ElasticSearch: Boosting score for content relevancy

Every search solution is build to serve relevant content to the users. Each of these search solutions provide different algorithms and mechanism for you to serve the relevant content matching your business requirements. The flexibility provided in terms of affecting and manipulating the relevancy of search results for your content allows business to serve end customers better. In this post, we will cover in details how ElasticSearch helps you to retrieve the relevant content and the ways to affect the scoring of your search data.

Content Relevance Ranking

Content relevancy is to return the relevant documents for a search query. Every business domain has its own relevancy for the content. For example, a typical eCommerce platform will have different relevancy for products based on different customers and different search criteria on different times.

You would like each product to score differently based on different criteria. Some of the typical requirements from business side for relevancy of the products,

• Relevancy based on search term, how many time a particular term occurs for a document
• Relevancy based on different content fields of the document, eg. title is more important than description for a document
• Relevancy based on combination of different field values
• Relevancy based on current conditions and field values at the query time
• Negative relevancy to some products based on certain ceriteria or field values
• Higher relevancy for new data
• Higher relevancy to products which are most liked, visited etc.

ElasticSearch: Faceted Search for Hierarchical data

Faceted Search is the navigational search allowing business to clearly define the properties or characteristics of the product catalog and navigate user to find relevant products with minimum efforts. Most of the available search solutions support the functionality now a days, in this post we will cover how to implemented faceted search using flattened data approach for hierarchical data using ElasticSearch for a typical eCommerce platform.

Search Scenarios/Business Example:

Earlier post, Data Modeling approach for search content and tagging  explains the different characteristics of a typical eCommerce platform serving hierarchical data in terms of categorization of data and sub categorization.

Take an example of such a typical eCommerce platform where one site you need to display the Navigration browsing of your hierarchical data based on some search solution. For example, you need to display products like Books/Clothes etc. Each product has its own specific characteristics and can be categoriezed in different categories and sub categories.

Hierarchical Data:

The hierarchical data in business form represents the taxonomy for your data. The way you can characterize your data in the form of different category type, categories and sub categories for the product catalog.

Getting started with ElasticSearch

A quick introduction to ElasticSearch, an open source, ditributed and RESTful search engine based on lucene, and how easily you can start working with it.

Search Solution

As mentioned in the earlier post, Choosing the right search solution for your site, feel free to analyze a best suited search solution for your requirements. In below section, we will cover further some of  functionality and capabilities offered by ElasticSearch.

ElasticSearch

In brief, ElasticSearch is open source, distributed, Schema Less and RESTful search engine based on Lucene. Some of the typical functionality of ES are,

Distributed: aggregated results of search performed on multiple shards/indices

Schema Less: is document oriented. Supports JSON format, automatic mapping types is supported.

RESTful: supports REST interface

Faceted Search: support for navigational search functionality

Replication: supports index replication

Fail over: replication and distributed nature provides inbuilt fail over.

Near Real time: supports near real time updates

Versioning: allows to store different versions of document.

Percolation: allows to register queries against an index, returning matching queries for a doc.

Index Aliasing: allows to create alias for indices.

Choosing the right search solution for your site

Effective search solution is the first step to provide relevant data to the end users for any eCommerce site. The right combination of marketing rules and corresponding technical capabilities of the solution put you closer to end customer inline with the target customer. In this article we will cover some of the different characteristics of a search solution which you need to focus upon while choosing a search solution for your site.

Your data

The requirements for the search solution vary from organization to organization and business to business. Each organization or domain have different variations of data. Some deal with small end user facing data and some have huge internal data. The common case of eCommerce sites is to store products data and making those available to the end customers with the intention to provide end customers the flexibility to search for the right content.

Search Solution Selection

Search Solutions

Now a days there are quite some options available in market to choose from the search solutions, ranging from open source to commercial products.