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Jinni was a website-based search engine and recommendation engine for movies, TV shows and short films. The service was powered by the Entertainment Genome, an approach to indexing titles based on attributes like mood, tone, plot, and structure. As of 2015, it was no longer available to the public, but is reportedly available via API and ...
Recommender systems. A recommender system, 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. [1][2][3] Recommender systems are particularly useful when an ...
2008. Current status. Active. 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.
Likewise, Inc. Likewise, Inc., is an American technology startup company which provides a social networking service for finding and saving content recommendations for movies, TV shows, books, and podcasts. [1] A team of ex- Microsoft employees founded Likewise in October 2017 with financial investment from Microsoft co-founder Bill Gates.
Knowledge-based recommender systems are often conversational, i.e., user requirements and preferences are elicited within the scope of a feedback loop. A major reason for the conversational nature of knowledge-based recommender systems is the complexity of the item domain where it is often impossible to articulate all user preferences at once.
Netflix Prize. ACM Conference on Recommender Systems. v. t. e. Cold start is a potential problem in computer-based information systems which involves a degree of automated data modelling. Specifically, it concerns the issue that the system cannot draw any inferences for users or items about which it has not yet gathered sufficient information.
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. The competition was held by Netflix, a video streaming ...
v. t. e. Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices. [1] This family of methods became widely known during the Netflix prize challenge due ...