Relevance ranking for vertical search engines
In plain, uncomplicated language, and using detailed examples to explain the key concepts, models, and algorithms in vertical search ranking, Relevance Ranking for Vertical Search Engines teaches readers how to manipulate ranking algorithms to achieve better results in real-world applications. T...
| Otros Autores: | , , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Morgan Kaufmann, an imprint of Elsevier
2014.
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| Edición: | 1st edition |
| Colección: | Gale eBooks
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627909906719 |
Tabla de Contenidos:
- Half Title; Title Page; Copyright; Contents; List of Tables; List of Figures; About the Editors; List of Contributors; Foreword; 1 Introduction; 1.1 Defining the Area; 1.2 The Content and Organization of This Book; 1.3 The Audience for This Book; 1.4 Further Reading; 2 News Search Ranking; 2.1 The Learning-to-Rank Approach; 2.1.1 Related Works; 2.1.2 Combine Relevance and Freshness; 2.1.2.1 Training Sample Collection; 2.1.2.2 Editorial Labeling; 2.2 Joint Learning Approach from Clickthroughs; 2.2.1 Joint Relevance and Freshness Learning; 2.2.2 Temporal Features; 2.2.2.1 URL Freshness Features
- 2.2.2.2 Query Freshness Features2.2.3 Experiment Results; 2.2.3.1 Datasets; 2.2.3.2 Click Datasets; 2.2.3.3 Preference Pair Selection; 2.2.3.4 Temporal Feature Implementation; 2.2.3.5 Baselines and Evaluation Metrics; 2.2.4 Analysis of JRFL; 2.2.4.1 Convergency; 2.2.4.2 Relevance and Freshness Learning; 2.2.4.3 Query Weight Analysis; 2.2.5 Ranking Performance; 2.3 News Clustering; 2.3.1 Architecture of the System; 2.3.2 Offline Clustering; 2.3.2.1 Feature Vector Generation; 2.3.2.2 Minhash Signature Generation; 2.3.2.3 Duplicate Detection; 2.3.2.4 Locality-Sensitive Hashing
- 2.3.2.5 Correlation Clustering2.3.2.6 Evaluation; 2.3.3 Incremental Clustering; 2.3.4 Real-Time Clustering; 2.3.4.1 Meta Clustering and Textual Matching; 2.3.4.2 Contextual Query-Based Term Weighting; 2.3.4.3 Offline Clusters as Features; 2.3.4.4 Performance Analysis; 2.3.5 Experiments; 2.3.5.1 Experimental Setup; 2.3.5.2 Evaluation Metrics; 2.3.5.3 Evaluating Meta Clustering and Textual Matching; 2.3.5.4 Results with QrySim; 2.3.5.5 Results with Offline Clusters as Features; 3 Medical Domain Search Ranking; 3.1 Search Engines for Electronic Health Records; 3.2 Search Behavior Analysis
- 3.3 Relevance Ranking3.3.1 Insights from the TREC Medical Record Track; 3.3.2 Implementing and Evaluating Relevance Ranking in EHR Search Engines; 3.4 Collaborative Search; 3.5 Conclusion; 4 Visual Search Ranking; 4.1 Generic Visual Search System; 4.2 Text-Based Search Ranking; 4.2.1 Text Search Models; 4.2.2 Textual Query Preprocessing; 4.2.2.1 Query Expansion; 4.2.2.2 Stemming Algorithm; 4.2.2.3 Stopword Removal; 4.2.2.4 N-Gram Query Segmentation; 4.2.2.5 Part-of-Speech Tagging; 4.2.3 Text Sources; 4.3 Query Example-Based Search Ranking; 4.3.1 Low-Level Visual Features
- 4.3.1.1 Global Feature4.3.1.2 Region Features; 4.3.1.3 Local Features; 4.3.2 Distance Metrics; 4.4 Concept-Based Search Ranking; 4.4.1 Query-Concept Mapping; 4.4.2 Search with Related Concepts; 4.5 Visual Search Reranking; 4.5.1 First Paradigm: Self-Reranking; 4.5.2 Second Paradigm: Example-Based Reranking; 4.5.3 Third Paradigm: Crowd Reranking; 4.5.4 Fourth Paradigm: Interactive Reranking; 4.6 Learning and Search Ranking; 4.6.1 Ranking by Classification; 4.6.2 Classification vs. Ranking; 4.6.3 Learning to Rank; 4.7 Conclusions and Future Challenges; 5 Mobile Search Ranking
- 5.1 Ranking Signals
