METHOD AND SYSTEM OF ITERATIVELY AUTOTUNING PREDICTION PARAMETERS IN A MEDIA CONTENT RECOMMENDER

摘要

In one exemplary embodiment, a method of a computerized media-content recommender includes receiving a user-judgment score based on an historical user-listening data with respect to a media content. A first prediction score for a user with respect to the media content is calculated with a media-content recommender. The media-content recommender includes a first set of prediction parameters. A first prediction error including a difference between the user-judgment score and the first prediction score is determined. At least one parameter value of the first set of prediction parameters is modified with a machine-learning optimization technique to generate a second set of prediction parameters. A second prediction score for the user with respect to the media content is calculated with a media-content recommender. A second prediction error including a difference between the user-judgment score and the second prediction score is calculated.

主权项

1. A method of a computerized media-content recommender comprising:
receiving a user-judgment score based on a historical user-listening data with respect to a media content; calculating, with a media-content recommender, a first prediction score for a user with respect to the media content, wherein the media-content recommender comprises a first set of prediction parameters; determining a first prediction error comprising a difference between the user-judgment score and the first prediction score; modifying, with at least one processor, at least one parameter value of the first set of prediction parameters with a machine-learning optimization technique to generate a second set of prediction parameters; calculating, with a media-content recommender, a second prediction score for the user with respect to the media content; and determining a second prediction error comprising a difference between the user-judgment score and the second prediction score.