OSINT

Worldwide search engine, use BUSCApique.com to find what you are looking for, search the world or an especific country

TLDR; When searching across most social networks, your direct contacts will be ranked higher than other users. Let’s take a look at the search of Linkedin and see if we can replicate something similar with Elasticsearch. Note, that this post only deals with autocomplete/search as you type suggestions and will not deep dive into the search results once a search has been sent, resulting in a search result page. Let’s look at Linkedin So let’s check out this search response.

O UOL Busca é um serviço de pesquisa que proporciona o fácil acesso à informação.

Most products have multiple attributes that they could be filtered by during a search experience. Attributes like colour, brand, product…

Datafari open source enterprise search

The previous post focused on how to determine the best autocomplete suggestions based on query probability and query performance. In this…

Search queries are made up of characters. It’s easy to take characters for granted; indeed, people who have never implemented a search…

Over the decades, information retrieval has been dominated by classical methods such as BM25. These lexical models are simple and effective yet vulnerable to vocabulary mismatch. With the introduction of pre-trained language models such as BERT and its relatives, deep retrieval models have achieved superior performance with their strong ability to capture semantic relationships. The downside is that training these deep models is computationally expensive, and suitable datasets are not always available for fine-tuning toward the target domain. While deep retrieval models work best on domains close to what they have been trained on, lexical models are comparatively robust across datasets and domains. This suggests that lexical and deep models can complement each other, retrieving different sets of relevant results. But how can these results effectively be combined? And can we learn something from language models to learn new indexing methods? This talk will delve into both these approaches and exemplify when they work well and not so well. We will take a closer look at different strategies to combine them to get the best of both, even in zero-shot cases where we don't have enough data to fine-tune the deep model. The Search track is presented by OpenSource Connections

Introduction

How much money are you losing or gaining, depending on a site-search query? You need to be able to know if search is working, and you can with these KPIs!

So far, we’ve focused on understanding searchers based entirely on the words they type into the search box. But search doesn’t occur in a…

When is the right time to move to a machine learned search ranker from a simpler score-based one

Does anyone else remember the Segway? Segway was billed as the most revolutionary transportation innovation, well, ever. We would use it instead of cars; instead of biking and walking....

In 1964, information science pioneer William Goffman said:

What are the key roles in a Search Team and how can you find people to fill them both from within and without your organisation?

Let’s explore this key bias in search systems towards the old algorithm and how to overcome it!

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In the previous post on query scoping, we discussed query tagging as a special case of named-entity recognition (NER). In this post, we’ll…

We will outline all the things you need to know about measuring the quality and effectiveness of your store’s site-search.

A well-tuned search experience keeps your users and customers coming back. A poor experience can send them to the wrong pages, or worse, your competitors. Learn how you can analyze search interaction ...

Now that we can handle characters, let’s move on to words.

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....

Efficient topic modelling in Python

Text Mining — Extracting Synonyms and Antonyms

In English Language, people generally type the queries which are separated by space, but sometimes and somehow this space is found to be…

Search + Merchandising = Searchandising. Learn what searchandising is and how it can increase sales in ecommerce stores with our practical hacks.

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The most immediate context for a search query is a search session, a sequence of activities the searcher performs in order to pursue an…