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My to-read list from CIKM'09
Última hora, Investigación
Thursday, September 10, 2009, 10:00 AM
Thanks to Jeff Dalton I've noticed the list of accepted papers for CIKM 2009.

Needless to say, there is a bunch of them I'd really like to read (see below). Unfortunately, most of them are still not available online. This is the, much shorter, list of material I'm going to skim in the following days:

And the list of papers I'll read once they are available:
  • Mining Data Streams with Periodically Changing Distributions / Mining Frequent Itemsets in Time-Varying Data Streams
  • Detecting Topic Evolution in Scientific Literature: How Can Citations Help?
  • Beyond Hyperlinks: Organizing Information Footprints in Search Logs to Support Effective Browsing
  • Clustering and Exploring Search Results using Timeline Constructions
  • Event Detection from Flickr Data through Wavelet-based Spatial Analysis
  • Voting in Social Networks
  • Semi-Supervised Learning of Semantic Classes for Query Understanding -- from the Web and for the Web
  • SELC: A Self-Supervised Model for Sentiment Classification
  • Learning to Recommend Questions Based on User Ratings
  • Improving Web Page Classification by Label-propagation over Click Graphs
  • Practical Lessons of Data Mining at Yahoo!
  • Who Tags the Tags?
  • Context Sensitive Synonym Discovery for Web Search Queries
  • Identifying Comparable Entities on the Web
  • Exploiting Bidirectional Links: Making Spamming Detection Easier
  • Exploring Relevance for Clicks
  • Learning from Past Queries for Resource Selection
  • What Makes Categories Difficult to Classify?
  • Effective Anonymization of Query Logs
  • Pure Spreading Activation is Pointless
  • Collaborative Resource Discovery in Social Tagging Systems
  • Data Extraction from the Web Using Wild Card Queries
  • Aging Effects on Query Flow Graphs for Query Suggestion
  • An Analysis Framework for Search Sequences
  • Evaluation of Methods for Relative Comparison of Retrieval Systems Based on Clickthroughs
  • Identifying Interesting Assertions from the Web
  • Opinion Classification with Tree Kernel SVM Using Linguistic Modality Analysis

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