Resource recommendation method, device, apparatus and computer readable storage medium

1. A resource recommendation method applied on a multimedia platform comprising a processor, wherein the method comprises:

receiving, by the processor, a request sent by a first user;

providing, by the processor, a first resource to the first user according to the request sent by the first user;

acquiring, by the processor, according to a resource type of the first resource provided to the first user, recommendable content categories corresponding to the resource type, and a recommendation weight of each of the recommendable content categories;

determining, by the processor according to the recommendation weight of each of the recommendable content categories, a target category to be recommended; and

recommending, by the processor to the first user, a second resource under the target category, and displaying entry information of the recommended second resource on a display interface of the first resource;

wherein the determining, by the processor according to the recommendation weight of each of the recommendable content categories, a target category to be recommended comprises:

determining, according to an online time of the first resource, whether the first resource is online;

if the first resource is not online and the target category does not comprise a preview category, determining a recommendation weight of the preview category according to a maximum value of the recommendation weight of the target category; and

using the preview category as the target category to be recommended.

2. The method according to claim 1 , wherein the acquiring, by the processor, according to a resource type of a first resource provided to a first user, recommendable content categories corresponding to the resource type, and a recommendation weight of each of the recommendable content categories comprises:

acquiring, according to the resource type of the first resource provided to the first user, historical behavior data of a second user who has used the first resource under the resource type;

determining, according to the historical behavior data of the second user, the recommendable content categories selected by the second user upon each usage of the first resource under the resource type to obtain the recommendable content categories corresponding to the resource type; and

determining, according to a number of times the second user selects each of the recommendable content categories, the recommendation weight of each of the recommendable content categories.

3. The method according to claim 1 , wherein the determining, according to the recommendation weight of each of the recommendable content categories, a target category to be recommended comprises:

determining, according to the recommendation weight of each of the recommendable content categories, a recommendable content category whose recommendation weight is greater than a weight threshold as the target category to be recommended;

or, determining, according to the recommendation weight of each of the recommendable content categories, a preset number of recommendable content categories with a highest recommendation weight as the target category to be recommended.

4. The method according to claim 1 , wherein the recommending, by the processor to the first user, a second resource under the target category comprises:

acquiring, according to attribute information of the first resource, the second resource under the target category related to the first resource; and

categorizing and displaying, on the display interface of the first resource, the entry information of the second resource.

5. The method according to claim 4 , wherein the categorizing and displaying, on a display interface of the first resource, the entry information of the second resource comprises:

determining, according to size of recommendation weights of content categories of the second resource, displaying areas of the content categories of the second resource in the display interface, wherein a displaying area of a content category having a higher recommendation weight is placed at a front; and

displaying, in a corresponding displaying area, the entry information of the second resource under different content categories.

6. The method according to claim 4 , wherein the categorizing and the displaying, on the display interface of the first resource, the entry information of the second resource comprises:

adding, according to the content categories of the second resource, a content category identifier to the entry information of the second resource; and

displaying, in the display interface of the first resource, the entry information of the second resource.

7. The method according to claim 1 , wherein the recommending, by the processor to the first user, a second resource under the target category comprises:

displaying, on the display interface of the first resource, voice interaction guidance information, wherein the voice interaction guidance information comprises the target category.

8. The method according to claim 7 , wherein after the displaying, on the display interface of the first resource, voice interaction guidance information, the method further comprises:

receiving voice information input by a user;

performing semantic analysis on the voice information input by the user to determine a target category selected by the user; and

opening the second resource under the target category selected by the user.

9. A resource recommendation apparatus applied on a multimedia platform, comprising:

a memory, a processor, and a computer program stored on the memory and operable on the processor,

wherein the processor, when running the computer program, is configured to:

receive a request sent by a first user;

provide a first resource to the first user according to the request sent by the first user;

acquire, according to a resource type of the first resource provided to the first user, recommendable content categories corresponding to the resource type, and a recommendation weight of each of the recommendable content categories;

determine, according to the recommendation weight of each of the recommendable content categories, a target category to be recommended; and

recommend, to the first user, a second resource under the target category, and display entry information of the recommended second resource on a display interface of the first resource;

wherein the processor is further configured to:

determine, according to an online time of the first resource, whether the first resource is online;

if the first resource is not online and the target category does not comprise a preview category, determine a recommendation weight of the preview category according to a maximum value of the recommendation weight of the target category; and

use the preview category as the target category to be recommended.

10. The apparatus according to claim 9 , wherein the processor is further configured to:

acquire, according to the resource type of the first resource provided to the first user, historical behavior data of a second user who has used the first resource under the resource type;

determine, according to the historical behavior data of the second user, the recommendable content categories selected by the second user upon each usage of the first resource under the resource type to obtain the recommendable content categories corresponding to the resource type; and

determine, according to a number of times the second user selects each of the recommendable content categories, the recommendation weight of each of the recommendable content categories.

11. The apparatus according to claim 9 , wherein the processor is further configured to:

determine, according to the recommendation weight of each of the recommendable content categories, a recommendable content category whose recommendation weight is greater than a weight threshold as the target category to be recommended;

or,

determine, according to the recommendation weight of each of the recommendable content categories, a preset number of recommendable content categories with a highest recommendation weight as the target category to be recommended.

12. The apparatus according to claim 9 , wherein the processor is further configured to:

acquire, according to attribute information of the first resource, the second resource under the target category related to the first resource; and

categorize and display, on the display interface of the first resource, the entry information of the second resource.

13. The apparatus according to claim 12 , wherein the processor is further configured to:

determine, according to size of recommendation weights of content categories of the second resource, displaying areas of the content categories of the second resource in the display interface, wherein a displaying area of a content category having a higher recommendation weight is placed at a front; and

display, in a corresponding displaying area, the entry information of the second resource under different content categories.

14. The apparatus according to claim 12 , wherein the processor is further configured to:

add, according to the content categories of the second resource, a content category identifier to the entry information of the second resource; and

display, in the display interface of the first resource, the entry information of the second resource.

15. The apparatus according to claim 9 , wherein the processor is further configured to:

display, on the display interface of the first resource, voice interaction guidance information, wherein the voice interaction guidance information comprises the target category.

16. The apparatus according to claim 15 , wherein the processor is further configured to:

receive voice information input by a user;

perform semantic analysis on the voice information input by the user to determine a target category selected by the user; and

open the second resource under the target category selected by the user.

17. A non-transitory computer readable storage medium having a computer program stored thereon,

where the computer program, when executed by a processor, implements the steps of:

receive a request sent by a first user;

provide a first resource to the first user according to the request sent by the first user;

acquire, according to a resource type of the first resource provided to the first user, recommendable content categories corresponding to the resource type and a recommendation weight of each of the recommendable content categories;

determine, according to the recommendation weight of each of the recommendable content categories, a target category to be recommended; and

recommend, to the first user, a second resource under the target category, and display entry information of the recommended second resource on a display interface of the first resource;

where the computer program, when executed by a processor, implements the steps of:

determine, according to an online time of the first resource, whether the first resource is online;

if the first resource is not online and the target category does not comprise a preview category, determine a recommendation weight of the preview category according to a maximum value of the recommendation weight of the target category; and

use the preview category as the target category to be recommended.

18. The non-transitory storage medium according to claim 17 , wherein the storage medium further comprises computer execution instruction which, when executed by a processor, implements the steps of:

acquire, according to the resource type of the first resource provided to the first user, historical behavior data of a second user who has used the first resource under the resource type;

determine, according to the historical behavior data of the second user, the recommendable content categories selected by the second user upon each usage of the first resource under the resource type to obtain the recommendable content categories corresponding to the resource type; and

determine, according to a number of times the second user selects each of the recommendable content categories, the recommendation weight of each of the recommendable content categories.