What is search relevance?

Search relevance measures how well search results provided by a search engine align with a user's query and intent. The quality of search results can be determined by how accurately the information displayed matches a user’s expectations.

Improving search relevance and performance involves linguistic analysis, ranking algorithms, and contextual factors. These might include user behavior analytics, location data, popularity, and search history. Search relevance is a huge factor in customer experience, and with the right balance, search can be optimized to meet both the needs of a business and its users.

Unpack why relevance is so critical to search engines and how to achieve the best relevance ranking capabilities with Elasticsearch. This video highlights the ways building modern search apps can help end-users find relevant results in real time.

Search relevance is powered by complex algorithms that assess, process, retrieve, and rank information. Algorithms for search relevance decode the semantics of keywords and intent, evaluate the authority of sources, and decode signals like engagement, geography, and personal preferences.

Common search-related algorithms include:

TF-IDF (Term Frequency-Inverse Document Frequency) is used to evaluate the importance of keywords, rewarding terms that appear frequently in target documents but not in other documents.

RankBrain is an AI-driven algorithm developed by Google that uses machine learning to interpret and process queries to achieve a better understanding of user intent, which can often be nuanced or ambiguous.

Vector space models work by representing documents and queries as vectors in dimensional space, enabling comparison and ranking.

Latent semantic analysis identifies the latent relationships between terms using a statistical approach to process associated words and phrases.

Natural language processing (NLP) is a machine-learning technique that allows search engines to better comprehend the context and semantics of human language used in search.

Key factors in search relevance include signals ranging from keyword density to engagement metrics. Search engines crawl for content and links, collect and index the data, and assign relevance to each page, scoring, sorting, and ranking results based on a vast pool of criteria. They then must dissect the contexts of keywords and speech to deliver what the searcher is actually looking for.

Key factors in search relevance include:

• Text analysis and keyword relevance
  The process of matching query terms to the content of articles, pages, documents, and product listings is critical for establishing relevance. The task is complicated by the complexities of natural language phrasing, synonyms, word variants, misspellings, regional spelling variants, and phonetic spellings, to name just a few.
• Content quality
  Authoritative content tends to rank higher based on its assessment of credibility and accuracy. Freshness also plays a role, with more recent content considered more relevant for certain queries.
• Term weighting
  In determining which keywords should take priority, numerical values are assigned to each term, and those values are crucial to the accuracy of results.
• User intent
  Algorithms help determine a user's true goal, addressing both the explicit search query and the semantic context behind it.
• Personalization and localization
  Contextual relevance allows search engines to tailor results based on user-specific factors including geographical location, language, device type, and browsing and search history.
• User engagement metrics
  Clickthrough rates help measure user engagement. Searches with no results and/or searches with no clicks provide insights into user satisfaction with search results.

Search relevance is important for user satisfaction. When users can quickly and efficiently find the information they're looking for, it greatly improves their experience. Depending on your business, that can mean more shoppers converted into customers, more casual browsers turned into regular readers or consumers, or more team members enabled to complete assignments and make important decisions.

43% of users¹ go directly to the search bar when navigating a website and are as much as three times more likely to convert than non-searchers. For businesses, search is often the first point of interaction with potential clients or customers. Thus, it’s vital to building user trust, engagement, and revenue. Internally, the streamlined delivery of relevant information saves time and effort.

Poor search relevance leads to frustration and users potentially abandoning a site to pursue their inquiry on a different channel — never to return again. For online retailers, that means losing shoppers who can’t find the products they’re looking for. Relevant results translate to higher conversion rates and increased revenue. Online retailers can also benefit from search-driven ecommerce experiences that increase cart size like the "shoppers also bought" or "similar items" tabs.

For businesses that produce content, higher search relevance means increased visibility and wider audience reach. For researchers, doctors, students, and professionals, accurate results lead to better work.

Every searcher’s needs and intent are different. Accurately or systematically measuring relevance is challenging. Industry benchmarks give some guidance, such as the Benchmarking-IR (BEIR), which is a pre-trained transformer that evaluates information retrieval methods on a ranking system. The MS-MARCO (Microsoft Machine Reading Comprehension), for example, is a part of the BEIR benchmark with a question-answering data set to evaluate the efficacy of search. While this provides an indication of search relevance, there is no absolute scoring method.

One way to gain insight into how effectively your search is performing is to look for trends in user behavior — and constantly seek incremental ways to improve them.

Search effectiveness can be measured by the amount of typing, retyping, and revising of a search term that’s required by a user to find the right information. Search efficiency is defined by the amount of overall effort a user puts in to locate desired information. Both offer a window into your overall search relevance. Search analytics can also offer insights into how often your users search, what they search for, and whether or not they find it.

Recall is the fraction of relevant records retrieved compared to the total number of relevant records. Precision is the ratio of the relevant results compared to the number of irrelevant and relevant records in total. While recall and precision are key measurements, they often require a degree of human evaluation and supervision to assess, which can be costly. Gathering user feedback through surveys or interactive buttons offers a less costly glimpse into how users perceive the relevance of search results.

Searcher behavior metrics offer indirect but implicit relevance judgments. One such valuable quantitative metric is the clickthrough rate (CTR), which gauges the percentage of users who click on a result after viewing it. Higher CTRs signify greater relevance. Dwell time, or the duration a user spends on a page after clicking a search results link, is another key indicator, with longer dwell times associated with more relevant, quality content. On the other hand, the percentage of users who leave a page without interacting, known as the bounce rate, can indicate that content did not meet expectations.

Enhancing search relevance is an ongoing process. In addition to adopting the latest machine learning algorithms, natural language processing capabilities, and entity recognition, here are a few techniques for improving search relevance over time:

• Expand query comprehension
  To broaden the scope of your results, expand the query comprehension for related terms connected by definition and scope, or keyword synonyms, which are words or phrases with the same or similar meaning as your search term. Also expand to include partial match results, which match some parts of a query if not all of it. Automatic language detection can also be used to suggest similar text to previous searches.
• Improve typo tolerance
  Improved typo tolerance means your users can still get the desired results despite misspelled words. Typo tolerance makes search more frictionless because the user doesn’t have to retype their query. Instead of depending on an exact match, add more tolerance for spelling mistakes and typos by improving your autocorrect and spellcheck capabilities. You can also suggest alternatives and correct search terms with a "Did you mean?" feature.
• Incorporate facets and filters
  Facets, also known as smart filters, help users narrow down their search. Facets and filters are most useful on sites with large amounts of information. Automatically detect filters or enable users to narrow down search results based on relevant attributes like location, topic, price, category, and date.
• Embrace personalization
  Customizing results based on user preferences, location, search and purchase history, and behavior leads to enhanced relevance. Tailor results to your user by ranking the most relevant searches higher. Personalization is also a key part of predictive search, anticipating your user's future needs with their past searches.
• Harness user feedback
  Encouraging users to provide feedback on result quality helps search engines learn and improve. User feedback is the best measure of how relevant a search actually is. Utilizing feedback can give your search engine continuous improvement.
• Optimize your UX
  Clean up your site by removing outdated pages, duplicate content, unused keywords or tags, and broken links. Streamline your UX design, like making the search bar easy to find and intuitive to use. Structure your site to ensure search engines can crawl and index your content more effectively.
• Prioritize high-value site areas
  To surface the most valuable to users, weight results from quality content areas of your site higher, including pages with the strongest engagement, authority, backlinks, and conversion rates.

Battling low-quality search results can be a frustrating process, and the solutions aren’t always transparent. There will always be ambiguity inherent in interpreting queries with multiple meanings. Deciphering true intent without much else to go on is an imperfect science. Truly relevant search is a cognitive labyrinth that even the most advanced AI has yet to solve.

To remain up to date, search engines must rapidly adapt to the ever-rising ocean of content generated every single second around the world — in a diverse and shifting landscape of languages, dialects, slang, and regional nuance. And though personalized search results contribute greatly to relevance, they can also create bubbles that limit exposure to different perspectives.

For organizations, optimizing search relevance can often feel mystifying. And the tiny window of time for delivering relevant search results to users always seems to be shrinking.

Currently, there are advanced search techniques that help make major strides in search relevance.

Semantic search interprets the meanings of the user’s query, which uses context to respond to their intent rather than the word-for-word search. Vector search is often used for semantic search, finding similar data using approximate nearest neighbor (ANN) algorithms to yield more relevant results. Hybrid search uses a combination of vector-enabled and keyword search methods.

As these search types evolve, search relevance metrics will also transform to account for their impact. Other technological innovations will also impact the future of search relevance.

In nearly every area of culture and enterprise, AI-powered search engines promise to vastly improve search relevance with far less manual work from organizations. AI will enable capabilities like automatic synonym detection, ranking algorithms that self-learn from metrics and feedback, and real-time user activity tracking that can anticipate what a customer wants before they even think to ask for it.

In ecommerce, smart multilingual search bars that can process visual information from images and video will revolutionize shopping, with the ability to suggest the exact product, in the exact color, brand, and size, that a customer actually wants — eliminating hours of scrolling. Accurate voice search will proliferate. And with increasing accuracy, generative AI will produce hyper-relevant contextual answers to queries, with specific, far more helpful recommendations, instead of a list of results. Machine learning is already opening up a wide new frontier in search relevance.

Deep dive into 2024 technical search trends. Watch this webinar to learn best practices, emerging methodologies, and how the top trends are influencing developers in 2024.

The Elasticsearch Relevance Engine helps users find exactly what they need to provide highly relevant, personalized, AI-powered search experiences right out of the box with state-of-the-art machine learning.

Our comprehensive drag-and-drop relevance tuning tools also enable you to analyze, optimize, and personalize to deliver even more precise results. With adaptive relevance, you can give your highest-performing results top billing with automated, data-driven recommendations based on search analytics. You can also improve semantic relevance with generative AI, vector search, support for NLP transformer models, and third-party model management.

• AI search algorithms explained
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• Embracing the future of search relevance

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¹ From "Must Have E-Commerce Features," Forrester, Sucharita Kodali and Scott Compton, February 18th, 2022, https://www.forrester.com/report/MustHave-eCommerce-Features/RES89561