CN111008330A - Expert recommendation method and system based on multiple data sources - Google Patents
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Abstract
The embodiment of the invention provides an expert recommendation method and system based on multiple data sources, wherein the method comprises the following steps: acquiring expert data of a plurality of data sources, wherein the expert data comprises an expert name field and an expert affiliated institution field; and adding a score field to each expert data, and sequencing the expert data from high to low according to the numerical value corresponding to the socre field so as to obtain an expert recommendation result according to the sequenced expert data. According to the embodiment of the invention, the score field is added to the expert data from multiple data sources, and the expert data is sequenced according to the values corresponding to the score field, so that the expert data sequencing based on the multiple data sources is more reasonable, the accuracy of the expert data sequencing is improved, and the subsequent expert recommendation result is more comprehensive.
Description
Technical Field
The invention relates to the technical field of internet, in particular to an expert recommendation method and system based on multiple data sources.
Background
Expert recommendation is a very important function in academic search and can be applied to different situations, such as enterprise expert docking, academic review or academic exchange, and the like, so that the expert recommendation needs to have an efficient and accurate expert recommendation algorithm so as to construct a stable and reliable expert recommendation system. At present, expert recommendation based on a single data source is limited by the single data source, so that expert recommendation effect is closely related to the quality of the data source, when the data source is incomplete, the expert recommendation effect is poor, in addition, the expert recommendation of the single data source often faces the problems of low data coverage and low integrity, and specific expert information cannot be searched.
The conventional expert recommendation mostly adopts the expert recommendation of multiple data sources, and the problems of small data coverage and poor expert recommendation effect in single-source data search are solved by integrating the contents of multiple source databases. However, the existing expert recommendation system based on multiple data sources also faces many problems, under the precondition that multiple databases cannot be directly merged, multi-source sequencing needs to be performed on expert data of different data sources, and the existing expert recommendation system based on multiple data sources has the problems of unreasonable expert data sequencing, low sequencing accuracy and the like.
Therefore, there is a need for an expert recommendation method and system based on multiple data sources to solve the above problems.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides an expert recommendation method and system based on multiple data sources.
In a first aspect, an embodiment of the present invention provides an expert recommendation method based on multiple data sources, including:
acquiring expert data of a plurality of data sources, wherein the expert data comprises an expert name field and an expert affiliated institution field;
and adding a score field to each expert data, and sequencing the expert data from high to low according to the numerical value corresponding to the socre field so as to obtain an expert recommendation result according to the sequenced expert data.
Further, the expert data further includes: thesis information, patent information, partner information, and project information.
Further, the adding a score field to each expert data includes:
judging whether the h-index exists in the expert data or not, and if yes, acquiring the h-index of the expert data;
taking h-index as score, adding score field for corresponding expert data.
Further, the determining whether the h-index exists in the expert data further includes:
if the h-index does not exist in the expert data, judging whether the thesis information exists in the expert data, if so, sorting the thesis according to the thesis citation times corresponding to the thesis information, and calculating the score field corresponding to the expert data according to the thesis sorting result.
Further, the sorting the papers according to the paper reference times corresponding to the paper information, and calculating the score field corresponding to the expert data according to the paper sorting result includes:
according to the reference times of the papers, sorting the papers in the expert data from high to low, and marking the serial number of each paper according to the serial numbers from 1 to N, wherein N represents a positive integer;
if the marking sequence number N is larger than the number of times of being referred to of the corresponding paper, taking N-1 as the score field of the expert data, and N is equal to N.
Further, before adding the score field to each piece of expert data and sorting the expert data from high to low according to the value corresponding to the secret field, the method further includes:
acquiring a plurality of pieces of expert data with the same expert name field and the same expert mechanism field, and acquiring a paper corresponding to the highest reference frequency in the paper information of the plurality of pieces of expert data;
and judging the paper corresponding to the highest reference frequency, and if the paper corresponding to the highest reference frequency is judged to be the same paper, performing duplicate removal processing on a plurality of expert data.
In a second aspect, an embodiment of the present invention provides an expert recommendation system based on multiple data sources, including:
the expert data acquisition module is used for acquiring expert data of a plurality of data sources, wherein the expert data comprises an expert name field and an expert affiliated mechanism field;
and the expert data processing module is used for adding a score field to each piece of expert data, sequencing the expert data from high to low according to a numerical value corresponding to the secret field, and acquiring an expert recommendation result according to the sequenced expert data.
Further, the system further comprises:
the system comprises a homonymous expert data acquisition module, a name matching module and a name matching module, wherein the homonymous expert data acquisition module is used for acquiring a plurality of expert data with the same expert name field and the same expert mechanism field to which an expert belongs and acquiring a paper corresponding to the highest reference frequency in the paper information of the plurality of expert data;
and the homonymy duplicate removal module is used for judging the paper corresponding to the highest citation frequency, and if the paper corresponding to the highest citation frequency is judged to be the same paper, carrying out duplicate removal processing on a plurality of expert data.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method provided in the first aspect when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method as provided in the first aspect.
According to the expert recommendation method and system based on multiple data sources, provided by the embodiment of the invention, the score field is added to the expert data from the multiple data sources, and the expert data is sequenced according to the values corresponding to the score field, so that the expert data sequencing based on the multiple data sources is more reasonable, the accuracy of the expert data sequencing is improved, and the subsequent expert recommendation result is more comprehensive.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of an expert recommendation method based on multiple data sources according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an expert recommendation system based on multiple data sources according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Aiming at the problems in the conventional multi-data-source expert recommendation system, the embodiment of the invention calculates the scores of different experts by accessing a plurality of expert databases and utilizing an algorithm, searches according to the expert score sorting, and performs duplicate removal processing on the same expert in the searching process, thereby solving the problems of the conventional multi-data-source expert recommendation system in multi-source sorting and multi-source duplicate removal. The multi-source de-duplication means that: the experts from multiple sources may be the same person, and duplicate removal is performed on the same expert under the condition that a certain accuracy is guaranteed.
Fig. 1 is a schematic flowchart of an expert recommendation method based on multiple data sources according to an embodiment of the present invention, and as shown in fig. 1, an expert recommendation method based on multiple data sources according to an embodiment of the present invention includes:
and 102, adding a score field to each piece of expert data, sequencing the expert data from high to low according to a numerical value corresponding to the score field, and acquiring an expert recommendation result according to the sequenced expert data.
In the embodiment of the invention, expert data from different data sources need to be sequenced, namely, multi-source sequencing is performed, wherein the multi-source sequencing refers to the following steps: the information of various different sources needs to be subjected to priority ranking in an expert recommendation system, ranking scores of expert data of the different sources are calculated through a specified algorithm, and ranking is carried out according to the ranking scores. In the embodiment of the present invention, the fields of each database may be different from each other in the databases of different sources, but it is determined that the expert data in these databases at least includes the name field name of the expert and the organization field org to which the expert belongs, and on the basis of the above embodiment, the expert data further includes: the system comprises paper information, patent information, collaborator information and project information, wherein the paper information comprises but is not limited to a paper title, a paper citation number, author information and the like, so that multi-source sequencing is performed more accurately. Specifically, in an embodiment of the present invention, a score field may be added to each piece of expert data according to the thesis information, and if the thesis information does not exist in the expert data, the score of the expert data is marked as 0; if the name field name of the expert in the expert data is the same as the org field of the organization to which the expert belongs, the expert data is represented as different experts with the same name in the same organization, and at the moment, whether the expert data belongs to the same expert in the same organization or not can be judged according to the thesis information with the highest reference times.
Further, according to the search keywords and the request data source sent by the user terminal, the corresponding search keywords are converted into SQL statements, expert data ordering of the request data source is obtained, and corresponding expert recommendation results are obtained according to the SQL statements. Wherein, the search keyword includes: expert names, expert institutions, expert research areas, patents, projects, and the like.
On the basis of the above embodiment, the adding a score field to each expert data includes:
judging whether the h-index exists in the expert data or not, and if yes, acquiring the h-index of the expert data;
taking h-index as score, adding score field for corresponding expert data.
In the embodiment of the invention, if the expert data comprises the h-index, the numerical value corresponding to the h-index of the expert data is used as the score value, and the score field is added to the expert data according to the score value. Further, after the score fields are added to the expert data of different data sources, the expert data are sorted according to the size of the score, so that the sorted expert data are provided for the expert recommendation system, and a user can obtain more accurate expert recommendation.
According to the expert recommendation method based on multiple data sources, the score field is added to the expert data from the multiple data sources, and the expert data are sequenced according to the values corresponding to the score field, so that the expert data based on the multiple data sources are sequenced more reasonably, the accuracy of expert data sequencing is improved, and the subsequent expert recommendation result is more comprehensive.
On the basis of the above embodiment, the determining whether the h-index exists in the expert data further includes:
if the h-index does not exist in the expert data, judging whether the thesis information exists in the expert data, if so, sorting the thesis according to the thesis citation times corresponding to the thesis information, and calculating the score field corresponding to the expert data according to the thesis sorting result.
On the basis of the above embodiment, the sorting the papers according to the paper reference times corresponding to the paper information, and calculating the score field corresponding to the expert data according to the paper sorting result includes:
according to the reference times of the papers, sorting the papers in the expert data from high to low, and marking the serial number of each paper according to the serial numbers from 1 to N, wherein N represents a positive integer;
if the marking sequence number N is larger than the number of times of being referred to of the corresponding paper, taking N-1 as the score field of the expert data, and N is equal to N.
In the embodiment of the invention, for the expert data which does not have h-index and contains the thesis information, the score field is added to the expert data by the following method: firstly, obtaining the reference frequency information of each paper in the expert data, sorting the papers from high to low according to the reference frequency, simultaneously labeling each paper from 1 according to the sorting, and subtracting 1 from the labeled sequence number to be used as the score field of the expert data when the labeled sequence number is greater than the reference frequency of the corresponding paper. For example, if a piece of expert data includes 5 papers, the number of times of reference is 5, 4, 3, 2, and 1, the number of the paper referencing 5 is marked as 1, the number of the paper referencing 4 is marked as 2, and so on, the number of times of reference is 5, 4, 3, 2, and 1, the corresponding numbers are 1, 2, 3, 4, and 5 in sequence, and thus the number of the paper with the number of reference 4 is greater than the number of times of reference 2, and thus the score value of the piece of expert data is 3; in addition, when the sequence number of the paper is marked, if the marked sequence number is not greater than the reference times of the corresponding paper, the score value of the expert data is directly selected by subtracting 1 from the maximum sequence number.
On the basis of the above embodiment, before adding the score field to each piece of expert data and sorting the expert data from high to low according to the value corresponding to the socre field, the method further includes:
acquiring a plurality of pieces of expert data with the same expert name field and the same expert mechanism field, and acquiring a paper corresponding to the highest reference frequency in the paper information of the plurality of pieces of expert data;
and judging the paper corresponding to the highest reference frequency, and if the paper corresponding to the highest reference frequency is judged to be the same paper, performing duplicate removal processing on a plurality of expert data.
In the embodiment of the invention, according to the search keyword and the request data source sent by the user terminal, the corresponding search keyword is converted into the SQL sentence, and meanwhile, the expert data ordering of the request data source is obtained, so that the corresponding initial expert recommendation result is obtained according to the SQL sentence; then, formatting the initial expert recommendation result into a uniform form, and sorting the initial expert recommendation result according to the order of the names of the experts from high to low, wherein the formatted expert data comprises the names, organizations, score and data sources of the experts, and optionally, in the embodiment of the invention, a paper list, an item list, a patent list and the like can also be included.
Further, for a plurality of expert data with completely consistent names and institutions, judging according to a paper with the highest reference frequency in the plurality of expert data, if the paper with the highest reference frequency is the same paper, judging that the expert data is the same data, for example, explaining by taking three expert data A, B and C as examples, if both A, B and C contain paper information, comparing the paper with the highest reference frequency in a paper list, if the papers are the same, judging A, B and C as the same expert, performing deduplication processing on the three expert data, keeping the expert data with the highest score, and marking the data source of the expert data as the sum of the three (that is, the data source of the expert data with the highest score can be the data source corresponding to the three expert data A, B and C); if there is some expert data in A, B and C without paper information, the expert data is defaulted as duplicate data and merged with other expert data with paper information. After the expert data is subjected to the duplicate removal processing through the embodiment, the expert data is sorted from high to low according to score, so that the expert recommendation result is obtained according to the sorted expert data.
Fig. 2 is a schematic structural diagram of an expert recommendation system based on multiple data sources according to an embodiment of the present invention, and as shown in fig. 2, an expert recommendation system based on multiple data sources according to an embodiment of the present invention includes an expert data acquisition module 201 and an expert data processing module 202, where the expert data acquisition module 201 is configured to acquire expert data of multiple data sources, and the expert data includes an expert name field and an organization field to which an expert belongs; the expert data processing module 202 is configured to add a score field to each piece of expert data, and sort the expert data from high to low according to a value corresponding to the secret field, so as to obtain an expert recommendation result according to the sorted expert data.
According to the expert recommendation system based on the multiple data sources, the score fields are added to the expert data from the multiple data sources, and the expert data are sequenced according to the values corresponding to the score fields, so that the expert data sequencing based on the multiple data sources is more reasonable, the accuracy of the expert data sequencing is improved, and the subsequent expert recommendation result is more comprehensive.
On the basis of the embodiment, the system further comprises a same-name expert data acquisition module and a same-name deduplication module, wherein the same-name expert data acquisition module is used for acquiring a plurality of pieces of expert data with the same expert name field and the same expert mechanism field, and acquiring a paper corresponding to the highest reference frequency in the paper information of the plurality of pieces of expert data; and the duplicate removal module with the same name is used for judging the paper corresponding to the highest citation frequency, and if the paper corresponding to the highest citation frequency is judged to be the same paper, the duplicate removal processing is carried out on the plurality of expert data.
The system provided by the embodiment of the present invention is used for executing the above method embodiments, and for details of the process and the details, reference is made to the above embodiments, which are not described herein again.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and referring to fig. 3, the electronic device may include: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a communication bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the communication bus 304. Processor 301 may call logic instructions in memory 303 to perform the following method: acquiring expert data of a plurality of data sources, wherein the expert data comprises an expert name field and an expert affiliated institution field; and adding a score field to each expert data, and sequencing the expert data from high to low according to the numerical value corresponding to the socre field so as to obtain an expert recommendation result according to the sequenced expert data.
In addition, the logic instructions in the memory 303 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the multi-data-source-based expert recommendation method provided in the foregoing embodiments, for example, the method includes: acquiring expert data of a plurality of data sources, wherein the expert data comprises an expert name field and an expert affiliated institution field; and adding a score field to each expert data, and sequencing the expert data from high to low according to the numerical value corresponding to the socre field so as to obtain an expert recommendation result according to the sequenced expert data.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An expert recommendation method based on multiple data sources is characterized by comprising the following steps:
acquiring expert data of a plurality of data sources, wherein the expert data comprises an expert name field and an expert affiliated institution field;
and adding a score field to each expert data, and sequencing the expert data from high to low according to the numerical value corresponding to the socre field so as to obtain an expert recommendation result according to the sequenced expert data.
2. The multi-data-source-based expert recommendation method of claim 1, wherein the expert data further comprises: thesis information, patent information, partner information, and project information.
3. The multi-data-source-based expert recommendation method of claim 2, wherein said adding a score field to each expert data comprises:
judging whether the h-index exists in the expert data or not, and if yes, acquiring the h-index of the expert data;
taking h-index as score, adding score field for corresponding expert data.
4. The multi-data-source-based expert recommendation method of claim 3, wherein the determining whether the h-index exists in the expert data further comprises:
if the h-index does not exist in the expert data, judging whether the thesis information exists in the expert data, if so, sorting the thesis according to the thesis citation times corresponding to the thesis information, and calculating the score field corresponding to the expert data according to the thesis sorting result.
5. The expert recommendation method based on multiple data sources as claimed in claim 4, wherein said sorting papers according to paper citation times corresponding to paper information and calculating score fields corresponding to expert data according to paper sorting results comprises:
according to the reference times of the papers, sorting the papers in the expert data from high to low, and marking the serial number of each paper according to the serial numbers from 1 to N, wherein N represents a positive integer;
if the marking sequence number N is larger than the number of times of being referred to of the corresponding paper, taking N-1 as the score field of the expert data, and N is equal to N.
6. The multi-data-source-based expert recommendation method as claimed in claim 2, wherein before adding score field to each expert data and sorting the expert data from high to low according to the value corresponding to the socre field, the method further comprises:
acquiring a plurality of pieces of expert data with the same expert name field and the same expert mechanism field, and acquiring a paper corresponding to the highest reference frequency in the paper information of the plurality of pieces of expert data;
and judging the paper corresponding to the highest reference frequency, and if the paper corresponding to the highest reference frequency is judged to be the same paper, performing duplicate removal processing on a plurality of expert data.
7. An expert recommendation system based on multiple data sources, comprising:
the expert data acquisition module is used for acquiring expert data of a plurality of data sources, wherein the expert data comprises an expert name field and an expert affiliated mechanism field;
and the expert data processing module is used for adding a score field to each piece of expert data, sequencing the expert data from high to low according to a numerical value corresponding to the secret field, and acquiring an expert recommendation result according to the sequenced expert data.
8. The expert recommendation system for multiple data sources of claim 7 wherein said system further comprises:
the system comprises a homonymous expert data acquisition module, a name matching module and a name matching module, wherein the homonymous expert data acquisition module is used for acquiring a plurality of expert data with the same expert name field and the same expert mechanism field to which an expert belongs and acquiring a paper corresponding to the highest reference frequency in the paper information of the plurality of expert data;
and the homonymy duplicate removal module is used for judging the paper corresponding to the highest citation frequency, and if the paper corresponding to the highest citation frequency is judged to be the same paper, carrying out duplicate removal processing on a plurality of expert data.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the multi-data-source-based expert recommendation method as claimed in any one of claims 1 to 6.
10. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of the multi-data source based expert recommendation method as claimed in any one of claims 1 to 6.
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Application publication date: 20200414 |