论文标题
ListWise学习与深Q-Networks排名
Listwise Learning to Rank with Deep Q-Networks
论文作者
论文摘要
学习排名是根据文档与给定查询的相关性对文档进行排名的问题。深度Q学习已被证明是训练代理进行顺序决策的有用方法。在本文中，我们表明，我们的深度Q学习对代理的DeepQrank展示了可以被视为最先进的性能。尽管计算效率较小，而不是线性回归等监督学习方法，但我们的代理在可以用于培训和评估的数据形式方面的限制较少。我们针对Microsoft的LeTor listWise数据集运行算法，并在0.5075范围内实现NDCG@1（排名在[0,1]中的排名准确性），险些击败了领先的监督学习模型SVMRANK（0.4958）。
Learning to Rank is the problem involved with ranking a sequence of documents based on their relevance to a given query. Deep Q-Learning has been shown to be a useful method for training an agent in sequential decision making. In this paper, we show that DeepQRank, our deep q-learning to rank agent, demonstrates performance that can be considered state-of-the-art. Though less computationally efficient than a supervised learning approach such as linear regression, our agent has fewer limitations in terms of which format of data it can use for training and evaluation. We run our algorithm against Microsoft's LETOR listwise dataset and achieve an NDCG@1 (ranking accuracy in the range [0,1]) of 0.5075, narrowly beating out the leading supervised learning model, SVMRank (0.4958).
