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Working in a hands-on learning environment, led by our Relevant Search expert instructor, students will learn about and explore: You'll learn how to apply Elasticsearch or Solr to your business's unique ranking problems.demonstrates how to program relevance and how to incorporate secondary data sources, taxonomies, text analytics, and personalization

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Unlimited Duration

Last Updated

February 4, 2021

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Course Description

Relevant Search demystifies the subject and shows you that a search engine is a programmable relevance framework. You'll learn how to apply Elasticsearch or Solr to your business's unique ranking problems. The course demonstrates how to program relevance and how to incorporate secondary data sources, taxonomies, text analytics, and personalization. In practice, a relevance framework requires softer skills as well, such as collaborating with stakeholders to discover the right relevance requirements for your business. By the end, you?ll be able to achieve a virtuous cycle of provable, measurable relevance improvements over a search product?s lifetime

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Course Curriculum

    • Your goal: gaining the skills of a relevance engineer 00:00:00
    • Why is search relevance so hard? 00:00:00
    • Gaining insight from relevance research 00:00:00
    • How do you solve relevance? 00:00:00
    • More than technology: curation, collaboration, and feedback 00:00:00
    • Search 101 00:00:00
    • Search engine data structures 00:00:00
    • Indexing content: extraction, enrichment, analysis, and indexing 00:00:00
    • Document search and retrieval 00:00:00
    • Applications to Solr and Elasticsearch: examples in Elasticsearch 00:00:00
    • Our most prominent data set: TMDB 00:00:00
    • Examples programmed in Python 00:00:00
    • Your first search application 00:00:00
    • Debugging query matching 00:00:00
    • Debugging ranking 00:00:00
    • Solved? Our work is never over! 00:00:00
    • Tokens as document features 00:00:00
    • Controlling precision and recall 00:00:00
    • Precision and recall—have your cake and eat it too 00:00:00
    • Analysis strategies 00:00:00
    • Signals and signal modeling 00:00:00
    • TMDB—search, the final frontier! 00:00:00
    • Signal modeling in field-centric search 00:00:00
    • What is term-centric search? 00:00:00
    • Why do you need term-centric search? 00:00:00
    • Performing your first term-centric searches 00:00:00
    • Solving signal discordance in term-centric search 00:00:00
    • Combining field-centric and term-centric strategies: having your – cake and eating it too 00:00:00
    • What do we mean by score shaping? 00:00:00
    • Boosting: shaping by promoting results 00:00:00
    • Filtering: shaping by excluding results 00:00:00
    • Score-shaping strategies for satisfying business needs 00:00:00
    • Relevance feedback at the search box 00:00:00
    • Relevance feedback while browsing 00:00:00
    • Relevance feedback in the search results listing 00:00:00
    • Yowl! The awesome new start-up! 00:00:00
    • Gathering information and requirements 00:00:00
    • Designing the search application 00:00:00
    • Deploying, monitoring, and improving 00:00:00
    • Knowing when good is good enough 00:00:00
    • Feedback: the bedrock of the relevance-centered enterprise 00:00:00
    • Why user-focused culture before data-driven culture? 00:00:00
    • Flying relevance-blind 00:00:00
    • Relevance feedback awakenings: domain experts and expert users 00:00:00
    • Relevance feedback maturing: content curation 00:00:00
    • Relevance streamlined: engineer/curator pairing 00:00:00
    • Relevance accelerated: test-driven relevance 00:00:00
    • Beyond test-driven relevance: learning to rank 00:00:00
    • Personalizing search based on user profiles 00:00:00
    • Personalizing search based on user behavior 00:00:00
    • information back to the search index 00:00:00
    • Basic methods for building concept search 00:00:00
    • Building concept search using machine learning 00:00:00
    • The personalized search—concept search connection 00:00:00
    • Recommendation as a generalization of search 00:00:00
    • Best wishes on your search relevance journey 00:00:00

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