What is RankNet and its loss?
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Answer from AI
RankNet is a ranking learning algorithm based on neural networks, developed by Microsoft Research. It is used for Learning to Rank (LTR) tasks, for example, in search engines to order documents by relevance.
The main idea of RankNet is to train a model to predict the probability that one document is more relevant than another for a given query. This is done by taking pairs of documents and their relative preferences.
The loss function of RankNet is a cross-entropy function between the predicted probability and the true preference label:
[ L = -\bar{P}{ij} \log P{ij} - (1 - \bar{P}{ij}) \log (1 - P{ij}) ]
where:
- (P_{ij}) is the probability predicted by the model that document i is better than document j,
- (\bar{P}_{ij}) is the true label (1 if i is better than j, otherwise 0).
Thus, RankNet minimizes the error in pairwise preference prediction, which allows for effective training of the ranking model.