Media content discovery and character organization techniques
View Patent ↗Techniques for recommending media are described. A character preference function comprising a plurality of preference coefficients is accessed. A first character model comprises a first set of attribute values for the plurality of attributes of a primary character. A second character model comprises a second set of attribute values for the plurality of attributes of a secondary character. The primary and secondary characters are associated with first and second predetermined salience values, respectively. A first character rating is calculated using the plurality of preference coefficients and the first set of attribute values. A second character rating of the secondary character is calculated using the plurality of preference coefficients with the second set of attribute values. A media rating is calculated based on the first and second salience values and the first and second character ratings. Media is recommended to a user based on the media rating.
1. A method implemented by a computer comprising at least one processor for recommending media content to a user for viewing the recommended media content, the method comprising:
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.
2. The computer-implemented method of claim 1 , wherein:
the primary character of the media content is a main character of the media content;
the secondary character of the media content is not a main character of the media content; and
the first salience value is greater than the second salience value.
3. The computer-implemented method of claim 2 , further comprising:
determining the first character model in part by:
identifying textual content associated with the primary character, the textual content contained in an electronic source;
aggregating a plurality of attribute terms associated with the primary character from the textual content;
mapping at least one of the plurality of attribute terms to at least one of the plurality of attributes; and
updating at least one attribute value of the first set of attribute values of the primary character based on the at least one attribute term.
4. The computer-implemented method of claim 3 , wherein
each of the at least one of the plurality of attribute terms is associated with a corresponding strength value; and
updating the at least one attribute value of the first set of attribute values of the primary character is based on the corresponding strength values.
5. The computer-implemented method of claim 3 , further comprising:
storing the updated attribute values of the primary character in a database as a vector, the vector associated with the primary character.
6. The computer-implemented method of claim 2 , further comprising:
accessing a minimum content rating value;
comparing the media content rating to the minimum content rating value, wherein the media content rating is numerical and the minimum content rating value is numerical; and
determining that the media content rating is greater than the minimum content rating value.
7. The computer-implemented method of claim 2 , wherein recommending the media content to the user comprises:
accessing a minimum content rating value; and
determining that the media content rating is greater than the minimum content rating value, wherein the media content rating is numerical and the minimum content rating value is numerical.
8. The computer-implemented method of claim 2 , wherein:
the character preference function is a second order function associating at least one of the plurality of preference coefficients with two or more attributes of interest of the plurality of attributes;
calculating the first character rating of the primary character further comprises determining the product of the at least one of the plurality of preference coefficients with each attribute value of the first set of attribute values of the two or more attributes of interest of the plurality of attributes; and
calculating the second character rating of the secondary character further comprises determining the product of the at least one of the plurality of preference coefficients with each attribute value of the second set of attribute values of the two or more attributes of interest of the plurality of attributes.
9. 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.
10. The non-transitory computer-readable storage medium of claim 9 , wherein:
the primary character of the media content is a main character of the media content;
the secondary character of the media content is not a main character of the media content; and
the first salience value is greater than the second salience value.
11. The non-transitory computer-readable storage medium of claim 10 , further comprising instructions for:
determining the first character model in part by:
identifying textual content associated with the primary character, the textual content contained in an electronic source;
aggregating a plurality of attribute terms associated with the primary character from the textual content;
mapping at least one of the plurality of attribute terms to at least one of the plurality of attributes; and
updating at least one attribute value of the first set of attribute values of the primary character based on the at least one attribute term.
12. The non-transitory computer-readable storage medium of claim 11 , wherein
each of the at least one of the plurality of attribute terms is associated with a corresponding strength value; and
updating the at least one attribute value of the first set of attribute values of the primary character is based on the corresponding strength values.
13. The non-transitory computer-readable storage medium of claim 11 , further comprising instructions for:
storing the updated attribute values of the primary character in a database as a vector, the vector associated with the primary character.
14. The non-transitory computer-readable storage medium of claim 10 , further comprising instructions for:
accessing a minimum content rating value;
comparing the media content rating to the minimum content rating value, wherein the media content rating is numerical and the minimum content rating value is numerical; and
determining that the media content rating is greater than the minimum content rating value.
15. The non-transitory computer-readable storage medium of claim 10 , wherein recommending the media content to the user comprises instructions for:
accessing a minimum content rating value; and
determining that the media content rating is greater than the minimum content rating value, wherein the media content rating is numerical and the minimum content rating value is numerical.
16. The non-transitory computer-readable storage medium of claim 10 , wherein:
the character preference function is a second order function associating at least one of the plurality of preference coefficients with two or more attributes of interest of the plurality of attributes;
calculating the first character rating of the primary character further comprises determining the product of the at least one of the plurality of preference coefficients with each attribute value of the first set of attribute values of the two or more attributes of interest of the plurality of attributes; and
calculating the second character rating of the secondary character further comprises determining the product of the at least one of the plurality of preference coefficients with each attribute value of the second set of attribute values of the two or more attributes of interest of the plurality of attributes.
17. 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.
18. The apparatus of claim 17 , wherein:
the primary character of the media content is a main character of the media content;
the secondary character of the media content is not a main character of the media content; and
the first salience value is greater than the second salience value.
19. The apparatus of claim 18 , the one or more computer processors further configured to:
determine the first character model in part by:
identifying textual content associated with the primary character, the textual content contained in an electronic source;
aggregating a plurality of attribute terms associated with the primary character from the textual content;
mapping at least one of the plurality of attribute terms to at least one of the plurality of attributes; and
updating at least one attribute value of the first set of attribute values of the primary character based on the at least one attribute term.
20. The apparatus of claim 19 , wherein
each of the at least one of the plurality of attribute terms is associated with a corresponding strength value; and
updating the at least one attribute value of the first set of attribute values of the primary character is based on the corresponding strength values.
21. The apparatus of claim 19 , the one or more computer processors further configured to:
store the updated attribute values of the primary character in a database as a vector, the vector associated with the primary character.
22. The apparatus of claim 18 , the one or more computer processors further configured to:
access a minimum content rating value;
compare the media content rating to the minimum content rating value, wherein the media content rating is numerical and the minimum content rating value is numerical; and
determine that the media content rating is greater than the minimum content rating value.
23. The apparatus of claim 18 , wherein recommending the media content to the user comprises:
accessing a minimum content rating value; and
determining that the media content rating is greater than the minimum content rating value, wherein the media content rating is numerical and the minimum content rating value is numerical.
24. The apparatus of claim 18 , wherein:
the character preference function is a second order function associating at least one of the plurality of preference coefficients with two or more attributes of interest of the plurality of attributes;
calculating the first character rating of the primary character further comprises determining the product of the at least one of the plurality of preference coefficients with each attribute value of the first set of attribute values of the two or more attributes of interest of the plurality of attributes; and
calculating the second character rating of the secondary character further comprises determining the product of the at least one of the plurality of preference coefficients with each attribute value of the second set of attribute values of the two or more attributes of interest of the plurality of attributes.