Scoring is the most important part of Apache Lucene. It is the process of calculating the score property of a document in a scope of a given query. A score is a factor that describes how well the document matches the query. For score calculation, Lucene supports many algorithms, but since the beginning of Lucene, TF-IDF (term frequency-inverse document frequency) has been the default scoring algorithm. With the release of Apache Lucene 6.0, one of the major changes in Lucene is the changed default scoring algorithm. The default algorithm is now BM25 (Best Matching). In this section, we will also cover two fundamental concepts of search relevancy: precision and recall, and after that, we'll look at the new default Apache Lucene scoring mechanism and how it differs from TF-IDF.

Mastering Elasticsearch 5.x - Third Edition
By :

Mastering Elasticsearch 5.x - Third Edition
By:
Overview of this book
Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source
search and analytics engine. Elasticsearch leverages the capabilities of Apache Lucene, and provides a new level of control over how you can index and search even huge sets of data.
This book will give you a brief recap of the basics and also introduce you to the new features of Elasticsearch 5. We will guide you through the intermediate and advanced functionalities of Elasticsearch, such as querying, indexing, searching, and modifying data. We’ll also explore advanced concepts, including aggregation, index control, sharding, replication, and clustering.
We’ll show you the modules of monitoring and administration available in Elasticsearch, and will also cover backup and recovery. You will get an understanding of how you can scale your Elasticsearch cluster to contextualize it and improve its performance. We’ll also show you how you can create your own analysis plugin in Elasticsearch.
By the end of the book, you will have all the knowledge necessary to master Elasticsearch and put it to efficient use.
Table of Contents (20 chapters)
Mastering Elasticsearch 5.x - Third Edition
Credits
About the Author
Acknowledgements
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Revisiting Elasticsearch and the Changes
The Improved Query DSL
Beyond Full Text Search
Data Modeling and Analytics
Improving the User Search Experience
The Index Distribution Architecture
Low-Level Index Control
Elasticsearch Administration
Data Transformation and Federated Search
Improving Performance
Developing Elasticsearch Plugins
Introducing Elastic Stack 5.0
Customer Reviews