加速搜索结果多样化马尔可夫决策过程收敛速度的新方法
Novel Approaches to Accelerating the Convergence Rate of Markov Decision Process for Search Result Diversification
摘要：
最近，一些研究已经利用马尔可夫决策过程多样化（MDP-DIV）的搜索结果在信息检索。虽然MDP-DIV可以提供有前途的性能，但其收敛速度非常慢，这阻碍了其在实际应用中的可用性。在本文中，我们的目标是提高性能的MDP-DIV加快收敛速度，而不牺牲太多的精度。收敛速度慢的原因主要有两个：行动空间大和数据稀缺。一方面，每个位置的顺序决策都需要对所有候选集进行查询-文档相关性评估，这给MDP带来了巨大的搜索空间;另一方面，由于数据的稀缺性，Agent不得不与环境进行更多的“试错”交互。为了解决这个问题，我们提出了MDP-DIV-kNN和MDP-DIV-NTN方法。MDP-DIV-kNN方法采用$k$最近邻策略，丢弃最近选择的动作（文档）的$k$最近邻居，以减小多样化搜索空间。MDP-DIV-NTN采用预训练的多样化神经张量网络（NTN-DIV）作为评估模型，并将结果与MDP相结合，以产生最终的排名解决方案。实验结果表明，这两种方法确实加快了MDP-DIV的收敛速度，快了3倍，而产生的精度几乎没有下降，甚至更好。
英文摘要：
Recently, some studies have utilized the Markov Decision Process for diversifying (MDP-DIV) the search results in information retrieval. Though promising performances can be delivered, MDP-DIV suffers from a very slow convergence, which hinders its usability in real applications. In this paper, we aim to promote the performance of MDP-DIV by speeding up the convergence rate without much accuracy sacrifice. The slow convergence is incurred by two main reasons: the large action space and data scarcity. On the one hand, the sequential decision making at each position needs to evaluate the query-document relevance for all the candidate set, which results in a huge searching space for MDP; on the other hand, due to the data scarcity, the agent has to proceed more "trial and error" interactions with the environment. To tackle this problem, we propose MDP-DIV-kNN and MDP-DIV-NTN methods. The MDP-DIV-kNN method adopts a $k$ nearest neighbor strategy, i.e., discarding the $k$ nearest neighbors of the recently-selected action (document), to reduce the diversification searching space. The MDP-DIV-NTN employs a pre-trained diversification neural tensor network (NTN-DIV) as the evaluation model, and combines the results with MDP to produce the final ranking solution. The experiment results demonstrate that the two proposed methods indeed accelerate the convergence rate of the MDP-DIV, which is 3x faster, while the accuracies produced barely degrade, or even are better.
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