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2. MovieLens - Wikipedia

   en.wikipedia.org/wiki/MovieLens

   MovieLens is a web-based recommender system and virtual community that recommends movies for its users to watch, based on their film preferences using collaborative filtering of members' movie ratings and movie reviews. It contains about 11 million ratings for about 8500 movies. [1]

3. Jinni (search engine) - Wikipedia

   en.wikipedia.org/wiki/Jinni_(search_engine)

   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 ...

4. Recommender system - Wikipedia

   en.wikipedia.org/wiki/Recommender_system

   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.

5. 20 Movies Like 'Mean Girls' That Are So Fetch - AOL

   www.aol.com/entertainment/20-movies-mean-girls...

   When two high school misfits and BFFs, Jodi (Justice) and Mindy (Sher), get pranked by the school's mean girls, they decide to fight back by spearheading an outcast uprising, with the help of ...

6. TasteDive - Wikipedia

   en.wikipedia.org/wiki/TasteDive

   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

7. Firefly (website) - Wikipedia

   en.wikipedia.org/wiki/Firefly_(website)

   The Firefly website was created by Firefly Network, Inc.(originally known as Agents Inc.) [1] The company was founded in March 1995 by a group of engineers from MIT Media Lab and some business people from Harvard Business School, including Pattie Maes (Media Lab professor), Upendra Shardanand, Nick Grouf, Max Metral, David Waxman and Yezdi Lashkari. [2]

8. Netflix Prize - Wikipedia

   en.wikipedia.org/wiki/Netflix_Prize

   Netflix provided a training data set of 100,480,507 ratings that 480,189 users gave to 17,770 movies. Each training rating is a quadruplet of the form <user, movie, date of grade, grade>. The user and movie fields are integer IDs, while grades are from 1 to 5 stars. [3]

9. Movie Genome - Wikipedia

   en.wikipedia.org/wiki/Movie_Genome

   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.