METHOD AND APPARATUS FOR RECOMMENDING ENTITY

• US 20190311275A1
• Filed: 04/19/2018
• Published: 10/10/2019
• Est. Priority Date: 04/10/2018
• Status: Active Application

• Current Assignee: Beijing Baidu Netcom Science and Technology Company Limited (Baidu Incorporated)
• Original Assignee: Baidu Online Network Technology (Beijing) Co., Ltd (Baidu Incorporated)
• Inventors: Huang, Jizhou, Ding, Shiqiang, Wang, Haifeng
• Application Number:
  US15/957,083
• Publication Number:
  US 20190311275A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/55 Push-based network services

Abstract

Embodiments of the present disclosure disclose a method and apparatus for recommending entity. A method for recommending entity includes: acquiring a candidate entity set associated with a to-be-searched entity, in response to receiving a user'"'"'s search request for an entity; inputting the candidate entity set into a pre-trained ranking model to obtain a candidate entity sequence; and selecting a candidate entity from the candidate entity sequence and recommending the selected candidate entity to the user. The ranking model ranks the candidate entity set based on at least one of: a degree of correlation between each candidate entity in the candidate entity set and the to-be-searched entity; a degree of interest of the user in the each candidate entity in the candidate entity set; and a degree of expectation of the user for the each candidate entity in the candidate entity set.

Claims

1. A method for recommending entity, comprising:

acquiring a candidate entity set associated with a to-be-searched entity, in response to receiving a user'"'"'s search request for the entity;

inputting the candidate entity set into a pre-trained ranking model to obtain a candidate entity sequence; and

selecting a candidate entity from the candidate entity sequence and recommending the selected candidate entity to the user,

wherein, the ranking model ranks the candidate entity set based on at least one of;

a degree of correlation between each candidate entity in the candidate entity set and the to-be-searched entity;

a degree of interest of the user in the each candidate entity in the candidate entity set;

or

a degree of expectation of the user for the each candidate entity in the candidate entity set.

2. The method according to claim 1, wherein the acquiring a candidate entity set associated with a to-be-searched entity further comprises:

adding a candidate entity to the candidate entity set, in response to an existence of an association between the candidate entity and the to-be-searched entity in a preset knowledge graph.

3. The method according to claim 1, wherein the acquiring a candidate entity set associated with a to-be-searched entity further comprises:

adding a candidate entity to the candidate entity set, in response to a number of co-occurrences of the candidate entity and the to-be-searched entity in a search session history exceeding a preset first threshold.

4. The method according to claim 3, wherein the acquiring a candidate entity set associated with a to-be-searched entity further comprises:

determining an entity having a co-occurrence relationship with the to-be-searched entity in a preset corpus as a co-occurrence candidate entity; and

adding a co-occurrence candidate entity having a degree of correlation with the to-be-searched entity exceeding a preset second threshold to the candidate entity set.

5. The method according to claim 1, wherein the ranking model is obtained by training through following steps:

generating a training sample set, each training sample in the training sample set comprising a triplet and a click behavior tag, the triplet comprising a user identification, a first entity, and a second entity, and the click behavior tag being used to indicate whether the user clicked on the second entity in a search result obtained by searching the first entity;

generating a feature vector of a training sample, for each training sample in the generated training sample set;

inputting the training sample set and the generated feature vector into a pre-established gradient boosting decision tree model, and training the gradient boosting decision tree model based on a stochastic gradient descent algorithm; and

generating the ranking model, in response to a minimum cross-entropy loss function,

wherein, the feature vector comprises a feature value for indicating at least one of;

a degree of correlation between the first entity and the second entity in the triplet;

a degree of interest of the user of the triplet in the second entity in the triplet;

or

a degree of expectation of the user of the triplet for the second entity in the triplet.

6. The method according to claim 5, wherein a component for indicating the degree of correlation between the first entity and the second entity in the triplet comprises at least one of:

a degree of correlation of the first entity and the second entity in the triplet in a preset knowledge graph;

a degree of co-occurrence of the first entity and the second entity in the triplet in a search session history;

a degree of co-occurrence of the first entity and the second entity in the triplet in a preset corpus;

or

a subject similarity between the first entity and the second entity in the triplet.

7. The method according to claim 5, wherein the feature value for indicating the degree of interest of the user of the triplet in the second entity in the triplet comprises at least one of:

a click rate of the second entity in the triplet;

a click rate of a subject category to which the second entity belongs in a preset classification table; and

a semantic similarity between the first entity and the second entity in the triplet.

8. The method according to claim 5, wherein the feature value for indicating the degree of expectation of the user of the triplet for the second entity in the triplet comprises at least one of:

a familiarity of relationship of the user and/or the first entity to the second entity determined based on historical click data of the user in the triplet;

a degree of surprise of the second entity relative to the user and/or the first entity in the triplet;

or

a click diversity of the first entity in the triplet.

9. An apparatus for recommending entity, comprising:

at least one processor; and

a memory storing instructions, the instructions when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising;

acquiring a candidate entity set associated with a to-be-searched entity, in response to receiving a user'"'"'s search request for an entity;

inputting the candidate entity set into a pre-trained ranking model to obtain a candidate entity sequence; and

selecting a candidate entity from the candidate entity sequence and recommending the selected candidate entity to the user,

wherein, the ranking model ranks the candidate entity set based on at least one of;

a degree of correlation between each candidate entity in the candidate entity set and the to-be-searched entity;

a degree of interest of the user in the each candidate entity in the candidate entity set;

or

a degree of expectation of the user for the each candidate entity in the candidate entity set.

10. The apparatus according to claim 9, wherein the acquiring a candidate entity set associated with a to-be-searched entity further comprises:

adding a candidate entity to the candidate entity set, in response to an existence of an association between the candidate entity and the to-be-searched entity in a preset knowledge graph.

11. The apparatus according to claim 9, wherein the acquiring a candidate entity set associated with a to-be-searched entity further comprises:

adding a candidate entity to the candidate entity set, in response to a number of co-occurrences of the candidate entity and the to-be-searched entity in a search session history exceeding a preset first threshold.

12. The apparatus according to claim 11, wherein the acquiring a candidate entity set associated with a to-be-searched entity further comprises:

determining an entity having a co-occurrence relationship with the to-be-searched entity in a preset corpus as a co-occurrence candidate entity; and

adding a co-occurrence candidate entity having a degree of correlation with the to-be-searched entity exceeding a preset second threshold to the candidate entity set.

13. The apparatus according to claim 9, wherein the ranking model is obtained by training through following steps:

generating a training sample set, each training sample in the training sample set comprising a triplet and a click behavior tag, the triplet comprising a user identification, a first entity, and a second entity, and the click behavior tag being used to indicate whether the user clicked on the second entity in a search result obtained by searching the first entity;

generating a feature vector of a training sample, for each training sample in the generated training sample set;

inputting the training sample set and the generated feature vector into a pre-established gradient boosting decision tree model, and training the gradient boosting decision tree model based on a stochastic gradient descent algorithm; and

generating the ranking model, in response to a minimum cross-entropy loss function,

wherein, the feature vector comprises a feature value for indicating at least one of;

a degree of correlation between the first entity and the second entity in the triplet;

a degree of interest of the user of the triplet in the second entity in the triplet;

or

a degree of expectation of the user of the triplet for the second entity in the triplet.

14. The apparatus according to claim 13, wherein a component for indicating the degree of correlation between the first entity and the second entity in the triplet comprises at least one of:

a degree of correlation of the first entity and the second entity in the triplet in a preset knowledge graph;

a degree of co-occurrence of the first entity and the second entity in the triplet in a search session history;

a degree of co-occurrence of the first entity and the second entity in the triplet in a preset corpus;

or

a subject similarity between the first entity and the second entity in the triplet.

15. The apparatus according to claim 13, wherein the feature value for indicating the degree of interest of the user of the triplet in the second entity in the triplet comprises at least one of:

a click rate of the second entity in the triplet;

a click rate of a subject category to which the second entity belongs in a preset classification table;

or

a semantic similarity between the first entity and the second entity in the triplet.

16. The apparatus according to claim 13, wherein the feature value for indicating the degree of expectation of the user of the triplet for the second entity in the triplet comprises at least one of:

a familiarity of relationship of the user and/or the first entity to the second entity determined based on historical click data of the user in the triplet;

a degree of surprise of the second entity relative to the user and/or the first entity in the triplet;

or

a click diversity of the first entity in the triplet.

17. A non-transitory computer-readable storage medium storing a computer program, the computer program when executed by one or more processors, causes the one or more processors to perform operations, the operations comprising:

acquiring a candidate entity set associated with a to-be-searched entity, in response to receiving a user'"'"'s search request for the entity;

inputting the candidate entity set into a pre-trained ranking model to obtain a candidate entity sequence; and

selecting a candidate entity from the candidate entity sequence and recommending the selected candidate entity to the user,

wherein, the ranking model ranks the candidate entity set based on at least one of;

a degree of correlation between each candidate entity in the candidate entity set and the to-be-searched entity;

a degree of interest of the user in the each candidate entity in the candidate entity set;

or

a degree of expectation of the user for the each candidate entity in the candidate entity set.