Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: An interstitial message server system selects a like interstitial message that corresponds to a like user event generated by a user, a skip interstitial message corresponding to a skip user event generated by the user and a playthrough interstitial message corresponding to a play through user event. The like, skip and playthrough interstitial messages can be selected based upon an identifier of a media object that can be presented to the user. At least one each of the like, skip and playthrough interstitial messages can be sent to a user computer before or during the presentation of a media object. While or after the media object is presented to the user at the user computer, the user computer can be caused to detect a like, skip or playthrough user event with respect to the media object.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: An interstitial message server system selects a like interstitial message that corresponds to a like user event generated by a user, a skip interstitial message corresponding to a skip user event generated by the user and a playthrough interstitial message corresponding to a play through user event. The like, skip and playthrough interstitial messages can be selected based upon an identifier of a media object that can be presented to the user. At least one each of the like, skip and playthrough interstitial messages can be sent to a user computer before or during the presentation of a media object. While or after the media object is presented to the user at the user computer, the user computer can be caused to detect a like, skip or playthrough user event with respect to the media object.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: A method of recommending content items includes obtaining vector representations of items based on a matrix of items versus item users that indicates item use by the users, reducing each of the vector representations to a two-dimensional space, creating clusters having cluster centers using one or more mixture models based on the reduced vector representations, using the cluster centers for one or more mixture models to create discrete categories to which items can be assigned and providing one or more recommendations to a first user based on the item assignments within the discrete categories.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

Abstract: In a method for evaluating a performance of a recommender system, a session of M content items consumed by a user can be identified. The session can be defined such that, for each of a first (M-1) content items in the session, a delay time between a completion of consumption of one of the first (M-1) content items and a commencement of consumption of a directly next content item in the session is less than a predetermined duration of time. Content items in a content item-base can be ranked, based on the first (M-1) content items, according to a likelihood of each content item in the content item-base to be consumed next. A session metric can be generated for each content item in the content item-base. The recommender system can be trained based on the session metric for the Mth content item.

Abstract: A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.