Presentation on theme: "1.Accuracy of Agree/Disagree relation classification. 2.Accuracy of user opinion prediction. 1.Task extraction performance on Bing web search log with."— Presentation transcript:
1.Accuracy of Agree/Disagree relation classification. 2.Accuracy of user opinion prediction. 1.Task extraction performance on Bing web search log with increasing volume of weak supervision. 2.Identified latent search task structure. 1.Model update trace in training process. 2.Ranking performance comparison with baselines on Yahoo! news search log. Joint Relevance and Freshness Learning (WWW’ 2012) In contrast to traditional Web search, where topical relevance is often the main ranking criterion, news search is characterized by the increased importance of freshness. However, the estimation of relevance and freshness, and especially the relative importance of these two aspects, are highly specific to the query and the time when the query was issued. In this work, we proposed a unified framework for modeling the topical relevance and freshness, as well as their relative importance, based on click logs. We explored click statistics and content analysis techniques to define a set of temporal features, which predict the right mix of freshness and relevance for a given query. Content-Aware Click Modeling (WWW’2013) Cross-Session Search Task Extraction (WWW’2013) Experimental Results Unsupervised Discovery of Opposing Opinion Networks (CIKM’2012) Computational User Intent Modeling Hongning Wang (email@example.com)Advisor: ChengXiang Zhai (firstname.lastname@example.org) Department of Computer Science, University of Illinois at Urbana-Champaign Urbana IL, 61801 USA Relevance Topical relatedness Metric: tf*idf, BM25, Language Model Freshness Temporal closeness Metric: age, elapsed time Trade-off Query specific To meet user’s information need Relevance v.s. Freshness Joint Relevance and Freshness Learning Query => trade-off URL => freshness Click => overall impression Experimental Results Modeling User Clicks Match my query? Redundant doc? Shall I move on? Relevance quality of a document: e.g., ranking features Chance to further examine the results: e.g., position, # clicks, distance to last click Chance to click on an examined and relevant document: e.g., clicked/skipped content similarity Experimental Results URL => relevance Key: Freshness v.s. Relevance In this work, we proposed a general Bayesian Sequential State (BSS) model for addressing two deficiencies of existing click modeling approaches, namely failing to utilize document content information for modeling clicks and not being optimized for distinguishing the relative order of relevance among the candidate documents. As our solution, a set of descriptive features and ranking-oriented pairwise preference are encoded via a probabilistic graphical model, where the dependency relations among a document's relevance quality, examine and click events under a given query are automatically captured from the data. 1.P@1 comparison between different click models over the random bucket click set and normal click set from Yahoo! news search log. 2.Feature weights learned by BSS model. (a) On normal bucket clicks (b) On random bucket clicks Search tasks frequently span multiple sessions, and thus developing methods to extract these tasks from historic data is central to understanding longitudinal search behaviors and in developing search systems to support users' long-running tasks. In this work, we developed a semi-supervised clustering model based on the latent structural SVM framework, which is capable of learning inter-query dependencies from users' searching behaviors. A set of effective automatic annotation rules are proposed as weak supervision to release the burden of manual annotation. Our method paves the way for user modeling and long-term task based personalized applications. Semi-supervised Structural Learning t ѱ = 30 minutes An impression An atomic information need that may result in one or more queries 5/29/2012 S1 5/29/2012 5:26bank of america 5/29/2012 S2 5/29/2012 11:11macy's sale 5/29/2012 11:12sas shoes 5/30/2012 S1 5/30/2012 10:19credit union 5/30/2012 S2 5/30/2012 12:256pm.com 5/30/2012 12:49coupon for 6pm shoes Heuristic constraints Identical queries Sub-queries Identical clicked URLs Structural knowledge Same task => tasks sharing related queries Latent With more and more people freely express opinions as well as actively interact with each other in discussion threads, online forums are becoming a gold mine with rich information about people’s opinions and social behaviors. In this work, we study an interesting new problem of automatically discovering opposing opinion networks of users from forum discussions, which are subset of users who are strongly against each other on some topic. Signals from both textual content (e.g., who says what) and social interactions (e.g., who talks to whom) are explored in an unsupervised optimization framework. Identifying Opposing Opinion Networks It’s human right! Budget increase It is nonsense! I insist my point. I agree with you! … Reply To … Thread, e.g. “health care reform” Thread, e.g. “health care reform” Hot Topics & Current Events forum in Military.com: 43,483 threads 1,343,427 posts 34,332 users 7.7 reply-to relation/ thread Post User Different Opinion Similar Opinion Supporting Group Against Group Sentiment prior Sentiment prior Opinions Agree Opinions Disagree Opinions Disagree subject to Text 1v 1 Text 2v 2 Text 3v 3… Text 1v 1 Text 2v 2 Text 3v 3… Opinion of posts Experimental Results Signal 1 : ReplyTo Text (R: agree/disagree) Signal 3 : Topical Similarity (T: agree/disagree) Signal 2 : Author Consistency (A)
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