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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. [1] [2] Metasearch engines take input from a user and immediately query search engines [3] for results. Sufficient data is gathered, ranked, and presented to the users.
A review aggregator is a system that collects reviews and ratings of products and services, such as films, books, video games, music, software, hardware, or cars. This system then stores the reviews to be used for supporting a website where users can view the reviews, sells information to third parties about consumer tendencies, and creates databases for companies to learn about their actual ...
A search aggregator is a type of metasearch engine which gathers results from multiple search engines simultaneously, typically through RSS search results. It combines user specified search feeds (parameterized RSS feeds which return search results) to give the user the same level of control over content as a general aggregator .
Review aggregators are websites that collect film reviews and reflect overviews of critical reception by providing a score for a film based on the reviews. Some review aggregation websites, such as Rotten Tomatoes and Metacritic, are considered reliable sources, but information from them should be used in proper context and have some ...
Aggregator websites or services that aggregates content from various sources, types include: Subcategories This category has the following 5 subcategories, out of 5 total.
Depending on the particular business model of the comparison shopping site, retailers either pay a flat fee to be included on the site, pay a fee each time a user clicks through to the retailer web site, or pay every time a user completes a specified action—for example, when they buy something or register with their e-mail address. Comparison ...
User Tower: Encodes user-specific features, such as interaction history or demographic data. Item Tower : Encodes item-specific features, such as metadata or content embeddings. The outputs of the two towers are fixed-length embeddings that represent users and items in a shared vector space.
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