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When another movie recommendation site, eachmovie.org, [5] closed in 1997, the researchers who built it publicly released the anonymous rating data they had collected for other researchers to use. The GroupLens Research team, led by Brent Dahlen and Jon Herlocker, used this data set to jumpstart a new movie recommendation site, which they chose ...
The Jinni service included semantic search, [1] a meaning-based approach to interpreting queries by identifying concepts within the content, rather than keywords. The search engine served as a video discovery tool focusing on user tastes, including mood, plot, and other parameters, with options to browse and refine using additional terms, e.g., “action in a future dystopia” or “Beautiful ...
The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings without any other information about the users or films, i.e. without the users being identified except by numbers assigned for the contest.
TasteDive (formerly named TasteKid) is an entertainment recommendation engine for films, TV shows, music, video games, books, people, places, and brands. It also has elements of a social media site; it allows users to connect with "tastebuds", people with like minded interests.
A recommender system (RecSys), or a recommendation system (sometimes replacing system with terms such as platform, engine, or algorithm), is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user.
The AOL.com video experience serves up the best video content from AOL and around the web, curating informative and entertaining snackable videos.
These organic results are determined by the search engine's algorithm. In certain cases, the leading organic results might coincide with, or closely resemble, the sponsored links. For instance, when searching for "pizza," you may observe that the top organic result corresponds to the same national pizza chain featured in the top sponsored link.
The data about each title in a Movie Genome can also support an item-based recommendation engine [6] that recommends based on similarities between content items and users’ preferred “genes.” [7] By contrast, collaborative filtering is used to make recommendations based on statistical similarities in preferences between users.