A metasearch engine (or search aggregator) 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.
Examples of metasearch engines include Skyscanner and Kayak.com, which aggregate search results of online travel agencies and provider websites and Searx, a free and open-source search engine which aggregates results from internet search engines.
Meta search engines offer a convenient way for users to access information from multiple sources in a single search, catering to various needs such as research, comparison, and privacy preferences. These tools are used by various individuals and groups for different purposes such as:
- Researchers and Students: Researchers and students often use meta search engines to get a comprehensive view of information available on the web. By pulling results from various search engines, they can access a wider range of sources.
- Comparison Shoppers: People looking to buy products or services often use meta search engines to compare prices and features across different platforms. This is common in industries such as travel, where users can compare prices for flights and hotels.
- Efficiency Seekers: Meta search engines can save time by eliminating the need to visit multiple search engines individually. Users can see results from various sources on one page, streamlining the search process.
- Privacy-Conscious Users: Some users prefer meta search engines for privacy reasons. By not relying solely on one search engine, they may reduce the amount of personal data collected and tracked by any single platform.
- Web Developers and SEO Professionals: Professionals in web development and SEO might use meta search engines to see how their websites rank across different search engines. It can also be a way to analyze competitors' online visibility.
- Cross-Platform Searches: Some meta search engines extend beyond traditional web search and include results from various sources like images, videos, news, etc., providing a more diverse range of information.
The first person to incorporate the idea of meta searching was Daniel Dreilinger of Colorado State University. He developed SearchSavvy, which let users search up to 20 different search engines and directories at once. Although fast, the search engine was restricted to simple searches and thus wasn't reliable. University of Washington student Eric Selberg released a more "updated" version called MetaCrawler. This search engine improved on SearchSavvy's accuracy by adding its own search syntax behind the scenes, and matching the syntax to that of the search engines it was probing. Metacrawler reduced the amount of search engines queried to 6, but although it produced more accurate results, it still wasn't considered as accurate as searching a query in an individual engine.
On May 20, 1996, HotBot, then owned by Wired, was a search engine with search results coming from the Inktomi and Direct Hit databases. It was known for its fast results and as a search engine with the ability to search within search results. Upon being bought by Lycos in 1998, development for the search engine staggered and its market share fell drastically. After going through a few alterations, HotBot was redesigned into a simplified search interface, with its features being incorporated into Lycos' website redesign.
A metasearch engine called Anvish was developed by Bo Shu and Subhash Kak in 1999; the search results were sorted using instantaneously trained neural networks. This was later incorporated into another metasearch engine called Solosearch.
In April 2005, Dogpile, then owned and operated by InfoSpace, Inc., collaborated with researchers from the University of Pittsburgh and Pennsylvania State University to measure the overlap and ranking differences of leading Web search engines in order to gauge the benefits of using a metasearch engine to search the web. Results found that from 10,316 random user-defined queries from Google, Yahoo!, and Ask Jeeves, only 3.2% of first page search results were the same across those search engines for a given query. Another study later that year using 12,570 random user-defined queries from Google, Yahoo!, MSN Search, and Ask Jeeves found that only 1.1% of first page search results were the same across those search engines for a given query.
By sending multiple queries to several other search engines this extends the coverage data of the topic and allows more information to be found. They use the indexes built by other search engines, aggregating and often post-processing results in unique ways. A metasearch engine has an advantage over a single search engine because more results can be retrieved with the same amount of exertion. It also reduces the work of users from having to individually type in searches from different engines to look for resources.
Metasearching is also a useful approach if the purpose of the user's search is to get an overview of the topic or to get quick answers. Instead of having to go through multiple search engines like Yahoo! or Google and comparing results, metasearch engines are able to quickly compile and combine results. They can do it either by listing results from each engine queried with no additional post-processing (Dogpile) or by analyzing the results and ranking them by their own rules (IxQuick, Metacrawler, and Vivismo).
A metasearch engine can also hide the searcher's IP address from the search engines queried thus providing privacy to the search.
Metasearch engines are not capable of parsing query forms or able to fully translate query syntax. The number of hyperlinks generated by metasearch engines are limited, and therefore do not provide the user with the complete results of a query.
The majority of metasearch engines do not provide over ten linked files from a single search engine, and generally do not interact with larger search engines for results. Pay per click links are prioritised and are normally displayed first.
Metasearching also gives the illusion that there is more coverage of the topic queried, particularly if the user is searching for popular or commonplace information. It's common to end with multiple identical results from the queried engines. It is also harder for users to search with advanced search syntax to be sent with the query, so results may not be as precise as when a user is using an advanced search interface at a specific engine. This results in many metasearch engines using simple searching.
A metasearch engine accepts a single search request from the user. This search request is then passed on to another search engine's database. A metasearch engine does not create a database of web pages but generates a Federated database system of data integration from multiple sources.
Since every search engine is unique and has different algorithms for generating ranked data, duplicates will therefore also be generated. To remove duplicates, a metasearch engine processes this data and applies its own algorithm. A revised list is produced as an output for the user. When a metasearch engine contacts other search engines, these search engines will respond in three ways:
- They will both cooperate and provide complete access to the interface for the metasearch engine, including private access to the index database, and will inform the metasearch engine of any changes made upon the index database;
- Search engines can behave in a non-cooperative manner whereby they will not deny or provide any access to interfaces;
- The search engine can be completely hostile and refuse the metasearch engine total access to their database and in serious circumstances, by seeking legal methods.
Architecture of ranking
Web pages that are highly ranked on many search engines are likely to be more relevant in providing useful information. However, all search engines have different ranking scores for each website and most of the time these scores are not the same. This is because search engines prioritise different criteria and methods for scoring, hence a website might appear highly ranked on one search engine and lowly ranked on another. This is a problem because Metasearch engines rely heavily on the consistency of this data to generate reliable accounts.
A metasearch engine uses the process of Fusion to filter data for more efficient results. The two main fusion methods used are: Collection Fusion and Data Fusion.
- Collection Fusion: also known as distributed retrieval, deals specifically with search engines that index unrelated data. To determine how valuable these sources are, Collection Fusion looks at the content and then ranks the data on how likely it is to provide relevant information in relation to the query. From what is generated, Collection Fusion is able to pick out the best resources from the rank. These chosen resources are then merged into a list.
- Data Fusion: deals with information retrieved from search engines that indexes common data sets. The process is very similar. The initial rank scores of data are merged into a single list, after which the original ranks of each of these documents are analysed. Data with high scores indicate a high level of relevancy to a particular query and are therefore selected. To produce a list, the scores must be normalized using algorithms such as CombSum. This is because search engines adopt different policies of algorithms resulting in the score produced being incomparable.
Spamdexing is the deliberate manipulation of search engine indexes. It uses a number of methods to manipulate the relevance or prominence of resources indexed in a manner unaligned with the intention of the indexing system. Spamdexing can be very distressing for users and problematic for search engines because the return contents of searches have poor precision. This will eventually result in the search engine becoming unreliable and not dependable for the user. To tackle Spamdexing, search robot algorithms are made more complex and are changed almost every day to eliminate the problem.
It is a major problem for metasearch engines because it tampers with the Web crawler's indexing criteria, which are heavily relied upon to format ranking lists. Spamdexing manipulates the natural ranking system of a search engine, and places websites higher on the ranking list than they would naturally be placed. There are three primary methods used to achieve this:
Content spam are the techniques that alter the logical view that a search engine has over the page's contents. Techniques include:
- Keyword Stuffing - Calculated placements of keywords within a page to raise the keyword count, variety, and density of the page
- Hidden/Invisible Text - Unrelated text disguised by making it the same color as the background, using a tiny font size, or hiding it within the HTML code
- Meta-tag Stuffing - Repeating keywords in meta tags and/or using keywords unrelated to the site's content
- Doorway Pages - Low quality webpages with little content, but relatable keywords or phrases
- Scraper Sites - Programs that allow websites to copy content from other websites and create content for a website
- Article Spinning - Rewriting existing articles as opposed to copying content from other sites
- Machine Translation - Uses machine translation to rewrite content in several different languages, resulting in illegible text
Link spam are links between pages present for reasons other than merit. Techniques include:
- Link-building Software - Automating the search engine optimization (SEO) process
- Link Farms - Pages that reference each other (also known as mutual admiration societies)
- Hidden Links - Placing hyperlinks where visitors won't or can't see them
- Sybil Attack - Forging of multiple identities for malicious intent
- Spam Blogs - Blogs created solely for commercial promotion and the passage of link authority to target sites
- Page Hijacking - Creating a copy of a popular website with similar content, but redirects web surfers to unrelated or even malicious websites
- Buying Expired Domains - Buying expiring domains and replacing pages with links to unrelated websites
- Cookie Stuffing - Placing an affiliate tracking cookie on a website visitor's computer without their knowledge
- Forum Spam - Websites that can be edited by users to insert links to spam sites
This is an SEO technique in which different materials and information are sent to the web crawler and to the web browser. It is commonly used as a spamdexing technique because it can trick search engines into either visiting a site that is substantially different from the search engine description or giving a certain site a higher ranking.
- Federated search
- List of metasearch engines
- Search aggregator
- Search engine optimization
- Berger, Sandy (2005). "Sandy Berger's Great Age Guide to the Internet" (Document). Que Publishing.ISBN 0-7897-3442-7
- "Architecture of a Metasearch Engine that Supports User Information Needs". 1999.
- Ride, Onion (2021). "How search Engine work". onionride.
- Lawrence, Stephen R.; Lee Giles, C. (October 10, 1997). "Patent US6999959 - Meta search engine" – via Google Books.
- Voorhees, Ellen M.; Gupta, Narendra; Johnson-Laird, Ben (April 2000). "The collection fusion problem".
- "The Meta-search — Search Engine History". Archived from the original on 2020-01-30. Retrieved 2014-12-02.
- "Search engine rankings on HotBot: a brief history of the HotBot search engine".
- Shu, Bo; Kak, Subhash (1999). "A neural network based intelligent metasearch engine". Information Sciences. 120 (4): 1–11. CiteSeerX 10.1.1.84.6837. doi:10.1016/S0020-0255(99)00062-6.
- Kak, Subhash (November 1999). "Better Web searches and prediction with instantaneously trained neural networks" (PDF). IEEE Intelligent Systems.
- "ABOUT US – Our history".
- Spink, Amanda; Jansen, Bernard J.; Kathuria, Vinish; Koshman, Sherry (2006). "Overlap among major web search engines" (PDF). Emerald.
- "Department of Informatics". University of Fribourg.
- "Intelligence Exploitation of the Internet" (PDF). 2002.
- HENNEGAR, ANNE (16 September 2009). "Metasearch Engines Expands your Horizon".
- MENG, WEIYI (May 5, 2008). "Metasearch Engines" (PDF).
- Selberg, Erik; Etzioni, Oren (1997). "The MetaCrawler architecture for resource aggregation on the Web". IEEE expert. pp. 11–14.
- Manoj, M; Jacob, Elizabeth (July 2013). "Design and Development of a Programmable Meta Search Engine" (PDF). Foundation of Computer Science. pp. 6–11.
- Manoj, M.; Jacob, Elizabeth (October 2008). "Information retrieval on Internet using meta-search engines: A review" (PDF). Council of Scientific and Industrial Research.
- Wu, Shengli; Crestani, Fabio; Bi, Yaxin (2006). "Evaluating Score Normalization Methods in Data Fusion". Information Retrieval Technology. Lecture Notes in Computer Science. Vol. 4182. pp. 642–648. CiteSeerX 10.1.1.103.295. doi:10.1007/11880592_57. ISBN 978-3-540-45780-0.
- Manmatha, R.; Sever, H. (2014). "A Formal Approach to Score Normalization for Meta-search" (PDF). Archived from the original (PDF) on 2019-09-30. Retrieved 2014-10-27.
- Najork, Marc (2014). "Web Spam Detection". Microsoft.
- Vandendriessche, Gerrit (February 2009). "A few legal comments on spamdexing".
- Wang, Yi-Min; Ma, Ming; Niu, Yuan; Chen, Hao (May 8, 2007). "Connecting Web Spammers with Advertisers" (PDF).