SEARCH ENGINES & SEARCHING

There is one topic that gives even the toughest search engineers a headache, multi-word synonyms. There are some ways to sort of get a solution, one of them is a nice tool called Querqy. Querqy is created by René Kriegler....

You can help save animals with every search you do! Cheerie is a new search engine that donates a portion of revenue from each search made. Try Cheerie & help us save them all.

What gets measured gets managed - Peter Drucker

Different forms of a word often communicate essentially the same meaning. For example, there’s probably no difference in intent between a…

SearXNG — a privacy-respecting, open metasearch engine

In my first couple of posts, I described the need for human- and metrics-centered approaches to relevance improvement and how we can…

In the previous post, we looked at measuring searcher behavior in order to evaluate search engine performance. Measuring searcher behavior…

Startpagina geeft je al meer dan 25 jaar een overzicht van handmatig geselecteerde links van relevante en betrouwbare Nederlandse websites.

Ellpedia is a private metasearch engine based on Google and Bing.

A “tell-all” account of improving our academic search engine.

In our last installment, we learned about the important of metrics and how the choice of metrics is so important to building, maintaining…

Kvasir gir deg raskt en oversikt - en smartere måte å lete på!

Search result snippets, also known as query-biased summaries, are the additional context included with each result on the search results…

When I enter a store, my intent is “I want to buy X,” not “Show me products that satisfy characteristics X, Y, and Z.”

Haystack is the no-holds-barred conference for practitioners working on search, matching, and relevance.

why the smartSuggest module might matter to you Guide the user during the search formulation process to facilitate accurate data entry, encourage exploratory search and boost product discovery. htt...

Entity resolution for Elasticsearch. Contribute to zentity-io/zentity development by creating an account on GitHub.

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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.

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