Modeling Interestingness with Deep Neural Networks

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2–13, October 25-29, 2014, Doha, Qatar. c⃝2014 Association for Computational Linguistics Modeling Interestingness with Deep Neural Networks Jianfeng Gao, Patrick Pantel, Michael Gamon, Xiaodong He, Li Deng Microsoft Research One Microsoft Way Redmond, WA 98052, USA {jfgao,ppantel,mgamon,xiaohe,deng}@microsoft.com Abstract 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 Introduction Tasks of predicting what interests a user based on the document she is reading are fundamental to many online recommendation systems. A recent survey is due to Ricci et al. (2011). In this paper, we exploit the use of a deep semantic model for two such interestingness tasks in which document semantics play a crucial role: automatic highlight- ing and contextual entity search. Automatic Highlighting. In this task we want a recommendation system to automatically dis- cover the entities (e.g., a person, location, organi- zation etc.) that interest a user when reading a doc- ument and to highlight the corresponding text spans, referred to as keywords afterwards. We show in this study that document semantics are among the most important factors that influence what is perceived as interesting to the user. For example, we observe in Web browsing logs that when a user reads an article about a movie, she is more likely to browse to an article about an actor or character than to another movie or the director. Contextual entity search. After identifying the keywords that represent the entities of interest to the user, we also want the system to recommend new, interesting documents by searching the Web for supplementary information about these enti- ties. The task is challenging because the same key- words often refer to different entities, and interest- ing supplementary information to the highlighted entity is highly sensitive to the semantic context. For example, “Paul Simon” can refer to many peo- ple, such as the singer and the senator. Consider an article about the music of Paul Simon …

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