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Home » Technology » Big Data & Data Science » Relevant Search

Course Skill Level:

Foundational

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    RELSEAL21E09

Who should attend & recommended skills:

Developers

Who should attend & recommended skills

  • Developers trying to build smarter search with Elasticsearch or Solr – those who want to know how to return engaging search results to your users, helping you understand and leverage the internals of Lucene-based search engines.
  • Skill-level: Foundation-level Relevant Search skills for Intermediate skilled team members. This is not a basic class.

About this course

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.

Skills acquired & topics covered

  • Developers trying to build smarter search with Elasticsearch or Solr – those who want to know how to return engaging search results to your users, helping you understand and leverage the internals of Lucene-based search engines.
  • Skill-level: Foundation-level Relevant Search skills for Intermediate skilled team members. This is not a basic class.

Course breakdown / modules

  • Your goal: gaining the skills of a relevance engineer
  • Why is search relevance so hard?
  • Gaining insight from relevance research
  • How do you solve relevance?
  • More than technology: curation, collaboration, and feedback

  • Search 101
  • Search engine data structures
  • Indexing content: extraction, enrichment, analysis, and indexing
  • Document search and retrieval

  • Applications to Solr and Elasticsearch: examples in Elasticsearch
  • Our most prominent data set: TMDB
  • Examples programmed in Python
  • Your first search application
  • Debugging query matching
  • Debugging ranking
  • Solved? Our work is never over!

  • Tokens as document features
  • Controlling precision and recall
  • Precision and recall-have your cake and eat it too
  • Analysis strategies

  • Signals and signal modeling
  • TMDB-search, the final frontier!
  • Signal modeling in field-centric search

  • What is term-centric search?
  • Why do you need term-centric search?
  • Performing your first term-centric searches
  • Solving signal discordance in term-centric search
  • Combining field-centric and term-centric strategies: having your – cake and eating it too

  • What do we mean by score shaping?
  • Boosting: shaping by promoting results
  • Filtering: shaping by excluding results
  • Score-shaping strategies for satisfying business needs

  • Relevance feedback at the search box
  • Relevance feedback while browsing
  • Relevance feedback in the search results listing

  • Yowl! The awesome new start-up!
  • Gathering information and requirements
  • Designing the search application
  • Deploying, monitoring, and improving
  • Knowing when good is good enough

  • Feedback: the bedrock of the relevance-centered enterprise
  • Why user-focused culture before data-driven culture?
  • Flying relevance-blind
  • Relevance feedback awakenings: domain experts and expert users
  • Relevance feedback maturing: content curation
  • Relevance streamlined: engineer/curator pairing
  • Relevance accelerated: test-driven relevance
  • Beyond test-driven relevance: learning to rank

  • Personalizing search based on user profiles
  • Personalizing search based on user behavior
  • information back to the search index
  • Basic methods for building concept search
  • Building concept search using machine learning
  • The personalized search-concept search connection
  • Recommendation as a generalization of search
  • Best wishes on your search relevance journey

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