The problem of relevance ranking consists of sorting a set of objects with respect to a given criterion. Since users may prefer different relevance criteria, the ranking algorithms should be adaptable to the user needs. Two main approaches exist in literature for the task of learning to rank: 1) a score function, learned by examples, which evaluates the properties of each object yielding an absolute relevance value that can be used to order the objects or 2) a pairwise approach, where a "preference function" is learned using pairs of objects to define which one has to be ranked first. In this paper, we present SortNet, an adaptive ranking algorithm which orders objects using a neural network as a comparator. The neural network training set provides examples of the desired ordering between pairs of items and it is constructed by an iterative procedure which, at each iteration, adds the most informative training examples. Moreover, the comparator adopts a connectionist architecture that is particularly suited for implementing a preference function. We also prove that such an architecture has the universal approximation property and can implement a wide class of functions. Finally, the proposed algorithm is evaluated on the LETOR dataset showing promising performances in comparison with other state of the art algorithms.

翻译：摘要：相关性排序问题旨在根据特定标准对一组对象进行排序。由于不同用户可能偏好不同的相关性标准，排序算法需具备适应不同用户需求的能力。现有文献中主要有两种学习排序方法：1）基于样本学习评分函数，通过评估各对象的属性得到绝对相关性值，并以此对对象排序；2）成对方法，利用对象对学习"偏好函数"，以确定对象间的优先顺序。本文提出SortNet算法，这是一种自适应排序算法，采用神经网络作为比较器对对象进行排序。神经网络训练集提供对象间期望排序的成对样本，并通过迭代过程构建，该过程在每次迭代中增加最具信息量的训练样本。此外，比较器采用特别适用于实现偏好函数的连接主义架构。我们证明该架构具有通用逼近性质，能够实现广泛的函数类。最后，在LETOR数据集上的评估表明，该算法相比其他前沿算法展现出具有竞争力的性能。