Recommendation Engines
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Modern recommendation engines use matching algorithms processing the data and attach tags to the words bearing emotional attitude as well as matching previously mentioned or searched items. On this basis, accurate, relevant and appealing recommendations and suggestions are made.
The providers are eager to develop a content bearing the emotional attachment to a viewer. Thus, the appropriate content would reach the right viewers at the correct time.
Benefits for the company
Recommendation engines give the entertainment and media providers a chance to focus on the users’ desires and feelings
Feasability
High
Type of expertise/ AI domain
Deep Learning, Matrix factorization
Internal data required
Consumer Content consumption, interaction data
Besides the history of a user within one company, a provider pays exceptional attention to the sensations related to the user.