending rare items: It is important to remove the popularity bias and recommend rare/novel items in many applications [8, 11, 34, 38, 43, 46, 48, 50].”
- Advances and Challenges in Conversational Recommender Systems: A Survey (2021, background): “(2020) analyze the phenomenon of popularity bias (Steck, 2011; Pradel et al., 2012) and selection bias (Marlin et al.”