Optimizing search engines using clickthrough data
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Summary
The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking.
- Type
- article
- Published
- 2002-07-23
- Cited by
- 4,692
- References
- 30
- OpenAlex
- https://openalex.org/W2047221353
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:207605508
Keywords
Computer science, Ranking (information retrieval), Relevance (law), Information retrieval, Search engine
References
- AIR/X - A rule-based multistage indexing system for Iarge subject fields
- An Efficient Boosting Algorithm for Combining Preferences
- Making large scale SVM learning practical
- A Machine Learning Architecture for Optimizing Web Search Engines
- Agglomerative clustering of a search engine query log
- Term-Weighting Approaches in Automatic Text Retrieval
- Optimum polynomial retrieval functions based on the probability ranking principle
- Web Watcher: A Tour Guide for the World Wide Web
- Measuring Retrieval Effectiveness Based on User Preference of Documents
- Robust Trainability of Single Neurons
- Mathematical models in the social sciences
- A training algorithm for optimal margin classifiers
- Support-Vector Networks
- Learning to Order Things
- Letizia: An Agent That Assists Web Browsing
- An Introduction to the Theory of Statistics
- Rank Correlation Methods
- The Nature of Statistical Learning Theory
- Statistical Learning Theory
- Modern Information Retrieval
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- Ontology Based Personalized Search Engine
- Learning for information extraction: from named entity recognition and disambiguation to relation extraction
- Future Link Prediction in the Blogosphere for Recommendation
- Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS
- Improve Web Search Using Image Snippets
- Learning to Rank with Graph Consistency
- A Decision Theoretic Framework for Ranking using Implicit Feedback
- dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
- Studying, developing, and experimenting contextual advertising systems
- Automatic task-based profile representation for content-based recommendation
- Summarizing certainty in uncertain data
- Modeling coherence in ESOL learner texts
- From Flat to Hierarchical: Modeling Structures in Visual Recognition
- All in Strings: a Powerful String-based Automatic MT Evaluation Metric with Multiple Granularities
- Google+facebook: a social-network-optimized web search approach
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