ABOUT SEARCHING AND SEARCH PRACTICIES

How to use synonyms and synonym filters in Elasticsearch. Synonyms are a powerful tool for increasing the recall of your search system, but there are many subtleties that are important to know and exp...

Similarity ranking (relevancy) in Elasticsearch relates directly to the amount of shards in your index. Learn more about how shards and relevancy are related, as well what you should consider when tun...

BM25 is the default similarity ranking (relevancy) algorithm in Elasticsearch. Learn more about how it works by digging into the equation and exploring the concepts behind its variables....

ElasticON shows you how to get the most relevant search, observability, and security results at unprecedented speed and scale with enterprise solutions — powered by Elasticsearch Platform and AI.

Users are rating items on your website. How do you know what the highest-rated items are?

In the past decade, the incredible progress in word embeddings and deep learning has fueled an interest in neural information retrieval. An increasing number of folks believe that it’s time to retire the traditional inverted indexes (aka posting lists) that search engines use for retrieval and ranki

Search can be computationally intensive. Indeed, search has been the driving force behind many advances in computational efficiency, from MapReduce for distributed indexing to approximate nearest-neighbor methods.

Last week Google announced that they were rolling out a big improvement to Google search by making use of BERT for improved query understanding, which in turn is aimed at producing better search results. In short, the goal is to be able to answer natural language questions better than before.

Information Retrieval (IR) systems are a vital component in the core of successful modern web platforms. The main goal of IR systems is to provide a communication layer that enables customers to establish a retrieval dialogue with underlying data.

I’m a big fan of opportunity analysis. I’ve seen far too many organizations underinvest in opportunity analysis and thus waste enormous — and avoidable — effort on experiments that produce negative or neutral results.

Search developers tend to focus most of their efforts on the first page of results. As a result, they prioritize investment in ranking models, with the goal of improving quality and business metrics, such as relevance and conversion.

We at searchhub thrive to make existing search engines better understand humans and deliver exceptional search experiences. Until a couple of months ago we were mainly focusing on rewriting the user queries into the best possible search engine queries helping our customers to deliver better results

Build search engines powered by the latest machine learning techniques and large language models. AI-Powered Search shows you how to build cutting-edge search engines that continuously learn from both your users and your content and drive more domain-aware and intelligent search. Inside you’ll learn modern, data-science-driven search techniques like: Semantic search using dense vector embeddings from foundation models Retrieval augmented generation Question answering and summarization combining search and LLMs Fine-tuning transformer-based LLMs Personalized search based on user signals and vector embeddings Collecting user behavioral signals and building signals boosting models Semantic knowledge graphs for domain-specific learning Implementing machine-learned ranking models (learning to rank) Building click models to automate machine-learned ranking Generative search, hybrid search, and the search frontier Today’s search engines are expected to be smart, understanding the nuances of natural language queries, as well as each user’s preferences and context. This book empowers you to build search engines that take advantage of user interactions and the hidden semantic relationships in your content to automatically deliver better, more relevant search experiences. You’ll even learn how to integrate large language models (LLMs) like GPT and other foundation models to massively accelerate the capabilities of your search technology.

Deep Learning for Search teaches you how to improve the effectiveness of your search by implementing neural network-based techniques. By the time you're finished with the book, you'll be ready to build amazing search engines that deliver the results your users need and that get better as time goes on!

Relevant Search demystifies relevance work. Using Elasticsearch, it teaches you how to return engaging search results to your users, helping you understand and leverage the internals of Lucene-based search engines.

Now that people can easily find answers to their questions directly on results pages, content creators must rethink their role in providing information to their users.

Eyetracking studies show that users sometimes look at only a single result on a search-results page because that result is good enough for their needs.

Because today’s search-results pages have many possible complex layouts, users don’t always process search results sequentially. They distribute their attention more variably across the page than in the past.

Restricting search to a specific area of a website can provide better results, faster. But users overlook, misunderstand, and forget about the search scope.

Your website’s search engine can tell you what your web visitors want, how they look for it, and how well your content strategy meets their needs.

When users get 0 search results, there’s a high risk of site abandonment. Better design can turn this UX disaster into an opportunity for content discovery.

Useful search suggestions lead to relevant results and are visually distinct from the query text. If appropriate, they include scope, thumbnails, or categories.

Detailed records of searches underscore how much people reveal about themselves when they use search engines.

At OpenSource Connections we specialize in search & AI training and consulting. Our search relevance engineers are experts in Elasticsearch, Solr and OpenSearch.