【摘要】 随着富媒体展现形式被越来越多地引入搜索交互界面,搜索引擎的结果页面呈异质化和二维模块展现形式,这对传统的点击预测模型提出了巨大的挑战。针对这一情况,我们对实际搜索引擎结果页面的多模态结果进行了分析,构建了一个结合深度神经网络和点击模型的框架,该框架既包含了神经网络的特性,又利用了点击模型的预测能力。我们希望利用这个框架挖掘出多模态信息与文本信息之间的相关性,使之具有描述异质化结果和二维模块展示形式的能力。实验表明,我们的框架相较于传统的点击模型在点击预测性能上有显著提升,但由于搜索引擎的多模态结果内容复杂,仅利用多模态结果的底层特征,即使使用深度神经网络,从中能够挖据出的语义相关性较弱。更多还原

【Abstract】 With the rich media introduced into searching interface,the result pages of the search engine appear to be heterogeneous and in a form of two-dimensional distribution.To deal with this new challenge to traditional click model,we analyze the result pages of a popular commercial search engine and build a click model based on deep neural network,trying to reveal correlations between multimedia information and text information.This framework contains both the characteristics of neural network and prediction ability of click model.The experiment demonstrates that our framework is well improved compared to original click model.However,due to the complexity of multimedia contents,even deep neural network would produce quite weak semantic correlations if we rely merely on basic characteristics of multimedia results.更多还原