Research Summary for 2012-2013

My research interest lies in the intersection of text mining and machine learning, and aims to develop general and effective techniques to understand users' latent intentions from their generated content and behaviors. (Slides) (Poster)
  1. User Intent Modeling in Search Query Logs

    Mining search engine query logs to discover user intents is essential for optimizing the accuracy of any search engine [1,2,3,4,5].
    In [1], I proposed a unified framework for modeling the topical relevance and freshness, as well as their relative importance, based on the real news search click logs automatically. To accurate understand user's search behaviors, I proposed a semi-supervised clustering model to identify long-term search tasks (transcending session boundaries) in the search logs [3], where the structural dependency among the queries and a set of effective automatic annotation rules are introduced as weak supervision to release the burden of manual annotation. And I proposed a probabilistic click model to precisely interpret user's result ranking preferences from their biased clicks in [2]. In the proposed model, the document content and dependencies among the sequential click events within a query session are explicitly captured via a set of descriptive features, and such information is largely ignored in previous work.
  2. Sentiment Analysis in User Networks

    In [9], we studied an interesting new problem of automatically discovering opposing opinion networks of users in forum discussions. Signals from both textual content (e.g., who says what) and social interactions (e.g., who talks to whom) are explored to faciliate finding the group of users who are strongly against each other on some topic. In [7], an information network-based framework was proposed to infer hidden similarity and dissimilarity between users by exploring similar and opposite opinions, so as to improve post-level and user-level sentiment classication in the same time.


  1. Hongning Wang, Anlei Dong, Lihong Li, Yi Chang and Evgeniy Gabrilovich. Joint Relevance and Freshness Learning From Clickthroughs for News Search. The 2012 World Wide Web Conference (WWW'2012), p579-588. (PDF) (slides)
  2. Hongning Wang, ChengXiang Zhai, Anlei Dong and Yi Chang. Content-Aware Click Modeling. The 23rd International World-Wide Web Conference (WWW'2013) (To Appear)
  3. Hongning Wang, Yang Song, Ming-Wei Chang, Xiaodong He, Ryen White and Wei Chu. Learning to Extract Cross-Session Search Tasks. The 23rd International World-Wide Web Conference (WWW'2013) (To Appear)
  4. Yang Song, Hao Ma, Hongning Wang and Kuansan Wang. Exploring and Exploiting User Search Behaviors on Mobile and Tablet Devices to Improve Search Relevance. The 23rd International World-Wide Web Conference (WWW'2013) (To Appear)
  5. Ryen White, Wei Chu, Ahmed Hassan, Xiaodong He, Yang Song and Hongning Wang. Enhancing Personalized Search by Mining and Modeling Task Behavior. The 23rd International World-Wide Web Conference (WWW'2013) (To Appear)
  6. Chi Wang, Hongning Wang, Jialu Liu, Ming Ji, Lu Su, Yuguo Chen, Jiawei Han. On the Detectability of Node Grouping in Networks. SIAM International Conference on Data Mining (SDM'2013) (To Appear)
  7. Hongbo Deng, Jiawei Han, Hao Li, Heng Ji, Hongning Wang and Yue Lu. Exploring and Inferring User-User Pseudo-Friendship for Sentiment Analysis with Heterogeneous Networks. SIAM International Conference on Data Mining (SDM'2013) (To Appear)
  8. Mianwei Zhou, Hongning Wang and Kevin Chen-Chuan Chang. Learning to Rank from Distant Supervision: Exploiting Noisy Redundancy for Relational Entity Search. The 29th IEEE International Conference on Data Engineering (ICDE'2013) (To Appear)
  9. Yue Lu, Hongning Wang, ChengXiang Zhai and Dan Roth. Unsupervised Discovery of Opposing Opinion Networks From Forum Discussions. The 21st ACM International Conference on Information and Knowledge Management (CIKM'2012), p1642-1646. (PDF)