What are different ranking algorithms?
The main focus of ranking algorithms is about using machine learning methods, such as classification clustering and regression methods, to proceed the task of ranking, to improve the ranking precision, recall and optimize the precision values of models. Learning and ranking are two processes to construct learning to rank models.
I suggest you to go through this journal paper for an overview of different Rnaking Algos.
Traditional ML algorithms solve a prediction problem (classification or regression) on a single instance at a time. E.g. if you are doing spam detection on email, you will look at all the features associated with that email and classify it as spam or not. The aim of traditional ML is to come up with a class (spam or no-spam) or a single numerical score for that instance.
Ranking algorithms like LTR solves a ranking problem on a list of items. The aim of LTR is to come up with optimal ordering of those items. As such, LTR doesn’t care much about the exact score that each item gets, but cares more about the relative ordering among all the items. RankNet , LambdaRank and LambdaMART are all LTR algorithms developed by Chris Burges and his colleagues at Microsoft Research.
- RankNet — The cost function for RankNet aims to minimize the number of inversions in ranking. RankNet optimizes the cost function using Stochastic Gradient Descent.
- LambdaRank — Burgess et. al. found that during RankNet training procedure, you don’t need the costs, only need the gradients (λ) of the cost with respect to the model score. You can think of these gradients as little arrows attached to each document in the ranked list, indicating the direction we’d like those documents to move. Further they found that scaling the gradients by the change in NDCG found by swapping each pair of documents gave good results. The core idea of LambdaRank is to use this new cost function for training a RankNet. On experimental datasets, this shows both speed and accuracy improvements over the original RankNet.
- LambdaMart — LambdaMART combines LambdaRank and MART (Multiple Additive Regression Trees). While MART uses gradient boosted decision trees for prediction tasks, LambdaMART uses gradient boosted decision trees using a cost function derived from LambdaRank for solving a ranking task. On experimental datasets, LambdaMART has shown better results than LambdaRank and the original RankNet.