Media content discovery and character organization techniques

A non-transitory computer-readable storage medium comprising computer-executable instructions for recommending media content to a user for viewing the recommended media content, the computer-executable instructions comprising instructions for:

• accessing a character preference function of the user, the character preference function comprising information identifying a plurality of preference coefficients, each preference coefficient of the plurality of preference coefficients associated with at least one attribute of interest of a plurality of attributes;
• accessing a first character model, the first character model comprising information identifying a first set of attribute values for the plurality of attributes of a primary character of a media content;
• accessing a first salience value, wherein the first salience value is predetermined or pre-calculated, and wherein the first salience value is associated with the primary character of the media content;
• accessing a second character model, the second character model comprising information identifying a second set of attribute values for the plurality of attributes of a secondary character of the media content;
• accessing a second salience value, wherein the second salience value is predetermined or pre-calculated, and wherein the second salience value is associated with the secondary character of the media content,
• wherein the first salience value and the second salience value are different;
• calculating a first character rating of the primary character by performing a summation of the products of the plurality of preference coefficients with the first set of attribute values;
• calculating a second character rating of the secondary character by performing a summation of the products of the plurality of preference coefficients with the second set of attribute values;
• calculating a media content rating, the media content rating calculated based on the first salience value, the second salience value, the first character rating, and the second character rating;
• and
• recommending the media content to the user based on the media content rating.

An apparatus for recommending media content to a user for viewing the recommended media content, the apparatus comprising:

• a memory configured to store a character preference function of the user;
• and
• one or more computer processors configured to:
• access the character preference function of the user, the character preference function comprising information identifying a plurality of preference coefficients, each preference coefficient of the plurality of preference coefficients associated with at least one attribute of interest of a plurality of attributes;
• access a first character model, the first character model comprising information identifying a first set of attribute values for the plurality of attributes of a primary character of a media content;
• access a first salience value, wherein the first salience value is predetermined or pre-calculated, and wherein the first salience value is associated with the primary character of the media content;
• access a second character model, the second character model comprising information identifying a second set of attribute values for the plurality of attributes of a secondary character of the media content;
• access a second salience value, wherein the second salience value is predetermined or pre-calculated, and wherein the second salience value is associated with the secondary character of the media content,
• wherein the first salience value and the second salience value are different;
• calculate a first character rating of the primary character by performing a summation of the products of the plurality of preference coefficients with the first set of attribute values;
• calculate a second character rating of the secondary character by performing a summation of the products of the plurality of preference coefficients with the second set of attribute values;
• calculate a media content rating, the media content rating calculated based on the first salience value, the second salience value, the first character rating, and the second character rating;
• and
• recommend the media content to the user based on the media content rating.