GenieKnows

Last updated
GenieKnows
Company type Private
Industry Internet
Founded Halifax, Canada (1999)
FounderRami Hamodah & John Manning
Headquarters,
Products Search Engine
Revenue US$8,400,000 (2007), Increase2.svg 19% from 2006
Number of employees
5 (2012)
Parent GenieKnows Inc.
Website www.genieknows.com

GenieKnows Inc. was a privately owned vertical search engine company based in Halifax, Nova Scotia. It was started by Rami Hamodah who also started SwiftlyLabs.com [1] and Salesboom.com. [2] Like many internet search engines, its revenue model centers on an online advertising platform and B2B transactions. It focuses on a set of search markets, or verticals, including health search, video games search, and local business directory search. [3]

Contents

Technologies

In 2005, GenieKnows entered the search engine market with a local business directory search engine. [4] Targeting only the United States in its beta release, the local search engine is similar to Google Maps but uses the proprietary GeoRank algorithm to associate potentially uncategorized web pages containing addresses with businesses listed in an internet Yellow Pages directory by extracting addresses and geocoding these to identify geographic coordinates for which it associates the web page. [5]

On February 29, 2008, GenieKnows Local was launched as a completely revised local search engine extending beyond the 100 most populous US cities covered in its beta release. The local search engine utilizes processed municipal business data, road network data, national park data, and geocoding technology to provide localized search results ranked according to a business's relevance to a user's web query. As of the February 2008 release, the engine covers over 90% of Canadian and US municipalities with populations above 1000 residents.

According to SEO and search marketing commentator Jim Hedger, GenieKnows' strongest, most unusual and important product is its local search engine. [6] [7]

Vertical Search Engine

GenieKnows entered the vertical search market in 2006 with a vertical search engine for video games-related web pages and another for health-related web pages.

Web pages often describe or discuss a particular topic. In information retrieval and machine learning literature, classification algorithms have been used to automatically identify the subject matter of a web page. GenieKnows uses such algorithms as a focused crawler to download web pages, identify pages that are on topic with the vertical, then index and save those pages.

The result is a search engine that contains only web pages that are on a given topic, tailoring to a niche market of web users who have an interest in a given topic, so all pages returned for a query will be on topic with the vertical being used. For revenue generation, the engine displays advertisements beside the search results from a network of advertisers where it receives pay per click. GenieKnows is collaborating with Yahoo! to display targeted, contextualized advertisements to a targeted market of users. [8] [9]

In February 2008, GenieKnows added online community functionality to its vertical search engine. Users sharing interest in a topic can communicate and contribute content to the site in a manner similar, but on a smaller scale, to those of Facebook, and Yahoo.

Company

Accomplishments

Philanthropy

GenieKnows has donated resources and money to many organizations including:

Related Research Articles

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Search engine optimization (SEO) is the process of improving the quality and quantity of website traffic to a website or a web page from search engines. SEO targets unpaid traffic rather than direct traffic or paid traffic. Unpaid traffic may originate from different kinds of searches, including image search, video search, academic search, news search, and industry-specific vertical search engines.

Cross-language information retrieval (CLIR) is a subfield of information retrieval dealing with retrieving information written in a language different from the language of the user's query. The term "cross-language information retrieval" has many synonyms, of which the following are perhaps the most frequent: cross-lingual information retrieval, translingual information retrieval, multilingual information retrieval. The term "multilingual information retrieval" refers more generally both to technology for retrieval of multilingual collections and to technology which has been moved to handle material in one language to another. The term Multilingual Information Retrieval (MLIR) involves the study of systems that accept queries for information in various languages and return objects of various languages, translated into the user's language. Cross-language information retrieval refers more specifically to the use case where users formulate their information need in one language and the system retrieves relevant documents in another. To do so, most CLIR systems use various translation techniques. CLIR techniques can be classified into different categories based on different translation resources:

<span class="mw-page-title-main">Metasearch engine</span> Online information retrieval tool

A metasearch engine is an online information retrieval tool that uses the data of a web search engine to produce its own results. Metasearch engines take input from a user and immediately query search engines for results. Sufficient data is gathered, ranked, and presented to the users.

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<span class="mw-page-title-main">Ricardo Baeza-Yates</span>

Ricardo A. Baeza-Yates is a Chilean-Catalan-American computer scientist that currently is the Director of Research of the Institute for Experiential AI at Northeastern University in the Silicon Valley campus. He is also part-time professor at Universitat Pompeu Fabra in Barcelona and Universidad de Chile in Santiago. He is an expert member of the Global Partnership on Artificial Intelligence, a member of the Association for Computing Machinery's US Technology Policy Committee as well as IEEE's Ethics Committee.

Local search is the use of specialized Internet search engines that allow users to submit geographically constrained searches against a structured database of local business listings. Typical local search queries include not only information about "what" the site visitor is searching for but also "where" information, such as a street address, city name, postal code, or geographic coordinates like latitude and longitude. Examples of local searches include "Hong Kong hotels", "Manhattan restaurants", and "Dublin car rental". Local searches exhibit explicit or implicit local intent. A search that includes a location modifier, such as "Bellevue, WA" or "14th arrondissement", is an explicit local search. A search that references a product or service that is typically consumed locally, such as "restaurant" or "nail salon", is an implicit local search.

<span class="mw-page-title-main">Search engine</span> Software system for finding relevant information on the Web

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A Web query topic classification/categorization is a problem in information science. The task is to assign a Web search query to one or more predefined categories, based on its topics. The importance of query classification is underscored by many services provided by Web search. A direct application is to provide better search result pages for users with interests of different categories. For example, the users issuing a Web query "apple" might expect to see Web pages related to the fruit apple, or they may prefer to see products or news related to the computer company. Online advertisement services can rely on the query classification results to promote different products more accurately. Search result pages can be grouped according to the categories predicted by a query classification algorithm. However, the computation of query classification is non-trivial. Different from the document classification tasks, queries submitted by Web search users are usually short and ambiguous; also the meanings of the queries are evolving over time. Therefore, query topic classification is much more difficult than traditional document classification tasks.

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Local search engine optimization is similar to (national) SEO in that it is also a process affecting the visibility of a website or a web page in a web search engine's unpaid results often referred to as "natural", "organic", or "earned" results. In general, the higher ranked on the search results page and more frequently a site appears in the search results list, the more visitors it will receive from the search engine's users; these visitors can then be converted into customers. Local SEO, however, differs in that it is focused on optimizing a business's online presence so that its web pages will be displayed by search engines when users enter local searches for its products or services. Ranking for local search involves a similar process to general SEO but includes some specific elements to rank a business for local search.

References

  1. "Home". swiftlylabs.com.
  2. "Home". salesboom.com.
  3. Official GenieKnows Website
  4. "GenieKnows local search reviewed on Search Engine Watch". Archived from the original on 2008-04-13. Retrieved 2008-02-20.
  5. Abou-Assaleh, Tony; Gao, Weizheng (2007). "Geographic ranking for a local search engine". Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07. p. 911. doi:10.1145/1277741.1277979. ISBN   9781595935977. S2CID   11021442.
  6. ""GenieKnows Its Verticals", Search Engine WAtch (2008)". Archived from the original on 2008-08-09. Retrieved 2008-08-11.
  7. "Baltimore SEO". Sunday, 18 April 2021
  8. Yahoo! Joins GenieKnows.com, in Sponsored Search Collaboration
  9. "Business Directory" . Retrieved 19 July 2023.
  10. "GenieKnows Ranks 10th on Progress Magazine's Best Places to Work Survey 2007". Archived from the original on 2009-02-14.
  11. "Progress Magazine, Best Places to Work Survey". Archived from the original on 2008-10-15. Retrieved 2008-08-11.
  12. "GenieKnows Named on Fastest Growing Company List for 2008". Archived from the original on 2009-04-04.
  13. "Branham Group Inc., Branham 300". Archived from the original on 2008-09-17.
  14. "ACOA Website: Atlantic Innovation Fund". 13 January 2022.