Chapter 11. Semantic and personalized search

 

This chapter covers

  • Making search personalized for individual users
  • Matching documents based on meaning rather than just words
  • Implementing recommendation as a generalization of search

You’re at the end of a long journey. You’ve learned to use search technology to build relevant search applications. But you’re still just scratching the surface. In this final chapter, we look toward the horizon to explore some of the more novel—and experimental—ways to improve your users’ search experience. In particular, we cover two related techniques that can provide better relevance:

  • Personalized search provides search results customized to a user’s particular tastes using knowledge about that user. User information can be gleaned from users’ previous interactions as well as anything they tell us directly.
  • Concept search ranks documents based on concepts extracted from text, not just words. Concept search relies on deep knowledge of the search domain, including jargon and the relations between concepts in that domain.

When used in tandem, the search solution understands users personal needs as well as the ideas latent in the content.

11.1. Personalizing search based on user profiles

 
 
 

11.2. Personalizing search based on user behavior

 

11.3. Basic methods for building concept search

 
 
 
 

11.4. Building concept search using machine learning

 
 

11.5. The personalized search—concept search connection

 
 

11.6. Recommendation as a generalization of search

 
 

11.7. Best wishes on your search relevance journey

 

11.8. Summary

 
 
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