Search: wsdm09 - Matches: 36
|Second ACM International Conference on Web Search and Data Mining - WSDM 2009|
WSDM (pronounced "wisdom") is a young ACM conference intended to be the publication venue for research in the areas of search and data mining. Indeed, the pace of innovation in these areas prevents proper coverage by conferences of broader scope. ...
|Ricardo Baeza-Yates: Welcome and Introduction to WSDM 2009|
Harvesting, Searching, and Ranking Knowledge from the Web |
There are major trends to advance the functionality of search engines to a more expressive semantic level. This is enabled by employing large-scale information extraction of entities and relationships from semistructured as well as natural-language Web sources. In addition, harnessing ...
Challenges in Building Large-Scale Information Retrieval Systems|
Building and operating large-scale information retrieval systems used by hundreds of millions of people around the world provides a number of interesting challenges. Designing such systems requires making complex design tradeoffs in a number of dimensions, including (a) the number ...
|Kazuhiro Seki, Kuniaki Uehara: Adaptive Subjective Triggers for Opinionated Document Retrieval|
|Fernando Diaz: Aggregation of News Content Into Web Results|
|Álvaro Pereira, Ricardo Baeza-Yates, Nivio Ziviani, Jesus Bisbal: A Model for Fast Web Mining Prototyping|
|Songhua Xu, Tao Jin, Francis C.M. Lau: A New Visual Search Interface for Web Browsing|
|Adish Singla, Ingmar Weber: Camera Brand Congruence in the Flickr Social Graph|
|Simon E. Overell, Börkur Sigurbjörnsson, Roelof Van Zwol: Classifying Tags using Open Content Resources|
|Daniel Ramage, Paul Heymann, Christopher D. Manning, Hector Garcia-Molina: Clustering the Tagged Web|
Paul Heymann, Hector Garcia-Molina:
Contrasting Controlled Vocabulary and Tagging: Do Experts Choose the Right Names to Label the Wrong Things?|
Social cataloging sites—tagging systems where users tag books—provide us with a rare opportunity to contrast tags to other information organization systems. We contrast tags to a controlled vocabulary, the Library of Congress Subject Headings, which has been developed over several ...
|Xuerui Wang, Andrei Broder, Evgeniy Gabrilovich, Vanja Josifovski, Bo Pang: Cross-Language Query Classification using Web Search for Exogenous Knowledge|
|Jaime Teevan, Meredith Ringel Morris, Steve Bush: Discovering and Using Groups to Improve Personalized Search|
|Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, Samuel Ieong: Diversifying Search Results|
|Hongbo Deng, Michael R. Lyu, Irwin King: Effective Latent Space Graph-based Re-ranking Model with Global Consistency|
|Fan Guo, Chao Liu, Yi-Min Wang: Efficient Multiple-Click Models in Web Search|
|Michael Bendersky, Bruce Croft: Finding Text Reuse on the Web|
|Eytan Adar, Michael Skinner, Daniel S. Weld: Information Arbitrage Across Multi-lingual Wikipedia|
|Jaap Kamps, Marijn Koolen: Is Wikipedia Link Structure Different?|
|Marc Najork, Sreenivas Gollapudi, Rina Panigrahy: Less is More: Sampling the Neighborhood Graph Makes SALSA Better and Faster|
|Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka: Measuring the Similarity between Implicit Semantic Relations using Web Search Engines|
|Benjamin Piwowarski, Georges Dupret, Rosie Jones: Mining User Web Search Activity with Layered Bayesian Networks or How to Capture a Click in its Context|
Aixin Sun, Anwitaman Datta:
On Stability, Clarity, and Co-occurrence of Self-Tagging|
Most studies on tags focus on collaborative tagging systems where each resource (e.g., article, photo) can be tagged by multiple users with multiple tags. The tag usage patterns in self-tagging systems where a resource (e.g., a blog post) can only ...
|Maggy Anastasia Suryanto, Ee-Peng Lim, Aixin Sun, Roger Chiang: Quality-Aware Collaborative Question Answering: Methods and Evaluation|
|David Yin Yang, Nilesh Bansal, Wisam Dakka, Panagiotis G. Ipeirotis, Nick Koudas, Dimitris Papadias: Query by Document|
Bruno Gonçalves, Mark R. Meiss, José Javier Ramasco:
Remembering what we like: Toward an agent-based model of Web traffic|
Analysis of aggregate Web traffic has shown that PageRank is a poor model of how people actually navigate the Web. Using the empirical traffic patterns generated by a thousand users over the course of two months, we characterize the properties ...
|Chinmay D. Karande: Speeding up Algorithms on Compressed Web Graphs|
Ioannis Antonellis, Hector Garcia-Molina, Jawed Karim:
Tagging with Queries: How and Why?|
Web search queries capture the information need of search engine users. Search engines store these queries in their logs and analyze them to guide their search results. In this work, we argue that not only a search engine can benefit ...
|Eytan Adar, Jaime Teevan, Susan T. Dumais, Jonathan Elsas: The Web Changes Everything: Understanding the Dynamics of Web Content|
Irem Arikan, Srikanta Bedathur, Klaus Berberich:
Time Will Tell: Leveraging Temporal Expressions in IR|
Temporal expressions, such as between 1992 and 2000, are frequent across many kinds of documents. Text retrieval, though, treats them as common terms, thus ignoring their inherent semantics. For queries with a strong temporal component, such as U.S. president 1997, ...
|Ravi Kumar, Kunal Punera, Torsten Suel, Sergei Vassilvitskii: Top-k Aggregation Using Intersection of Ranked Inputs|
Yiqun Liu, Yijiang Jin, Min Zhang, Shaoping Ma, Liyun Ru:
User Browsing Graph: Structure, Evolution and Application|
This paper focuses on ‘user browsing graph’ which is constructed with users’ click-through behavior modeled with Web access logs. User browsing graph has recently been adopted to improve Web search performance and the initial study shows it is more reliable ...
|Marijn Koolen, Gabriella Kazai, Nick Craswell: Wikipedia Pages as Entry Points for Book Search|
|Ricardo Baeza-Yates: WSDM 2009 Closing Talk|
|Paolo Boldi: WSDM 2009 Program Committee Report|