RankNet and LambdaRank

The ranking problem is about ordering a collection of documents according to their relevance to the given query.

Their are multiple approaches to the problem, but in pairwise approach, we simply care about predicting order of document pairs for the query. Given 2 documents $d_i$ and $d_j$ the true relative ordering is specified as $$h_{ij}=\{\begin{array}{ll}1& d_i>d_j\\ 0& d_i=d_j\\ -1& d_i<d_j\end{array}$$

In terms of modelling, we assume there is a base model takes in features $x_i$ corresponds to document $d_i$ and predict a score depicting relevance to the given query. $$s_i=f(x_i)$$

A comparator model is feed these scores which predicts $\mathrm{P}(d_i>d_j)$.

The comparator model can be a binary classifier by setting $$y_{ij}\triangleq \frac{1+h_{ij}}{2}$$ as target variable. The a comparator is trained to predict 1 for verifying the order and 0 for negating it.

RankNet

RankNet uses a logistic regression as comparator which is feed the difference of scores. $${\hat{y}}_{ij}=\mathrm{P}(d_i>d_j)=\frac{1}{1+e^{-\alpha (s_i-s_j)}}$$ $\alpha$ is a parameter which controls the slope of sigmoid function.

We can define binary cross entropy loss on this model as

$$C_{ij}=-(y_{ij}\log {\hat{y}}_{ij}+(1-y_{ij})\log (1-{\hat{y}}_{ij}))$$

Now consider a mini-batch of document $d_1,\dots ,d_n$ corresponding to a particular query. The documents have some perfect ordering to answer the query which is specified by values of $h_{ij}$.

For gradient update, we are interested in computing the gradients generated by each documents. $$\begin{array}{rl}\frac{\partial C}{\partial w}& =\frac{1}{n}\sum _{i=1}^n\frac{\partial C_i}{\partial w}\\ w& \leftarrow w-\eta \frac{\partial C}{\partial w}\end{array}$$

Now, if we assume the documents follow the order $d_b>d_i>d_a$, then the loss incurred by the $d_i$ can be written as

$$\begin{array}{rl}{C}_i& =-\sum _{a:d_i>d_a}{y}_{ia}\log {\hat{y}}_{ia}-\sum _{b:d_b>d_i}(1-y_{ib})\log (1-{\hat{y}}_{ib})\end{array}$$

Note that the ground truth labels $y_{ia}=1$ and $y_{ib}=0$. The gradient from $d_i$ can also be simplified as

$$\begin{array}{rl}\frac{\partial C_i}{\partial w}& =\sum _a\frac{\partial C_i}{\partial s_a}\frac{\partial s_a}{\partial w}+\sum _b\frac{\partial C_i}{\partial s_b}\frac{\partial s_b}{\partial w}\end{array}$$

The $\partial s_{◻}/\partial w$ part of the gradient only depends on the score prediction network. For computing the gradient of the comparator, we have $$\begin{array}{rl}\frac{\partial C_i}{\partial s_a}& =-\frac{\partial \log {\hat{y}}_{ia}}{\partial s_a}=\frac{-\alpha }{1+e^{\alpha (s_i-s_a)}}=-\alpha (1-{\hat{y}}_{ia})\\ \frac{\partial C_i}{\partial s_b}& =-\frac{\partial \log (1-{\hat{y}}_{ib})}{\partial s_b}=\frac{-\alpha }{1+e^{-\alpha (s_i-s_b)}}=-\alpha {\hat{y}}_{ib}\end{array}$$

If we randomly select a document pair $(d_i,d_j)$, the gradients would be

$$\begin{aligned} \frac{\partial C_i}{\partial s_j} &= \begin{cases} \alpha(\hat{y}_{ij}-1) &d_i>d_jorh_{ij}=1 \ -\alpha\hat{y}_{ij}&d_j>d_iorh_{ij}=-1\end{cases} \end{aligned}$$

Now if we define the quantities $${\lambda }_{ij}\triangleq \alpha [\frac{(1-h_{ij})}{2}-(1-{\hat{y}}_{ij})]$$ we can write the individual gradient as $$\begin{array}{rl}\frac{\partial C_i}{\partial w}& =\sum _a{\lambda }_{ia}\frac{\partial s_a}{\partial w}-\sum _b{\lambda }_{ib}\frac{\partial s_b}{\partial w}\\ \frac{\partial C}{\partial w}& =\frac{1}{n}\sum _{i=1}^n{\lambda }_i\frac{\partial s_i}{\partial w}\\ {\lambda }_i& =\sum _{d_i>d_j}{\lambda }_{ij}-\sum _{d_i<d_j}{\lambda }_{ij}\end{array}$$

So for each document in the batch, we can simply accumulate $\lambda$ and then apply it to the gradient thus not requiring $n^2$ gradient computation.

Each ${\lambda }_i$ can also be thought of as the strength of gradient, getting larger for every inversions in the the ordering and getting smaller for correct orderings.

LambdaRank

At this point explaining lambda rank is very simple. Its exactly same as RankNet, but we modify computation of $\lambda$’s as follows. $${\lambda }_{ij}\triangleq -\alpha (1-{\hat{y}}_{ij})|{\mathrm{\Delta }}_{NDCG}|$$

${\mathrm{\Delta }}_{NDCG}$ is the change in $NDCG$ measure if we swap $d_i$ and $d_j$ in the ordering. This results in gradient updates optimising for NDCG measure. Since in terms of NDCG, higher is better, we have to do gradient ascent instead of gradient descent $$w\leftarrow w+\eta \left(\frac{1}{n}\sum _{i=1}^n{\lambda }_i\frac{\partial s_i}{\partial w}\right)$$