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This workshop is intended for people who are
interested in both machine learning and web search. With its tens of
billions of unstructured and dynamic pages and its increasing number of
users, the World Wide Web poses new great challenges to the existing
machine learning algorithms, and at the same time it also fuels the rapid
development of new machine learning techniques. This workshop aims at
bringing machine learning and web search people together to discuss the
fundamental issues in web search from relevance ranking and web spam
detection to online advertising.
Topics to be discussed:
- Web page ranking: ranking algorithms and theory,
rank aggregation, link analysis
- Online advertising: click-rate prediction,
keyword generation, clicks fraud detection, content matching, and
auction mechanism
- Usage data: learning from query and click-through
logs, interactive experimentation, active learning,
exploration/exploitation
- Web spam detection: link spam, content spam, blog
spam, cloaking
- Query rewriting: spelling check, query
alternation, query suggestion, query classification
- Social networks: online community discovering,
trust and reputation, collaborative filtering
- Large-scale machine learning for web search
issues
Invited speakers:
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