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In addition to movie recommendations, MovieLens also provides information on individual films, such as the list of actors and directors of each film. Users may also submit and rate tags (a form of metadata, such as "based on a book", "too long", or "campy"), which may be used to increase the film recommendations system's accuracy. [3]
The cold start problem is a well known and well researched problem for recommender systems.Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (e-commerce, films, music, books, news, images, web pages) that are likely of interest to the user.
Content-based recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user's likes and dislikes based on an item's features. In this system, keywords are used to describe the items, and a user profile is built to indicate the type of item this user likes.
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]
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
The AOL.com video experience serves up the best video content from AOL and around the web, curating informative and entertaining snackable videos.
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 user based top-N recommendation algorithm uses a similarity-based vector model to identify the k most similar users to an active user. After the k most similar users are found, their corresponding user-item matrices are aggregated to identify the set of items to be recommended.