Stacking recommendation engines with additional meta-features

By Xinlong Bao, Lawrence D. Bergman, Rich Thompson. 2009

58 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

1. Su-In Lee
2. Scott M. Lundberg
3. Hugh Chen
4. Yue Shi
5. Martha A. Larson
6. Alan Hanjalic
7. Przemyslaw Biecek
8. Adrian Weller

In Information Systems

1. Yue Shi
2. Martha A. Larson
3. Alan Hanjalic
4. Su-In Lee
5. Scott M. Lundberg
6. Harald Steck
7. Marius Kaminskas
8. Derek G. Bridge

In Management Science and Operations Research

1. Yue Shi
2. Martha A. Larson
3. Alan Hanjalic
4. Su-In Lee
5. Scott M. Lundberg
6. Harald Steck
7. Marius Kaminskas
8. Derek G. Bridge

Citations in the corpus

• Explaining Models by Propagating Shapley Values of Local Components (2019, background): “Stacks, and more generally ensembles, of models are increasingly popular for performant predictions (Bao, Bergman, and Thompson 2009; G¨unes¸, Wolfinger, and Tan 2017; Zhai and Chen 2018).”
• Collaborative Filtering beyond the User-Item Matrix (2014, background): “One of the most common side information sources is attribute information [Agarwal and Chen 2009; Bao et al. 2009; Gantner et al. 2010a; Koenigstein et al. 2011; Li et al. 2010; Moshfeghi et al. 2011; Shi et al. 2010a].”
• Recommender Systems (2016, unknown): “These methods can be enhanced further with different types of meta-level content features [65, 66, 554].”