Is Faceted Search Right for You?


Is Faceted Search Right for You?

Although nowadays, if you google “hp zbook vs elitebook,” you will instantly find relevant results, during the internet’s humble beginnings, we had to click and scroll through countless pages. Of course, at the time, search engines were always compared to physical libraries, so we still marvelled at their uniqueness and novelty. Yet in spite of our disdain for the ancient paper system, ironically, it was precisely a librarian who first conceptualised faceted navigation, one of the most advanced search features in software today. After completing his education in library science at University College London, Indian Mathematician Siyali Ranganathan took immediate steps to reorganise libraries in a more scientific manner. In 1933, he published Colon Classification, a book describing what today is hailed as the very first faceted system. Since then, Ranganathan’s creation has evolved to become a standard search feature in e-commerce websites such as Amazon and eBay, along with other internet giants like Google. Yet despite its illustrious advocates, faceted search may be used by any company wishing to help customers quickly and intuitively browse through vast quantities of data. A window shows faceted search in action.
The system is best explained through examples, because at this point, although we may not recognise it by name, we have certainly all come to use faceted search. It is the handy filtering feature that allows users to narrow their search results down to the products they desire. For instance, if a user would only like to see laptops with a 15in screen, a 1TB hard drive and a cost between £500 and £700, they would simply have to search for the product, and then with just a few extra clicks, select the appropriate characteristics — or “facets” — that would reduce thousands of devices to a list of 100 or less. While this convenience is easily taken for granted, the actual process of implementing these systems can be extremely complex and time-consuming. Although software engineers are able to create the illusion of searching through millions of products in real time, in reality, such a feat would require enormous computing power. Instead, sophisticated algorithms are created beforehand to tag items with custom metadata or automatically extracted text. This can be accomplished rather quickly for small volumes of data, but as a company grows, implementing large-scale faceted systems often becomes a considerable investment. Despite anything to the contrary, bigger companies that require sorting through thousands or even millions of items can greatly benefit from the feature, as smart facets may even be customised to teach consumers the sort of questions they should be asking themselves, much as a salesperson would do in a conventional store. In this way, customers searching for a dress may be guided to narrow results by size, brand, colour and any other chosen facets. This creates a much more complete and fluid shopping experience for desktop and mobile devices alike. But faceted search is most definitely not exclusive to retailers. Today, companies from every sector are finding new ways to use the technology, and for these, it is important to note that maintenance may at times be necessary. As more items are added to the database, it is not uncommon for a faceted system to eventually stop working as it used to. In this case, software developers are able to make the required adjustments to ensure systems are equipped to handle additional volumes of data. With a recent survey revealing that no more than 40 percent of American retailers employ these systems, much like Ranganathan was years ahead of his time, companies leveraging faceted search today are able to place themselves vastly ahead of the competition. In order to help you stand out on the world stage, Software Planet Group use state-of-the-art technologies like Apache Lucene and Solr to deliver advanced faceted solutions for both small and large businesses  so your customers never have to stress over timeworn search features again.

Related Stories

How to Deal with Performance Problems in Software Development Img
February 8, 2023

Software Optimisation and Performance Profiling

Everything you need to know about software optimisation,performance testing,problems and profiling. What are the top performance profiling tools today

December 18, 2019

Top Software Development Technologies In 2020

AI-vs-Machine-Learning (AI vs ML)
February 9, 2023

AI vs Machine Learning: What Is the Difference?

Artificial Intelligence and Machine Learning.With these terms increasingly used with every new product, join us today as we investigate the AI vs ML enigma.