Temporally Sequenced Content Recommender Method and System

A computer-implemented system comprising one or more processor-based devices configured to:

• access usage information comprising durations of one or more users' attention that is associated with the one or more users' interactions with a first plurality of content objects, wherein each of the first plurality of content object comprises temporally sequenced content objects;
• generate automatically a first vector of affinities that is based upon the usage information;
• generate automatically a second vector of affinities, wherein the affinities of the second vector are generated by a computer-implemented neural network that is applied to a second plurality of content objects, wherein each of the second plurality of content objects comprises temporally sequenced content objects;
• perform automatically a calculation of similarity between the first vector of affinities and the second vector of affinities;
• determine automatically a recommendation of a content object comprising temporally sequenced content objects for delivery to a user of the one or more users, wherein the recommendation is determined in accordance with the calculation of similarity between the first vector of affinities and the second vector of affinities;
• and
• deliver automatically the recommendation to the user.

A computer-implemented system comprising one or more processor-based devices configured to:

• access usage information comprising durations of one or more users' attention that is associated with the one or more users' interactions with a first plurality of content objects, wherein each of the first plurality of content object comprises temporally sequenced content objects;
• generate automatically a first vector of affinities that is based upon the usage information;
• generate automatically a second vector of affinities, wherein the affinities of the second vector are generated by a computer-implemented neural network that is applied to a second plurality of content objects;
• perform automatically a calculation of similarity between the first vector of affinities and the second vector of affinities;
• apply automatically a recommendation serendipity function, wherein the recommendation serendipity function incorporates beneficial serendipity in determining a recommendation;
• determine automatically a recommendation comprising a plurality of recommended content objects, wherein each of the plurality of recommended content objects comprises temporally sequenced content objects, wherein the recommendation is determined in accordance with the calculation of similarity between the first vector of affinities and the second vector of affinities and the application of the recommendation serendipity function;
• and
• deliver automatically the recommendation to a user of the one or more users.