User interest learning through hierarchical interest graph techniques are described. In one or more implementations, each of a plurality of categories in a directed hierarchical interest graph are assigned a distance value which represents a shortest distance in the directed hierarchical interest graph from a root category to the category. A list of keywords is formed from user data that denotes a corresponding said category and frequency of the category. A maximum of the frequencies amongst the plurality of categories is determined and a score is calculated for each of the keywords based on the frequency of the category, the maximum of the frequencies, and the distance value for the keyword. Increments of scores may be propagated from child categories to parent categories in the hierarchical interest graph such that greater weighting is given to child categories that are less abstract than parent categories in the directed graph. Further, the scores of the plurality of categories may be adjusted based on subsequent scores calculated from subsequent user data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method implemented by one or more computing devices, the method comprising: assigning each of a plurality of categories in a directed hierarchical interest graph a distance value that represents a shortest distance in the directed hierarchical interest graph from a root said category to the category; forming a list of keywords by the one or more computing devices from user data that denotes a corresponding said category and frequency of the category; calculating a score for each of the keywords by the one or more computing devices based on the frequency of the category, a maximum of the frequencies amongst the plurality of categories, and the distance value for the keyword; propagating increments of scores through the directed hierarchical interest graph from child said categories to parent said categories by the one or more computing devices such that greater weighting is given to child said categories that are less abstract than parent said categories in the directed hierarchical interest graph; and outputting a resulting score by the one or more computing devices based at least in part on the calculating and the propagating. 2. A method as described in claim 1, wherein the propagating of the increments is performed for a plurality of said categories disposed in a plurality of levels in the directed hierarchical interest graph. 3. A method as described in claim 1, wherein the increments are based at least in part on a number of the parent said categories. 4. A method as described in claim 1, wherein the increments are based at least in part on semantic similarity of the parent said category and the child said category. 5. A method as described in claim 1, wherein the increments are based at least in part on a path taken in the directed hierarchical interest graph for the propagating. 6. A method as described in claim 1, further comprising determining there are a plurality of paths in the directed hierarchical interest graph between the parent and child said categories and the propagating is performed for the shortest one of the plurality of paths based on a number of edges involved in the plurality of paths, one to another. 7. A method as described in claim 1, further comprising adjusting the scores of the plurality of categories based on subsequent said scores calculated from subsequent user data and wherein the exposing is performed based at least in part on the calculating, the propagating, and the adjusting. 8. A method as described in claim 1, further comprising obtaining the user data from one or more social network services. 9. A method as described in claim 8, wherein the user data describes likes or followed user accounts in the one or more social network services. 10. A method implemented by one or more computing devices, the method comprising: assigning each of a plurality of categories in a directed hierarchical interest graph a distance value that represents a shortest distance in the directed hierarchical interest graph from a root said category to the category; forming a list of keywords from user data that denotes a corresponding one of the plurality of categories and frequency of the category; determining a maximum of the frequencies amongst the plurality of categories; calculating a score for each of the keywords based on the frequency of the category, the maximum of the frequencies, and the distance value for the keyword; adjusting the scores of the plurality of categories based on subsequent said scores calculated from subsequent user data; and outputting a resulting score by the one or more computing devices based at least in part on the calculating and the adjusting. 11. A method as described in claim 10, wherein the adjusting is performed as a linear combination of the calculated score of the user data and the subsequent said scores calculated from the subsequent user data. 12. A method as described in claim 11, wherein the resulting score from the adjusting is based at least in part on the distance. 13. A method as described in claim 10, further comprising obtaining the user data and the subsequent said user data from one or more social network services. 14. A method as described in claim 13, wherein the user data and the subsequent said user data describes likes or followed user accounts in the one or more social network services. 15. A system comprising: a distance module implemented at least partially in hardware, the distance module configured to assign each of a plurality of categories in a directed hierarchical interest graph a distance value that represents a shortest distance in the directed hierarchical interest graph from a root said category to the category; a text generation tool implemented at least partially in hardware, the text generation tool configured to form a list of keywords from user data that denotes a corresponding said category and frequency of the category and determine a maximum of the frequencies amongst the plurality of categories; a score calculation module implemented at least partially in hardware, the score calculation module configured to calculate a score for each of the keywords based on the frequency of the category, the maximum of the frequencies, and the distance value for the keyword; a score propagation module implemented at least partially in hardware, the score propagation module configured to propagate increments of scores from child said categories to parent said categories such that greater weighting is given to child said categories that are less abstract than parent said categories in the directed hierarchical interest graph; and a score adjustment module implemented at least partially in hardware, the score adjustment configured to adjust the scores of the plurality of categories based on subsequent said scores calculated from subsequent user data. 16. A system as described in claim 15, wherein the score adjustment module performs the adjustment based at least in part on the distance value. 17. A system as described in claim 15, wherein the score adjustment module performs the adjustment as a linear combination of the calculated score of the user data and the subsequent said scores calculated from the subsequent user data. 18. A system as described in claim 15, wherein the increments are based at least in part on semantic similarity of the parent said category and the child said category. 19. A system as described in claim 15, wherein the increments are based at least in part on a path taken in the directed hierarchical interest graph for the propagating. 20. A system as described in claim 15, wherein the user data and the subsequent said user data describes likes or followed user accounts in the one or more social network services.