Learning to rank recommendations with the k-order statistic loss
By Jason Aaron Edward Weston, Hector Yee, Ron J. Weiss. 2013
75 citations recorded by the source corpus.
Subfields: Artificial Intelligence, 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 Artificial Intelligence
- Jason Aaron Edward Weston
- Arthur Szlam
- Mohit Bansal
- Kyunghyun Cho
- Antoine Bordes
- Yoshua Bengio
- Jianfeng Gao
- Alexander H. Miller
In Information Systems
- Sumit Chopra
- Antoine Bordes
- Harald Steck
- Jason Aaron Edward Weston
- Alexander H. Miller
- Stephen P. Boyd
- Corinne Horn
- Xiang Zhang
In Management Science and Operations Research
- Harald Steck
- Alexander H. Miller
- Jason Aaron Edward Weston
- Antoine Bordes
- Stephen P. Boyd
- Xiang Zhang
- Andreea Gane
- Arthur Szlam
Citations in the corpus
- Gaussian Ranking by Matrix Factorization (2015, methodology): “There has been a large body of work on training collaborative filtering approaches as to maximize various ranking metrics, see eg [5, 8, 17, 2, 18, 10, 20, 21, 14, 13, 12].”
- Generalized Low Rank Models (2014, background): “osses include the area under the curve loss [Ste07], ordered weighted average of pairwise classication losses [UBG09], the weighted approximate-rank pairwise loss [WBU10], the k-order statistic loss [WYW13], and the accuracy at the top loss [BCMR12]. 6.2 Osets and scaling Just as in the previous sec”
- Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems (2015, methodology): “However, it has been shown to be outperformed by other flavors of matrix factorization, in particular by using a ranking loss rather than squared loss (Weston et al., 2013) which we will compare to (cf. sec 3.2), as well as improvements like SVD++ (Koren, 2008).”