Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning.
Claudio LuccheseFranco Maria NardiniRama Kumar PasumarthiSebastian BruchMichael BenderskyXuanhui WangHarrie OosterhuisRolf JagermanMaarten de RijkePublished in: SIGIR (2019)
Keyphrases
- learning to rank
- neural network
- gradient boosting
- loss function
- learning to rank algorithms
- ranking functions
- learning tasks
- learning problems
- concept learning
- supervised learning
- active learning
- reinforcement learning
- directly optimize
- feature vectors
- learning process
- pairwise
- bayesian networks
- information retrieval
- data sets