Modeling Interestingness with Deep Neural Networks

This paper presents a deep semantic simi-larity model (DSSM), a special type of deep neural networks designed for text analysis, for recommending target docu-ments to be of interest to a user based on a source document that she is reading. We observe, identify, and detect naturally oc-curring signals of interestingness in click transitions on the Web between source and target documents, which we collect from commercial Web browser logs. The DSSM is trained on millions of Web transitions, and maps source-target document pairs to feature vectors in a latent space in such a way that the distance between source doc-uments and their corresponding interesting targets in that space is minimized. The ef-fectiveness of the DSSM is demonstrated using two interestingness tasks: automatic highlighting and contextual entity search. The results on large-scale, real-world da-tasets show that the semantics of docu-ments are important for modeling interest-ingness and that the DSSM leads to signif-icant quality improvement on both tasks, outperforming not only the classic docu-ment models that do not use semantics but also state-of-the-art topic models. 1