Method and Electronic Device for Recommending Media Data

• US 20170169018A1
• Filed: 08/19/2016
• Published: 06/15/2017
• Est. Priority Date: 12/09/2015
• Status: Abandoned Application

• Current Assignee: Le Holdings (Beijing) Co., Ltd., Le Shi Internet Information Technology Corp. Beijing
• Original Assignee: Le Holdings (Beijing) Co., Ltd., Le Shi Internet Information Technology Corp. Beijing
• Inventors: HE, Xingwei
• Application Number:
  US15/242,161
• Publication Number:
  US 20170169018A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/48 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually

Abstract

The present disclosure discloses a method and an electronic device for recommending media data, the method includes: generating a regional feature vector of each region; receiving an instruction for obtaining recommended content; obtaining user information, historical access data and location information of a target user; forming an alternative media data group; scoring interest popularity of the target user on the media data in the alternative media data group; obtaining the regional feature vector related to the location information of the target user; performing regional information scoring on the media data in the alternative media data group; obtaining a comprehensive score of the media data in the alternative media data group by combining the interest popularity score of the target user with the regional information score; and recommending a plurality of media data with top ranked comprehensive scores to the target user.

Claims

1. A method for recommending media data, which is applied to an electronic device, comprising:

generating a regional feature vector of each region based on user information and historical access data of a regional user;

receiving an instruction for obtaining recommended content sent by a target user;

obtaining user information, historical access data and location information of the target user;

grasping a plurality of media data related to an interest of the target user from a media database according to the historical access data of the target user to form an alternative media data group;

performing interest popularity scoring of the target user on media data in the alternative media data group according to the historical access data of the target user;

obtaining a regional feature vector related to the location information of the target user according to the location information of the target user;

performing regional information scoring on the media data in the alternative media data group by utilizing the regional feature vector related to the location information of the target user;

obtaining a comprehensive score of the media data in the alternative media data group by combining the interest popularity score of the target user with the regional information score; and

recommending a plurality of media data with top ranked comprehensive scores to the target user.

2. The method according to claim 1, wherein, the step to generate a regional feature vector of each region based on user information and historical access data of a regional user comprises:

obtaining a preset media data classification tree;

obtaining the user information and the historical access data of the regional user;

dividing the user information and the historical access data of the regional user according to regions to form regional user data groups;

performing feature obtained training on each regional user data group respectively according to structure of the media data classification tree; and

obtaining the regional feature vector corresponding to the each region from the feature obtained training result generated.

3. The method according to claim 2, wherein, the step to train each regional user data group respectively according to structure of the media data classification tree comprises:

classifying media data in the regional user data group according to the media data classification tree;

mining and obtaining, from media data of each lowest subclassification, a classification feature of the lowest subclassification via a cluster algorithm; and

obtaining the feature obtained training result by combining the media data classification tree with the classification feature of the lowest subclassification.

4. The method according to claim 1, wherein, the step to perform regional information scoring on the media data in the alternative media data group by utilizing the regional feature vector related to the location information of the target user comprises:

obtaining a feature vector of the media data in the alternative media data group;

calculating a cosine similarity between the feature vector of the media data and the regional feature vector; and

representing the regional information score of the media data with the cosine similarity obtained.

5. The method according to claim 1, wherein, the step to grasp a plurality of media data related to an interest of the target user from the media database comprises:

performing preset character scoring and sequencing on the media data in the media database based on channel character to which the media data belongs; and

grasping the media data according to an order of character scores of the media data.

6. A non-volatile computer-readable storage medium stored with computer executable instructions that, when executed by an electronic device, cause the electronic device to:

generate a regional feature vector of each region based on user information and historical access data of a regional user;

receive an instruction for obtaining recommended content sent by a target user;

obtain user information, historical access data and location information of the target user;

grasp a plurality of media data related to an interest of the target user from a media database according to the historical access data of the target user to form an alternative media data group;

perform interest popularity scoring of the target user on media data in the alternative media data group according to the historical access data of the target user;

obtain a regional feature vector related to the location information of the target user according to the location information of the target user;

perform regional information scoring on the media data in the alternative media data group by utilizing the regional feature vector related to the location information of the target user;

obtain a comprehensive score of the media data in the alternative media data group by combining the interest popularity score of the target user with the regional information score; and

recommend a plurality of media data with top ranked comprehensive scores to the target user.

7. The non-volatile computer-readable storage medium according to claim 6, wherein, the step to generate a regional feature vector of each region based on user information and historical access data of a regional user comprises:

obtaining a preset media data classification tree;

obtaining the user information and the historical access data of the regional user;

dividing the user information and the historical access data of the regional user according to regions to form regional user data groups;

performing feature obtained training on each regional user data group respectively according to structure of the media data classification tree; and

obtaining the regional feature vector corresponding to the each region from the feature obtained training result generated.

8. The non-volatile computer-readable storage medium according to claim 7, wherein, the step to train each regional user data group respectively according to structure of the media data classification tree comprises:

classifying media data in the regional user data group according to the media data classification tree;

mining and obtaining, from media data of each lowest subclassification, a classification feature of the lowest subclassification via a cluster algorithm; and

obtaining the feature obtained training result by combining the media data classification tree with the classification feature of the lowest subclassification.

9. The non-volatile computer-readable storage medium according to claim 6, wherein, the step to perform regional information scoring on the media data in the alternative media data group by utilizing the regional feature vector related to the location information of the target user comprises:

obtaining a feature vector of the media data in the alternative media data group;

calculating a cosine similarity between the feature vector of the media data and the regional feature vector; and

representing the regional information score of the media data with the cosine similarity obtained.

10. The non-volatile computer-readable storage medium according to claim 6, wherein, the step to grasp a plurality of media data related to an interest of the target user from the media database comprises:

performing preset character scoring and sequencing on the media data in the media database based on channel character to which the media data belongs; and

grasping the media data according to an order of character scores of the media data.

11. An electronic device, comprising:

at least one processor; and

a memory, communicably connected with the at least one processor for storing instructions executed by the at least one processor,

wherein execution of the instructions by the at least one processor causes the at least one processor to;

generate a regional feature vector of each region based on user information and historical access data of a regional user;

receive an instruction for obtaining recommended content sent by a target user;

obtain user information, historical access data and location information of the target user;

grasp a plurality of media data related to an interest of the target user from a media database according to the historical access data of the target user to form an alternative media data group;

perform interest popularity scoring of the target user on media data in the alternative media data group according to the historical access data of the target user;

obtain a regional feature vector related to the location information of the target user according to the location information of the target user;

perform regional information scoring on the media data in the alternative media data group by utilizing the regional feature vector related to the location information of the target user;

obtain a comprehensive score of the media data in the alternative media data group by combining the interest popularity score of the target user with the regional information score; and

recommend a plurality of media data with top ranked comprehensive scores to the target user.

12. The electronic device according to claim 11, wherein, the step to generate a regional feature vector of each region based on user information and historical access data of a regional user comprises:

obtaining a preset media data classification tree;

obtaining the user information and the historical access data of the regional user;

dividing the user information and the historical access data of the regional user according to regions to form regional user data groups;

performing feature obtained training on each regional user data group respectively according to structure of the media data classification tree; and

obtaining the regional feature vector corresponding to the each region from the feature obtained training result generated.

13. The electronic device according to claim 12, wherein, the step to train each regional user data group respectively according to structure of the media data classification tree comprises:

classifying media data in the regional user data group according to the media data classification tree;

mining and obtaining, from media data of each lowest subclassification, a classification feature of the lowest subclassification via a cluster algorithm; and

obtaining the feature obtained training result by combining the media data classification tree with the classification feature of the lowest subclassification.

14. The electronic device according to claim 11, wherein, the step to perform regional information scoring on the media data in the alternative media data group by utilizing the regional feature vector related to the location information of the target user comprises:

obtaining a feature vector of the media data in the alternative media data group;

calculating a cosine similarity between the feature vector of the media data and the regional feature vector; and

representing the regional information score of the media data with the cosine similarity obtained.

15. The electronic device according to claim 11, wherein, the step to grasp a plurality of media data related to an interest of the target user from the media database comprises:

performing preset character scoring and sequencing on the media data in the media database based on channel character to which the media data belongs; and

grasping the media data according to an order of character scores of the media data.