We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classi...
Versioned textual collections are collections that retain multiple versions of a document as it evolves over time. Important large-scale examples are Wikipedia and the web collect...
Query performance prediction is aimed at predicting the retrieval effectiveness that a query will achieve with respect to a particular ranking model. In this paper, we study quer...
As the Internet grows explosively, search engines play a more and more important role for users in effectively accessing online information. Recently, it has been recognized that ...
Ming Ji, Jun Yan, Siyu Gu, Jiawei Han, Xiaofei He,...
Online forum discussions are emerging as valuable information repository, where knowledge is accumulated by the interaction among users, leading to multiple threads with structure...
Hongning Wang, Chi Wang, ChengXiang Zhai, Jiawei H...
In this paper, we investigate a search-based face annotation framework by mining weakly labeled facial images that are freely available on the internet. A key component of such a ...
The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So ...
Steffen Rendle, Zeno Gantner, Christoph Freudentha...
We study a novel problem of social context summarization for Web documents. Traditional summarization research has focused on extracting informative sentences from standard docume...
Zi Yang, Keke Cai, Jie Tang, Li Zhang, Zhong Su, J...
Web search behaviour studies, including eye-tracking studies of search result examination, have resulted in numerous insights to improve search result quality and presentation. Ye...
Large search engines process thousands of queries per second over billions of documents, making query processing a major performance bottleneck. An important class of optimization...