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Home > Data > Enterprise Search > Apache Solr Search Patterns
Apache Solr Search Patterns
Apache Solr Search Patterns

Apache Solr Search Patterns: Leverage the power of Apache Solr to power up your business by navigating your users to their data quickly and efficiently

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Apache Solr Search Patterns

Chapter 2. Customizing the Solr Scoring Algorithm

In this chapter, we will go through the relevance calculation algorithm used by Solr for ranking results and understand how relevance calculation works with reference to the parameters in the algorithm. In addition to this, we will look at tweaking the algorithm and create our own algorithm for scoring results. Then, we will add it as a plugin to Solr and see how the search results are ranked. We will discuss the problems with the default algorithm used in Solr and define a new algorithm known called the information gain model. This chapter will incorporate the following topics:

  • The relevance calculation algorithm
  • Building a custom scorer
  • Drawback of the TF-IDF model
  • The information gain model
  • Implementing the information gain model
  • Options to TF-IDF similarity
  • BM25 similarity
  • DFR similarity

Relevance calculation

Now that we are aware of how Solr works in the creation of an inverted index and how a search returns results for a query from an index, the question that comes to our mind is how Solr or Lucene (the underlying API) decides which documents should be at the top and how the results are sorted. Of course, we can have custom sorting, where we can sort results based on a particular field. However, how does sorting occur in the absence of any custom sorting query?

The default sorting mechanism in Solr is known as relevance ranking. During a search, Solr calculates the relevance score of each document in the result set and ranks the documents so that the highest scoring documents move to the top. Scoring is the process of determining how relevant a given document is with respect to the input query. The default scoring mechanism is a mix of the Boolean model and the Vector Space Model (VSM) of information retrieval. The binary model is used to figure out documents that match...

Building a custom scorer

Now that we know how the default relevance calculation works, let us look at how to create a custom scorer. The default scorer used for relevance calculation is known as DefaultSimilarity. In order to create a custom scorer, we will need to extend DefaultSimilarity and create our own similarity class and eventually use it in our Solr schema. Solr also provides the option of specifying different similarity classes for different fieldTypes configuration directive in the schema. Thus, we can create different similarity classes and then specify different scoring algorithms for different fieldTypes as also a different global Similarity class.

Let us create a custom scorer that disables the IDF factor of the scoring algorithm. Why would we want to disable the IDF? The IDF boosts documents that have query terms that are rare in the index. Therefore, if a query contains a term that occurs in fewer documents, the documents containing the term will be ranked higher. This does...

Drawbacks of the TF-IDF model

Suppose, on an e-commerce website, a customer is searching for a jacket and intends to purchase a jacket with a unique design. The keyword entered is unique jacket. What happens at the Solr end?

http://solr.server/solr/clothes/?q=unique+jacket

Now, unique is a comparatively rare keyword. There would be fewer items or documents that mention unique in their description. Let us see how this affects the ranking of our results via the TF-IDF scoring algorithm. A relook at the scoring algorithm with respect to this query is shown in the following diagram:

Drawbacks of the TF-IDF model

A relook at the TF-IDF scoring algorithm

The following parameters in the scoring formula do not affect the ranking of the documents in the query result:

  • coord(q,d): This would be constant for a MUST query. Herein we are searching for both unique and jacket, so all documents will have both the keywords and the coord(q,d) value will be the same for all documents.
  • queryNorm(q): This is used to make the scores from different...

The information gain model

The information gain model is a type of machine learning concept that can be used in place of the inverse document frequency approach. The concept being used here is the probability of observing two terms together on the basis of their occurrence in an index. We use an index to evaluate the occurrence of two terms x and y and calculate the information gain for each term in the index:

  • P(x): Probability of a term x appearing in a listing
  • P(x|y): Probability of the term x appearing given a term y also appears

The information gain value of the term y can be computed as follows:

The information gain model

Information gain equation

This equation says that the more number of times term y appears with term x with respect to the total occurrence of term x, the higher is the information gain for that y.

Let us take a few examples to understand the concept.

In the earlier example, if the term unique appears with jacket a large number of times as compared to the total occurrence of the term jacket, then unique...

Implementing the information gain model

The problem with the information gain model is that, for each term in the index, we will have to evaluate the occurrence of every other term. The complexity of the algorithm will be of the order of square of the two terms, square(xy). It is not possible to compute this using a simple machine. What is recommended is that we create a map-reduce job and use a distributed Hadoop cluster to compute the information gain for each term in the index.

Our distributed Hadoop cluster would do the following:

  • Count all occurrences of each term in the index
  • Count all occurrences of each co-occurring term in the index
  • Construct a hash table or a map of co-occurring terms
  • Calculate the information gain for each term and store it in a file in the Hadoop cluster

In order to implement this in our scoring algorithm, we will need to build a custom scorer where the IDF calculation is overwritten by the algorithm for deriving the information gain for the term from the Hadoop cluster...

Relevance calculation

Now that we are aware of how Solr works in the creation of an inverted index and how a search returns results for a query from an index, the question that comes to our mind is how Solr or Lucene (the underlying API) decides which documents should be at the top and how the results are sorted. Of course, we can have custom sorting, where we can sort results based on a particular field. However, how does sorting occur in the absence of any custom sorting query?

The default sorting mechanism in Solr is known as relevance ranking. During a search, Solr calculates the relevance score of each document in the result set and ranks the documents so that the highest scoring documents move to the top. Scoring is the process of determining how relevant a given document is with respect to the input query. The default scoring mechanism is a mix of the Boolean model and the Vector Space Model (VSM) of information retrieval. The binary model is used to figure out documents that match...

Building a custom scorer

Now that we know how the default relevance calculation works, let us look at how to create a custom scorer. The default scorer used for relevance calculation is known as DefaultSimilarity. In order to create a custom scorer, we will need to extend DefaultSimilarity and create our own similarity class and eventually use it in our Solr schema. Solr also provides the option of specifying different similarity classes for different fieldTypes configuration directive in the schema. Thus, we can create different similarity classes and then specify different scoring algorithms for different fieldTypes as also a different global Similarity class.

Let us create a custom scorer that disables the IDF factor of the scoring algorithm. Why would we want to disable the IDF? The IDF boosts documents that have query terms that are rare in the index. Therefore, if a query contains a term that occurs in fewer documents, the documents containing the term will be ranked higher. This does...

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Description

This book is for developers who already know how to use Solr and are looking at procuring advanced strategies for improving their search using Solr. This book is also for people who work with analytics to generate graphs and reports using Solr. Moreover, if you are a search architect who is looking forward to scale your search using Solr, this is a must have book for you. It would be helpful if you are familiar with the Java programming language.

Who is this book for?

This book is for developers who already know how to use Solr and are looking at procuring advanced strategies for improving their search using Solr. This book is also for people who work with analytics to generate graphs and reports using Solr. Moreover, if you are a search architect who is looking forward to scale your search using Solr, this is a must have book for you.

What you will learn

  • Customize the Solr scoring algorithm to get better and more relevant search results Use Solr with big data for analytical purposes Get insights into Solr internals-indexing and search Setting up and scaling with Solr cloud Implement spatial search with Solr Understand Finite State Transducers (FST) and implement text tagging using FST Breeze through the strategies used in executing search using Solr in e-commerce, advertising, and real estate websites Learn more about how to use Solr with AJAX

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 24, 2015
Length: 316 pages
Edition : 1st
Language : English
ISBN-13 : 9781783981854
Vendor :
Apache
Category :
Data
Languages :
Java
Concepts :
Enterprise Search
Tools :
Solr

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Publication date : Apr 24, 2015
Length: 316 pages
Edition : 1st
Language : English
ISBN-13 : 9781783981854
Vendor :
Apache
Category :
Data
Languages :
Java
Concepts :
Enterprise Search
Tools :
Solr

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Table of Contents

11 Chapters
1. Solr Indexing Internals Chevron down icon Chevron up icon
1. Solr Indexing Internals
The job site problem statement – Solr indexing fundamentals
Working of analyzers, tokenizers, and filters
Handling a multilingual search
Measuring the quality of search results
The e-commerce problem statement
The job site problem statement
Challenges of large-scale indexing
The SolrCloud solution
Summary
2. Customizing the Solr Scoring Algorithm Chevron down icon Chevron up icon
2. Customizing the Solr Scoring Algorithm
Relevance calculation
Building a custom scorer
Drawbacks of the TF-IDF model
The information gain model
Implementing the information gain model
Options to TF-IDF similarity
Summary
3. Solr Internals and Custom Queries Chevron down icon Chevron up icon
3. Solr Internals and Custom Queries
Working of a scorer on an inverted index
Working of OR and AND clauses
The eDisMax query parser
Using BRS queries instead of DisMax
Building a custom query parser
Summary
4. Solr for Big Data Chevron down icon Chevron up icon
4. Solr for Big Data
Introduction to big data
Getting data points using facets
Radius faceting for location-based data
Data analysis using pivot faceting
Graphs for analytics
Summary
5. Solr in E-commerce Chevron down icon Chevron up icon
5. Solr in E-commerce
Designing an e-commerce search
Handling unclean data
Handling variations in the product
Sorting
Problems and solutions of flash sale searches
Faceting with the option of multi-select
Faceting with hierarchical taxonomy
Faceting with size
Implementing semantic search
Optimizations
Summary
6. Solr for Spatial Search Chevron down icon Chevron up icon
6. Solr for Spatial Search
Features of spatial search
Lucene 4 spatial module
Indexing for spatial search
Searching and filtering on a spatial index
Distance sort and relevancy boost
Advanced concepts
Summary
7. Using Solr in an Advertising System Chevron down icon Chevron up icon
7. Using Solr in an Advertising System
Ad system functionalities
Architecture of an ad distribution system
Requirements of an ad distribution system
Performance improvements
Merging Solr with Redis
Summary
8. AJAX Solr Chevron down icon Chevron up icon
8. AJAX Solr
The purpose of AJAX Solr
The AJAX Solr architecture
Working with AJAX Solr
Performance tuning
Summary
9. SolrCloud Chevron down icon Chevron up icon
9. SolrCloud
The SolrCloud architecture
Centralized configuration
Setting up SolrCloud
Distributed indexing and search
Routing documents to a particular shard
Adding more nodes to the SolrCloud
Fault tolerance and high availability in SolrCloud
Advanced sharding with SolrCloud
Asynchronous calls
Migrating documents to another collection
Sizing and monitoring of SolrCloud
Using SolrCloud as a NoSQL database
Summary
10. Text Tagging with Lucene FST Chevron down icon Chevron up icon
10. Text Tagging with Lucene FST
An overview of FST and text tagging
Implementation of FST in Lucene
Text tagging algorithms
Using Solr for text tagging
Implementing a text tagger using Solr
Summary
Index Chevron down icon Chevron up icon
Index

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(2 Ratings)
5 star 50%
4 star 50%
3 star 0%
2 star 0%
1 star 0%
BMille Jul 02, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It does a great job of in detail on important items.
Amazon Verified review Amazon
Tim Crothers Jun 19, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book is an excellent treatise on how to use and tune Solr for various uses. I really enjoyed how the author struck a good balance between hands-on specifics and details while still covering the breadth of the application needs. In particular including details like how to tune and handle language differences (a necessity typically overlooked in most technical books) was very appreciated. Given the depth covered by the author developers of every level of experience with Solr should find a lot of value in this book. I had moderate experience with Solr when I picked this book up and definitely learned several useful techniques to add to my toolkit.
Amazon Verified review Amazon

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About the author

Profile icon Jayant Kumar
Jayant Kumar
Jayant Kumar is an experienced software professional with a bachelor of engineering degree in computer science and more than 14 years of experience in architecting and developing large-scale web applications. Jayant is an expert on search technologies and PHP and has been working with Lucene and Solr for more than 11 years now. He is the key person responsible for introducing Lucene as a search engine on www.naukri.com, the most successful job portal in India. Jayant is also the author of the book Apache Solr PHP Integration, Packt Publishing, which has been very successful. Jayant has played many different important roles throughout his career, including software developer, team leader, project manager, and architect, but his primary focus has been on building scalable solutions on the Web. Currently, he is associated with the digital division of HT Media as the chief architect responsible for the job site www.shine.com. Jayant is an avid blogger and his blog can be visited at http://jayant7k.blogspot.in. His LinkedIn profile is available at http://www.linkedin.com/in/jayantkumar.
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