The long tail of recommender systems and how to leverage it
By Yoon-Joo Park, Alexander Tuzhilin. 2008
490 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Fernando Ortega
- Antonio Hernando
- Jesús Bobadilla
- Zuohui Fu
- Hanxiong Chen
- Yue Shi
- Emilia G'omez
- Abraham Gutiérrez
In Information Systems
- Fernando Ortega
- Antonio Hernando
- Hanxiong Chen
- Yue Shi
- Abraham Gutiérrez
- Zuohui Fu
- Jesús Bobadilla
- Yingqiang Ge
In Signal Processing
- Jesús Bobadilla
- Yue Shi
- Fernando Ortega
- Emilia G'omez
- Antonio Hernando
- Jesus Bernal
- Martha A. Larson
- Carlos Castillo
Citations in the corpus
- How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, background): “Many eorts have been made to understand whether or not various recommender systems impact diversity by reinforcing the popularity of already-popular products or by increasing interactions with niche items [2, 11, 15, 20, 47, 63].”
- Collaborative Filtering beyond the User-Item Matrix (2014, background): “Another possibility is to first explicitly identify the tail items in a given collection and then generate recommendations intentionally biased to those tail items [Park and Tuzhilin 2008].”
- Recommender Systems (2016, unknown): “Methods that are specifically designed for the long tail in recommender algorithms are discussed in [173, 463, 648].”
- Exploring Artist Gender Bias in Music Recommendation (2020, background): “Highlighted in literature as a prominent issue for CF algorithms [1, 10, 34], Kowald et al.”
- A collaborative filtering approach to mitigate the new user cold start problem (2012, background): “The new item problem [38,39] arises due to the fact that the new items entered in RS do not usually have initial votes, and therefore, they are not likely to be recommended.”
- User-oriented Fairness in Recommendation (2021, background): “, the popular items can get more exposure than those unpopular ones in recommendations [33].”
- Recommender systems survey (2013, background): “The new item problem [174,172] arises because the new items entered in RS do not usually have initial ratings, and therefore, they are not likely to be recommended.”