CN112328645A - Method and system for determining interests and hobbies of users based on knowledge graph - Google Patents
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Abstract
The invention provides a method and a system for determining user interests and hobbies based on a knowledge graph. The method comprises the following steps: acquiring browsing records of a user on a target knowledge graph within a preset time period; determining the interest degree of the user on the target knowledge graph according to the browsing record of the user on the target knowledge graph; and generating a knowledge graph set which is interested by the user according to the interest degree of the user to the target knowledge graph.
Description
Technical Field
The invention relates to the technical field of intelligent learning, in particular to a method and a system for determining user interests and hobbies based on a knowledge graph.
Background
In the prior art, a plurality of knowledge points are shown by means of a knowledge graph. Knowledge map (Knowledge Graph) is a series of different graphs displaying Knowledge development process and structure relationship in the book intelligence field, describing Knowledge resources and carriers thereof by using visualization technology, mining, analyzing, constructing, drawing and displaying Knowledge and mutual relation between Knowledge resources and Knowledge carriers.
For a user who often browses knowledge through a knowledge graph, the interest degree of the user on knowledge points in the knowledge graph can be greatly influenced by the quality of the display form of the knowledge graph and the number of knowledge points related to the knowledge graph, so that a technology capable of intelligently and accurately determining the interest and hobbies of the user on knowledge based on the knowledge graph needs to be designed.
Disclosure of Invention
The invention provides a method and a system for determining user interests and hobbies based on a knowledge graph.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a method for determining user interests and hobbies based on a knowledge graph in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides a method for determining user interests based on a knowledge graph, which comprises the following steps of S1-S3:
and step S1, acquiring browsing records of the target knowledge graph in a preset time period.
And step S2, determining the interest degree of the user on the target knowledge graph according to the browsing record of the user on the target knowledge graph.
And step S3, generating a knowledge graph set which is interested by the user according to the interest degree of the user on the target knowledge graph.
The beneficial effects of the above technical scheme are: according to the technical scheme, the interest degree of the user on the target knowledge graph is determined according to the browsing record of the user on the target knowledge graph; and generating a knowledge graph set which is interested by the user according to the interest degree of the user to the target knowledge graph, thereby determining all knowledge graphs with high interest of the user.
In one embodiment, before obtaining the browsing record of the target knowledge-graph by the user in the preset time period, the method further includes:
step A1, obtaining a target knowledge graph, and performing quality evaluation on the target knowledge graph according to the following formula (1):
wherein J represents the quality identification value of the target knowledge graph, N represents the number of important knowledge points in the target knowledge graph, and SiRepresenting the data size occupied by the ith important knowledge point in the target knowledge graph; x is the total data size in the target knowledge graph; y represents the average data size occupied by the important knowledge points in the target knowledge graph; m represents the total number of text paragraphs in the target knowledge-graph, QjThe important coefficient of the jth text paragraph in the target knowledge graph is represented, and the value range is [0, 1 ]]The more important a text passage is, the larger its importance coefficient is; alpha is alphajRepresenting the data size occupied by the jth text paragraph in the target knowledge-graph; alpha is alphamaxRepresenting the data size occupied by the text paragraph occupying the largest data size in the target knowledge graph; beta represents the data size occupied by all the non-important knowledge points in the target knowledge graph;
step A2, judging whether the quality identification value of the target knowledge graph is larger than or equal to a preset threshold value, if so, setting a first number of associated knowledge point keywords for the target knowledge graph, and establishing an association relation between the first number of associated knowledge point keywords and the target knowledge graph; otherwise, setting a second number of associated knowledge point keywords for the target knowledge graph, and establishing an association relation between the second number of associated knowledge point keywords and the target knowledge graph; wherein the first number is greater than the second number; the associated knowledge point keywords are keywords of the knowledge points with preset similar relations with the key knowledge points.
The beneficial effects of the above technical scheme are: the quality of the target knowledge graph can be obtained by analyzing parameters in the target knowledge graph, such as relevant data of important knowledge points and non-important knowledge points, text paragraphs and other data, and different quantities of associated knowledge points are configured for knowledge graphs of different qualities, so that the target knowledge graph can amplify advantages, reduce disadvantages and provide a high-quality data basis for subsequent continuous analysis and determination of user interest and hobby knowledge points.
In one embodiment, determining the interest degree of the user in the target knowledge graph according to the browsing record of the user on the target knowledge graph comprises:
step B1, calculating the interest degree of the user on the target knowledge graph according to the following formula (2):
wherein eta represents the interest degree of the user on the target knowledge graph; ln is expressed as a natural logarithm, BiRepresenting the frequency of clicking the ith important knowledge point in the target knowledge graph by the user in a preset time period; fiRepresenting the number of text paragraphs corresponding to the ith important knowledge point in the target knowledge graph; m represents the total number of text paragraphs in the target knowledge-graph, RkRepresenting the similarity between the kth associated knowledge point key word clicked by the user and the corresponding key knowledge point in the target knowledge graph; h represents the number of the keywords of the associated knowledge points clicked by the user in a preset time period; g1Representing the total browsing times of browsing the target knowledge graph after the user clicks the associated knowledge point keywords in a preset time period, G2Representing the maximum times of browsing the target knowledge graph in the historical browsing records of the user within a time period equal to the preset time period; t represents the duration of a preset time period; i iskShowing the page loading time of the target knowledge graph after the user clicks the kth associated knowledge point keyword; o iskRepresenting the browsing time of the target knowledge graph after the user clicks the kth associated knowledge point keyword;
step B2, comparing the interest degree of the user on the target knowledge graph with a preset interest degree threshold value, and confirming that the user has high interest degree on the target knowledge graph when the interest degree is larger than or equal to the preset interest degree threshold value; otherwise, confirming that the user has low hobby on the target knowledge graph.
The beneficial effects of the above technical scheme are: the interest degree of the user on the target knowledge graph is calculated by using the parameters such as the total browsing time length and the browsing times of the user on the target knowledge graph, wherein the used parameters are detailed to the times of entering the browsing knowledge graph by clicking the associated knowledge points, the accuracy of the calculation result is ensured, and the accuracy of evaluating the hobby degree of the user on the knowledge graph is improved.
In one embodiment, generating a set of knowledge-graphs of interest to a user based on the user's interest level in a target knowledge-graph comprises:
and adding all target knowledge graphs with high hobby degrees into the knowledge graph set which is interested by the user to form the knowledge graph set which is interested by the user.
In another embodiment, generating the set of knowledge-graphs in which the user is interested according to the interest degree of the user in the target knowledge-graph comprises:
and sequencing all target knowledge graphs with high hobby degrees from large to small according to the corresponding interestingness to form a sequentially-arranged knowledge graph set which is interesting to the user.
The beneficial effects of the above technical scheme are: by calculating the target knowledge graph with high hobby degree and adding the target knowledge graph with high hobby degree into the knowledge graph set interested by the user, the interest information of the user on the knowledge graph can be conveniently counted finally, the subsequent analysis of the relation between the knowledge graph and the user is facilitated, and a data research and development basis is provided for the further improvement of the knowledge graph.
Corresponding to the method for determining the user interests based on the knowledge graph provided by the embodiment of the invention, the embodiment of the invention also provides a system for determining the user interests based on the knowledge graph, which comprises the following steps:
the acquisition module is used for acquiring browsing records of a target knowledge graph by a user in a preset time period;
the determining module is used for determining the interest degree of the user on the target knowledge graph according to the browsing record of the user on the target knowledge graph;
and the generating module is used for generating a knowledge graph set which is interested by the user according to the interest degree of the user to the target knowledge graph.
In one embodiment, before obtaining the browsing record of the target knowledge-graph by the user in the preset time period, the method further includes:
step A1, obtaining a target knowledge graph, and performing quality evaluation on the target knowledge graph according to the following formula (1):
wherein J represents the quality identification value of the target knowledge graph, N represents the number of important knowledge points in the target knowledge graph, and SiRepresenting the data size occupied by the ith important knowledge point in the target knowledge graph; x is the total data size in the target knowledge graph; y represents the average data size occupied by the important knowledge points in the target knowledge graph; m represents the total number of text paragraphs in the target knowledge-graph, QjThe important coefficient of the jth text paragraph in the target knowledge graph is represented, and the value range is [0, 1 ]]The more important a text passage is, the larger its importance coefficient is; alpha is alphajRepresenting the data size occupied by the jth text paragraph in the target knowledge-graph; alpha is alphamaxRepresenting the data size occupied by the text paragraph occupying the largest data size in the target knowledge graph; beta represents the data size occupied by all the non-important knowledge points in the target knowledge graph;
step A2, judging whether the quality identification value of the target knowledge graph is larger than or equal to a preset threshold value, if so, setting a first number of associated knowledge point keywords for the target knowledge graph, and establishing an association relation between the first number of associated knowledge point keywords and the target knowledge graph; otherwise, setting a second number of associated knowledge point keywords for the target knowledge graph, and establishing an association relation between the second number of associated knowledge point keywords and the target knowledge graph; wherein the first number is greater than the second number; the associated knowledge point keywords are keywords of the knowledge points with preset similar relations with the key knowledge points.
In one embodiment, determining the interest degree of the user in the target knowledge graph according to the browsing record of the user on the target knowledge graph comprises:
step B1, calculating the interest degree of the user on the target knowledge graph according to the following formula (2):
wherein eta represents the interest degree of the user on the target knowledge graph; ln is expressed as a natural logarithm, BiRepresenting the frequency of clicking the ith important knowledge point in the target knowledge graph by the user in a preset time period; fiRepresenting the number of text paragraphs corresponding to the ith important knowledge point in the target knowledge graph; m represents the total number of text paragraphs in the target knowledge-graph, RkRepresenting the similarity between the kth associated knowledge point key word clicked by the user and the corresponding key knowledge point in the target knowledge graph; h represents the number of the keywords of the associated knowledge points clicked by the user in a preset time period; g1Representing the total browsing times of browsing the target knowledge graph after the user clicks the associated knowledge point keywords in a preset time period, G2Representing the maximum times of browsing the target knowledge graph in the historical browsing records of the user within a time period equal to the preset time period; t represents the duration of a preset time period; i iskShowing the page loading time of the target knowledge graph after the user clicks the kth associated knowledge point keyword; o iskRepresenting the browsing time of the target knowledge graph after the user clicks the kth associated knowledge point keyword;
step B2, comparing the interest degree of the user on the target knowledge graph with a preset interest degree threshold value, and confirming that the user has high interest degree on the target knowledge graph when the interest degree is larger than or equal to the preset interest degree threshold value; otherwise, confirming that the user has low hobby on the target knowledge graph.
In one embodiment, generating a set of knowledge-graphs of interest to a user based on the user's interest level in a target knowledge-graph comprises:
and adding all target knowledge graphs with high hobby degrees into the knowledge graph set which is interested by the user to form the knowledge graph set which is interested by the user.
In one embodiment, generating a set of knowledge-graphs of interest to a user based on the user's interest level in a target knowledge-graph comprises:
and sequencing all target knowledge graphs with high hobby degrees from large to small according to the corresponding interestingness to form a sequentially-arranged knowledge graph set which is interesting to the user.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for determining user interests based on a knowledge graph is characterized by comprising the following steps:
acquiring browsing records of a user on a target knowledge graph within a preset time period;
determining the interest degree of the user on the target knowledge graph according to the browsing record of the user on the target knowledge graph;
and generating a knowledge graph set which is interested by the user according to the interest degree of the user to the target knowledge graph.
2. The method of claim 1,
the obtaining, in a preset time period and before the browsing record of the target knowledge graph by the user, further includes:
step A1, obtaining a target knowledge graph, and performing quality evaluation on the target knowledge graph according to the following formula (1):
wherein J represents the quality identification value of the target knowledge graph, N represents the number of important knowledge points in the target knowledge graph, and SiRepresenting the data size occupied by the ith important knowledge point in the target knowledge graph; x is the total data size in the target knowledge graph; y represents the average data size occupied by the important knowledge points in the target knowledge graph; m represents the total number of text paragraphs in the target knowledge-graph, QjThe important coefficient of the jth text paragraph in the target knowledge graph is represented, and the value range is [0, 1 ]]The more important a text passage is, the larger its importance coefficient is; alpha is alphajRepresenting the data size occupied by the jth text paragraph in the target knowledge-graph; alpha is alphamaxRepresenting the data size occupied by the text paragraph occupying the largest data size in the target knowledge graph; beta represents the data size occupied by all the non-important knowledge points in the target knowledge graph;
step A2, judging whether the quality identification value of the target knowledge graph is larger than or equal to a preset threshold value, if so, setting a first number of associated knowledge point keywords for the target knowledge graph, and establishing an association relation between the first number of associated knowledge point keywords and the target knowledge graph; otherwise, setting a second number of associated knowledge point keywords for the target knowledge graph, and establishing an association relation between the second number of associated knowledge point keywords and the target knowledge graph; wherein the first number is greater than the second number; and the associated knowledge point keywords are keywords of the knowledge points with preset similar relations with the key knowledge points.
3. The method of claim 1,
the determining the interest degree of the user in the target knowledge graph according to the browsing record of the user in the target knowledge graph comprises the following steps:
step B1, calculating the interest degree of the user to the target knowledge graph according to the following formula (2):
wherein eta represents the interest degree of the user in the target knowledge graph; ln is expressed as a natural logarithm, BiRepresenting the frequency of clicking the ith important knowledge point in the target knowledge graph by a user within the preset time period; fiRepresenting the number of text paragraphs corresponding to the ith important knowledge point in the target knowledge graph; m represents the total number of text paragraphs in the target knowledge-graph, RkRepresenting the similarity between the kth associated knowledge point key word clicked by the user and the corresponding key knowledge point in the target knowledge graph; h represents the number of the associated knowledge point keywords clicked by the user in the preset time period; g1Representing the total browsing times, G, of the user browsing the target knowledge graph after clicking the associated knowledge point keywords in the preset time period2Representing the maximum times of browsing the target knowledge graph in the historical browsing records of the user within a time period equal to the preset time period; t represents the duration of the preset time period; i iskShowing the page loading time of the target knowledge graph after the user clicks the kth associated knowledge point keyword; o iskRepresenting the browsing time of the target knowledge graph after the user clicks the kth associated knowledge point keyword;
step B2, comparing the interest degree of the user on the target knowledge graph with a preset interest degree threshold value, and confirming that the user has high interest degree on the target knowledge graph when the interest degree is larger than or equal to the preset interest degree threshold value; and otherwise, confirming that the user has low hobby on the target knowledge graph.
4. The method of claim 3, wherein generating the set of knowledge-graphs of interest to the user based on the user's interest level in the target knowledge-graph comprises:
and adding all target knowledge graphs with high hobby degrees into the knowledge graph set which is interested by the user to form the knowledge graph set which is interested by the user.
5. The method of claim 3, wherein generating the set of knowledge-graphs of interest to the user based on the user's interest level in the target knowledge-graph comprises:
and sequencing all target knowledge graphs with high hobby degrees from large to small according to the corresponding interestingness to form a sequentially-arranged knowledge graph set which is interesting to the user.
6. A system for determining interests and tastes of a user based on a knowledge-graph, comprising:
the acquisition module is used for acquiring browsing records of a target knowledge graph by a user in a preset time period;
the determining module is used for determining the interest degree of the user on the target knowledge graph according to the browsing record of the user on the target knowledge graph;
and the generating module is used for generating the knowledge graph set which is interested by the user according to the interest degree of the user to the target knowledge graph.
7. The system of claim 6,
the obtaining, in a preset time period and before the browsing record of the target knowledge graph by the user, further includes:
step A1, obtaining a target knowledge graph, and performing quality evaluation on the target knowledge graph according to the following formula (1):
wherein J represents the quality identification value of the target knowledge graph, and N represents the number of important knowledge points in the target knowledge graph，SiRepresenting the data size occupied by the ith important knowledge point in the target knowledge graph; x is the total data size in the target knowledge graph; y represents the average data size occupied by the important knowledge points in the target knowledge graph; m represents the total number of text paragraphs in the target knowledge-graph, QjThe important coefficient of the jth text paragraph in the target knowledge graph is represented, and the value range is [0, 1 ]]The more important a text passage is, the larger its importance coefficient is; alpha is alphajRepresenting the data size occupied by the jth text paragraph in the target knowledge-graph; alpha is alphamaxRepresenting the data size occupied by the text paragraph occupying the largest data size in the target knowledge graph; beta represents the data size occupied by all the non-important knowledge points in the target knowledge graph;
step A2, judging whether the quality identification value of the target knowledge graph is larger than or equal to a preset threshold value, if so, setting a first number of associated knowledge point keywords for the target knowledge graph, and establishing an association relation between the first number of associated knowledge point keywords and the target knowledge graph; otherwise, setting a second number of associated knowledge point keywords for the target knowledge graph, and establishing an association relation between the second number of associated knowledge point keywords and the target knowledge graph; wherein the first number is greater than the second number; and the associated knowledge point keywords are keywords of the knowledge points with preset similar relations with the key knowledge points.
8. The system of claim 6,
the determining the interest degree of the user in the target knowledge graph according to the browsing record of the user in the target knowledge graph comprises the following steps:
step B1, calculating the interest degree of the user to the target knowledge graph according to the following formula (2):
wherein eta represents the interest degree of the user in the target knowledge graph; ln is expressed as a natural logarithm, BiRepresenting the frequency of clicking the ith important knowledge point in the target knowledge graph by a user within the preset time period; fiRepresenting the number of text paragraphs corresponding to the ith important knowledge point in the target knowledge graph; m represents the total number of text paragraphs in the target knowledge-graph, RkRepresenting the similarity between the kth associated knowledge point key word clicked by the user and the corresponding key knowledge point in the target knowledge graph; h represents the number of the associated knowledge point keywords clicked by the user in the preset time period; g1Representing the total browsing times, G, of the user browsing the target knowledge graph after clicking the associated knowledge point keywords in the preset time period2Representing the maximum times of browsing the target knowledge graph in the historical browsing records of the user within a time period equal to the preset time period; t represents the duration of the preset time period; i iskShowing the page loading time of the target knowledge graph after the user clicks the kth associated knowledge point keyword; o iskRepresenting the browsing time of the target knowledge graph after the user clicks the kth associated knowledge point keyword;
step B2, comparing the interest degree of the user on the target knowledge graph with a preset interest degree threshold value, and confirming that the user has high interest degree on the target knowledge graph when the interest degree is larger than or equal to the preset interest degree threshold value; and otherwise, confirming that the user has low hobby on the target knowledge graph.
9. The system of claim 8, wherein the generating the set of knowledge-graphs of interest to the user based on the user's interest level in the target knowledge-graph comprises:
and adding all target knowledge graphs with high hobby degrees into the knowledge graph set which is interested by the user to form the knowledge graph set which is interested by the user.
10. The system of claim 8, wherein the generating the set of knowledge-graphs of interest to the user based on the user's interest level in the target knowledge-graph comprises:
and sequencing all target knowledge graphs with high hobby degrees from large to small according to the corresponding interestingness to form a sequentially-arranged knowledge graph set which is interesting to the user.
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