Meta Search Engines: Go-To Guide for 2024

What are Meta Search Engines?

Simply put, a meta search engine is a type of search engine that aggregates results from several search engines, presenting users with a consolidated list of search results from multiple sources.

Unlike traditional search engines, which crawl the Web directly to index pages, meta search engines send user queries to multiple search engines in real-time. They then aggregate and filter the responses to display the most relevant results.

The term “meta” indicates that these search engines operate at a higher level, working across multiple other search engines rather than within their own index.

Is Google a Metasearch Engine?

Google is not a metasearch engine.

Unlike metasearch engines, which compile results from different search engines, Google operates as a standalone search engine, using its own custom and mysterious algorithms to produce search results.

What Does a Meta Search Engine Do?

A meta search engine works by querying several major search engines simultaneously and then aggregating the results into a single list for the user.

A metasearch engine processes a single query from the user and sends it to the databases of multiple search engines. Unlike traditional search engines, it doesn’t maintain its own database of Web pages, but instead creates a federated database system that integrates data from multiple sources.

To better visualize this, let’s look at one of the most popular applications of metasearch engines: the travel industry.

Here, meta-search engines are often used by consumers to compare options and prices across different booking platforms and OTAs.

Kayak is a prime example of a metasearch engine for travel services such as flights, hotels, car rentals, and vacation packages.

When a user searches for a flight, Kayak queries multiple travel booking sites, such as Expedia and Priceline, as well as airline websites. It then aggregates the results, allowing users to compare prices and schedules in a single interface.

This approach allows consumers to efficiently find the best deals and options without having to visit each site individually, providing a comprehensive view of available travel options to help users make informed decisions quickly.

How Do Meta Search Engines Work?

Retrieving information from multiple sources and presenting it in a usable and consistent way is no easy feat.

Metasearch engines operate through a series of sophisticated processes that ensure users receive the most accurate and timely results.

Let’s look at the core components of how these systems work.

Step 1: Data Collection

Similar to a traditional search engine, the first step in a metasearch engine’s operation is data collection, but unlike its single search counterparts, a metasearch engine does not maintain its own database of indexed pages.

Instead, they crawl and dynamically collect data from multiple sources.

This is done by sending user queries to multiple external data sources, such as other search engines or specialized APIs.

This process requires real-time communication with these sources to retrieve up-to-date information on prices, availability, or other relevant metrics.

The system must be able to handle multiple concurrent connections to ensure that it efficiently collects all the necessary data.

Step 2: Data Processing and Normalization

Once the data is retrieved, it is often in different formats and structures because each provider may use different terminologies, categorizations, and data schemas.

The metasearch engine normalizes this data and converts it to a standard format – the key aspect of all metasearch engines.

This process includes parsing the raw data, standardizing terms (e.g., converting all currencies to a standard denomination), and matching similar listings across providers (e.g., the same flight sold by different OTAs).

A metasearch engine uses the process of fusion to filter data for more efficient results. The two main fusion methods are Collection Fusion and Data Fusion.

• Collection Fusion: Also referred to as distributed retrieval, this approach specifically addresses search engines that index disparate data sources. It involves gathering and ranking results based on their relevance to the query from multiple sources independently, and then merging these ranked lists into a single unified list.
• Data Fusion: This approach is used when retrieving information from search engines that indexes common data sets. The process is actually quite similar. Firstly, all the data from various sources is first aggregated into a single, large dataset. The filtering and ranking process then occurs on this combined dataset, treating it as a single collection. To generate a usable list, the scores needs be normalized using algorithms such as CombSum.

Step 3: Real-Time Search and Ranking

When a user enters a search query (e.g., flight search from New York to London), the metasearch engine queries its aggregated database in real time and retrieves the relevant results.

These systems apply complex algorithms, such as the collection fusion and data fusion mentioned above, to rank these results based on various factors. These factors typically include

• Price Optimization: Prioritizing results based on cost-effectiveness, with adjustments for currency conversions, tax inclusions, and ancillary fees.
• User Preferences: Incorporating user-specific preferences, such as preferred airlines, hotel chains, or loyalty programs, which may be inferred from past interactions or explicitly stated preferences.
• Contextual Factors: Adjusting rankings based on contextual factors like booking lead time (e.g., same-day vs. future travel), travel class preferences, or stopover tolerances in flight searches.

The final ranking is often the result of multi-objective optimization, using weighted scoring systems for all of these factors (prioritizing price over convenience and vice versa, depending on the type of user).

The ranking process may also include machine learning algorithms to personalize results based on the user’s past behavior or profile.

Step 4: Dynamic Results Display and User Interface Optimization

Finally, once the ranking algorithm has determined the order of results, the metasearch engine dynamically generates the user interface (UI) to present those results.

This involves rendering a responsive, user-friendly display that can efficiently handle large data sets.

Results are often presented using interactive data visualization components such as sortable tables, price charts, or heat maps. These visual elements allow users to quickly compare key metrics (e.g., price, travel time, layovers) and customize the display according to their preferences.

Sorting mechanisms can include client-side sorting, where the browser handles the reordering based on user-selected criteria, and server-side sorting, where a new request is made to the server to retrieve a reordered dataset based on a different ranking criteria.

The key takeaway is that metasearch UIs tend to be much more complicated than single search engines like Google.

When designing and deploying such systems, companies must pay close attention to the user experience and work in iterations to improve navigation and information retrieval for end users.

How Different Industries Benefit From Metasearch Engines?

Meta search engines can be used in a wide range of industries, especially those where comparison shopping, data aggregation, and multiple sources of information are valuable to consumers.

Some key industries where meta search engines are commonly used include:

1. Travel: As mentioned earlier, in the travel industry, travel meta search engines aggregate data from various booking platforms, allowing users to compare prices for flights, hotels, and rental cars.
2. Real Estate: Websites like Zillow or Realtor often function as meta search engines for real estate, aggregating property listings from various real estate databases, MLS (Multiple Listing Services), and brokerage websites to offer a comprehensive overview of available properties.
3. E-commerce: Many metasearch engines compare prices and product availability across various online retailers, assisting consumers in finding the best deals on a wide range of products.
4. Job Search: Meta search engines like Indeed or SimplyHired gather job postings from company websites, job boards, and staffing agencies, simplifying the job search process by allowing job seekers to find opportunities from multiple sources with a single search engine.

Top Meta Search Engines Comparison

Given how difficult it is to build a custom meta search engine, and how many moving parts the whole process involves, it’s good to look at some of the top meta search engines on the market today to see what functionality they offer.

Some of the most popular metasearch engines include:

• Kayak: A leading travel meta search engine that aggregates and compares prices for flights, hotels, car rentals, and vacation packages from multiple sources, offering robust filtering and personalized search options.
• Skyscanner: Known for its powerful flight search capabilities, Skyscanner helps users find the best deals on flights, hotels, and car rentals, with features for flexible date and destination searches.
• Google Flights: Google’s flight search engine provides real-time flight information, price trends, and alternative routes, integrating seamlessly with other Google services for a streamlined user experience.
• Pricerunner: A meta search engine focused on product comparisons, Pricerunner aggregates prices from various online retailers, helping users find the best deals on electronics, home goods, and more.
• Indeed: A job search meta engine that aggregates job listings from thousands of websites, including company career pages, job boards, and newspapers, offering tools for job seekers to filter and apply to positions.
• Dogpile: It is the first meta search engine that combines results from major search engines like Google, Bing, and Yahoo, providing users with a comprehensive set of search results across the web.

Here is also a brief overview of these popular meta search engines and their interesting features:

And here are the main pros and cons of every metasearch solution so that you can have a complete overview:

Thank you for taking the time to read our blog post!