SwiftypeSwiftype is a search and index company based in San Francisco, California, that provides search software for organizations, websites, and computer programs. Notable customers include AT&T, Dr. Pepper, Hubspot and TechCrunch. Swiftype was founded in 2012 by Matt Riley and Quin Hoxie. The company participated in Y Combinator’s incubator program and received investment from a number of prominent sources. Their site search uses semantic understanding of queries to differentiate the meaning of words based on their use.
SpamdexingSpamdexing (also known as search engine spam, search engine poisoning, black-hat search engine optimization, search spam or web spam) is the deliberate manipulation of search engine indexes. It involves a number of methods, such as link building and repeating unrelated phrases, to manipulate the relevance or prominence of resources indexed in a manner inconsistent with the purpose of the indexing system.
ForbesForbes (fɔrbz) is an American business magazine founded in 1917 and owned by the Hong Kong-based investment group Integrated Whale Media Investments since 2014. Its chairperson and editor-in-chief is Steve Forbes, and its CEO is Mike Federle. Published eight times a year, it features articles on finance, industry, investing, and marketing topics. Forbes also reports on related subjects such as technology, communications, science, politics, and law. It is based in Jersey City, New Jersey.
Pay-per-clickPay-per-click (PPC) is an internet advertising model used to drive traffic to websites, in which an advertiser pays a publisher (typically a search engine, website owner, or a network of websites) when the ad is clicked. Pay-per-click is usually associated with first-tier search engines (such as Google Ads, Amazon Advertising, and Microsoft Advertising formerly Bing Ads). With search engines, advertisers typically bid on keyword phrases relevant to their target market and pay when ads (text-based search ads or shopping ads that are a combination of images and text) are clicked.
Search engine marketingSearch engine marketing (SEM) is a form of Internet marketing that involves the promotion of websites by increasing their visibility in search engine results pages (SERPs) primarily through paid advertising. SEM may incorporate search engine optimization (SEO), which adjusts or rewrites website content and site architecture to achieve a higher ranking in search engine results pages to enhance pay per click (PPC) listings and increase the Call to action (CTA) on the website. In 2007, U.S. advertisers spent US $24.
Full-text searchIn text retrieval, full-text search refers to techniques for searching a single computer-stored document or a collection in a full-text database. Full-text search is distinguished from searches based on metadata or on parts of the original texts represented in databases (such as titles, abstracts, selected sections, or bibliographical references). In a full-text search, a search engine examines all of the words in every stored document as it tries to match search criteria (for example, text specified by a user).
Compound-term processingCompound-term processing, in information-retrieval, is search result matching on the basis of compound terms. Compound terms are built by combining two or more simple terms; for example, "triple" is a single word term, but "triple heart bypass" is a compound term. Compound-term processing is a new approach to an old problem: how can one improve the relevance of search results while maintaining ease of use? Using this technique, a search for survival rates following a triple heart bypass in elderly people will locate documents about this topic even if this precise phrase is not contained in any document.
StemmingIn linguistic morphology and information retrieval, stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form—generally a written word form. The stem need not be identical to the morphological root of the word; it is usually sufficient that related words map to the same stem, even if this stem is not in itself a valid root. Algorithms for stemming have been studied in computer science since the 1960s.
Learning to rankLearning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data consists of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment (e.g. "relevant" or "not relevant") for each item.
