METHOD AND SYSTEM FOR RECOMMENDING CONTENT

Abstract

The present teaching relates to recommending content by analyzing the streamed data. A request is received from a user requesting one or more recommendations from a set of items. A first distribution indicative of an interest distribution of the user in a plurality of topics is obtained. For each item, a second distribution indicative of a classification distribution of the item with respect to the plurality of topics is obtained. A score is estimated based on the first distribution and the second distribution, wherein the score indicates likelihood that the user is interested in the item. The scores associated with the set of items are ranked. The one or more recommendations are presented based on the ranked scores.

Description

Claims

1. A method for content recommendation, comprising: receiving, by a content recommendation computing system, an input over a network;if the input involves a new user; initializing, based on data associated with existing users, an interest distribution for the new user with respect to a plurality of topics,updating, based on activities of the new user, the interest distribution and an average interest distribution of existing users on the plurality of topics; and if the input involves a new content item;initializing, based on data associated with existing content items, a classification distribution of the new content item with respect to the plurality of topics,updating, based on activities of users directed to the new content item, the classification distribution and an average classification distribution of existing content items with respect to the plurality of topics; andrecommending at least one content item from existing content items to the user based on the updated interest distribution and the updated classification distribution.

2. The method of claim 1, wherein the activities of the new user comprise a rating from the new user.

3. The method of claim 1, wherein the updated interest distribution comprises a first plurality of values each representing an interest of the new user in a respective one of the plurality of topics.

4. The method of claim 1, wherein the updated classification distribution comprises a second plurality of values each representing a frequency of the new content item being classified into a respective one of the plurality of topics.

5. The method of claim 1, further comprising: estimating a set of correlation values based on the updated interest distribution and the updated classification distribution;comparing the set of correlation values with historical data to determine a shift of the new user’s interest from one of the plurality of topics to another one of the plurality of topics within a predetermined time period;adjusting the set of correlation values based on the shift of the new user’s interest; andselecting the at least one content item from the existing content items based on the adjusted set of correlation values.

6. The method of claim 5, wherein each of the set of correlation values indicates a rating from the new user to a corresponding existing content item.

7. The method of claim 1, further comprising: receiving a new rating of an existing content item from an existing user;updating an interest distribution associated with the existing user based on the new rating; andupdating a classification distribution associated with the existing content item based on the new rating.

8. A non-transitory, computer-readable medium having information recorded thereon for content recommendation, wherein the information, when read by a machine, causes the machine to perform operations comprising: receiving, by a content recommendation computing system, an input over a network;if the input involves a new user; initializing, based on data associated with existing users, an interest distribution for the new user with respect to a plurality of topics,updating, based on activities of the new user, the interest distribution and an average interest distribution of existing users on the plurality of topics; and if the input involves a new content item;initializing, based on data associated with existing content items, a classification distribution of the new content item with respect to the plurality of topics,updating, based on activities of users directed to the new content item, the classification distribution and an average classification distribution of existing content items with respect to the plurality of topics; andrecommending at least one content item from existing content items to the user based on the updated interest distribution and the updated classification distribution.

9. The medium of claim 8, wherein the activities of the new user comprise a rating from the new user.

10. The medium of claim 8, wherein the updated interest distribution comprises a first plurality of values each representing an interest of the new user in a respective one of the plurality of topics.

11. The medium of claim 8, wherein the updated classification distribution comprises a second plurality of values each representing a frequency of the new content item being classified into a respective one of the plurality of topics.

12. The medium of claim 8, wherein the operations further comprise: estimating a set of correlation values based on the updated interest distribution and the updated classification distribution;comparing the set of correlation values with historical data to determine a shift of the new user’s interest from one of the plurality of topics to another one of the plurality of topics within a predetermined time period;adjusting the set of correlation values based on the shift of the new user’s interest; andselecting the at least one content item from the existing content items based on the adjusted set of correlation values.

13. The medium of claim 12, wherein each of the set of correlation values indicates a rating from the new user to a corresponding existing content item.

14. The medium of claim 8, wherein the operations further comprise: receiving a new rating of an existing content item from an existing user;updating an interest distribution associated with the existing user based on the new rating; andupdating a classification distribution associated with the existing content item based on the new rating.

15. A system for content recommendation, the system comprising: memory storing computer program instructions; andone or more processors that, in response to executing the computer program instructions, effectuate operations comprising: receiving, by a content recommendation computing system, an input over a network;if the input involves a new user; initializing, based on data associated with existing users, an interest distribution for the new user with respect to a plurality of topics,updating, based on activities of the new user, the interest distribution and an average interest distribution of existing users on the plurality of topics; and if the input involves a new content item;initializing, based on data associated with existing content items, a classification distribution of the new content item with respect to the plurality of topics,updating, based on activities of users directed to the new content item, the classification distribution and an average classification distribution of existing content items with respect to the plurality of topics; andrecommending at least one content item from existing content items to the user based on the updated interest distribution and the updated classification distribution.

16. The system of claim 15, wherein the activities of the new user comprise a rating from the new user.

17. The system of claim 15, wherein the updated interest distribution comprises a first plurality of values each representing an interest of the new user in a respective one of the plurality of topics.

18. The system of claim 15, wherein the updated classification distribution comprises a second plurality of values each representing a frequency of the new content item being classified into a respective one of the plurality of topics.

19. The system of claim 15, wherein the operations further comprise: estimating a set of correlation values based on the updated interest distribution and the updated classification distribution;comparing the set of correlation values with historical data to determine a shift of the new user’s interest from one of the plurality of topics to another one of the plurality of topics within a predetermined time period;adjusting the set of correlation values based on the shift of the new user’s interest; andselecting the at least one content item from the existing content items based on the adjusted set of correlation values.

20. The system of claim 19, wherein each of the set of correlation values indicates a rating from the new user to a corresponding existing content item.