CN114238778A - Scientific and technological information recommendation method, device, medium and electronic equipment based on big data - Google Patents
Scientific and technological information recommendation method, device, medium and electronic equipment based on big data Download PDFInfo
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Abstract
The embodiment of the application provides a scientific and technological information recommendation method and device based on big data, a computer readable medium and electronic equipment. The scientific and technological information recommendation method based on big data comprises the following steps: the method comprises the steps of obtaining account attributes, retrieval history and retrieval frequency in account information of a target account, then determining a main recommendation type corresponding to the target account based on the account attributes and the retrieval history, determining a target direction corresponding to content to be recommended based on keywords in the retrieval history, determining a recommendation cycle corresponding to the content to be recommended based on the retrieval frequency, and finally recommending scientific and technical information corresponding to the main recommendation type to the target account in the recommendation cycle based on the target direction corresponding to the content to be recommended. According to the technical scheme, the recommendation type, the target direction and the recommendation period of the scientific and technological information are determined based on the search habit of the target account, then the recommendation of the scientific and technological information is carried out on the target account, and the accuracy of the recommendation of the scientific and technological information and the information utilization rate of the scientific and technological information are improved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a scientific and technological information recommendation method and device based on big data, a computer readable medium and electronic equipment.
Background
At present, in the rapid development of science and technology, the method of recording, collecting, organizing, transmitting, managing and the like of science and technology information and the information technology are used to keep the best efficiency of the process of circulating the science and technology information and the science and technology information system, so that the purpose of applying big data to the information processing of the science and technology information is achieved. But at present when handling scientific and technological information, can't accurate search, the recommendation of carrying out scientific and technological information etc. and then cause the lower problem of utilization ratio of scientific and technological information.
Disclosure of Invention
The embodiment of the application provides a scientific and technological information recommendation method and device based on big data, a computer readable medium and electronic equipment, and further the information utilization rate of scientific and technological information can be improved to at least a certain extent.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.
According to an aspect of the embodiments of the present application, a scientific and technical intelligence recommendation method based on big data is provided, including: obtaining account information of a target account, wherein the account information comprises at least one of the following information: account attributes, retrieval history and retrieval frequency, wherein the account attributes comprise enterprises or scientific research institutions; determining a primary recommendation type corresponding to the target account based on the account attributes and a retrieval history; determining a target direction corresponding to the content to be recommended based on the keywords in the retrieval history; determining a recommendation cycle corresponding to the content to be recommended based on the retrieval frequency; and recommending scientific and technological intelligence corresponding to the main recommendation type to the target account according to the recommendation period based on the target direction corresponding to the content to be recommended.
In some embodiments of the present application, based on the foregoing solution, the obtaining account information of the target account includes: acquiring account attributes of a target account; and acquiring the retrieval history and the retrieval frequency of the target account within the historical time based on the set time period.
In some embodiments of the present application, based on the foregoing scheme, the primary recommendation type includes at least one of: patents, treatises, and web forums; determining a primary recommendation type corresponding to the target account based on the account attributes and the retrieval history, including: determining a target type corresponding to a target account based on the account attribute; determining the preference type of the target account based on the proportion of each type of data in the retrieval history; and determining a main recommendation type based on the weights respectively corresponding to the target type and the preference type.
In some embodiments of the present application, based on the foregoing solution, the determining a target type corresponding to a target account based on the account attribute includes: if the account attribute of the target account is an enterprise, determining that the target type is a patent or a webpage forum; and if the account attribute of the target account is the scientific research institute, determining that the target type is a paper or a webpage forum.
In some embodiments of the present application, based on the foregoing solution, the determining the preference type of the target account based on the proportion of each type of data in the retrieval history includes: acquiring retrieval data quantity corresponding to each retrieval type in retrieval history, and determining the proportion between the retrieval data quantity corresponding to each type and total data quantity; and determining the retrieval type corresponding to the maximum proportion as the preference type of the target account.
In some embodiments of the present application, based on the foregoing solution, the determining a target direction corresponding to a content to be recommended based on a keyword in the search history includes: clustering the keywords in the retrieval history, and determining at least one direction corresponding to the keywords in the retrieval history; a target direction is selected from the at least one direction.
In some embodiments of the present application, based on the foregoing solution, the method further comprises: determining a secondary recommendation type corresponding to the target account based on the account attribute and the retrieval history, wherein the secondary recommendation type comprises at least one of the following items: patents, treatises, and web forums; and recommending scientific and technological intelligence corresponding to the recommendation type to the target account according to the recommendation period based on the target direction corresponding to the content to be recommended.
According to an aspect of the embodiments of the present application, there is provided a scientific and technical information recommendation apparatus based on big data, including:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring account information of a target account, and the account information comprises at least one of the following information: account attributes, retrieval history and retrieval frequency, wherein the account attributes comprise enterprises or scientific research institutions;
the type unit is used for determining a main recommendation type corresponding to the target account based on the account attribute and the retrieval history;
the direction unit is used for determining a target direction corresponding to the content to be recommended based on the keywords in the retrieval history;
the frequency unit is used for determining a recommendation cycle corresponding to the content to be recommended based on the retrieval frequency;
and the recommending unit is used for recommending the scientific and technological intelligence corresponding to the main recommending type to the target account according to the target direction corresponding to the content to be recommended and the recommending period.
In some embodiments of the present application, based on the foregoing solution, the obtaining account information of the target account includes: acquiring account attributes of a target account; and acquiring the retrieval history and the retrieval frequency of the target account within the historical time based on the set time period.
In some embodiments of the present application, based on the foregoing scheme, the primary recommendation type includes at least one of: patents, treatises, and web forums; determining a primary recommendation type corresponding to the target account based on the account attributes and the retrieval history, including: determining a target type corresponding to a target account based on the account attribute; determining the preference type of the target account based on the proportion of each type of data in the retrieval history; and determining a main recommendation type based on the weights respectively corresponding to the target type and the preference type.
In some embodiments of the present application, based on the foregoing solution, the determining a target type corresponding to a target account based on the account attribute includes: if the account attribute of the target account is an enterprise, determining that the target type is a patent or a webpage forum; and if the account attribute of the target account is the scientific research institute, determining that the target type is a paper or a webpage forum.
In some embodiments of the present application, based on the foregoing solution, the determining the preference type of the target account based on the proportion of each type of data in the retrieval history includes: acquiring retrieval data quantity corresponding to each retrieval type in retrieval history, and determining the proportion between the retrieval data quantity corresponding to each type and total data quantity; and determining the retrieval type corresponding to the maximum proportion as the preference type of the target account.
In some embodiments of the present application, based on the foregoing solution, the determining a target direction corresponding to a content to be recommended based on a keyword in the search history includes: clustering the keywords in the retrieval history, and determining at least one direction corresponding to the keywords in the retrieval history; a target direction is selected from the at least one direction.
In some embodiments of the present application, based on the foregoing solution, the method further comprises: determining a secondary recommendation type corresponding to the target account based on the account attribute and the retrieval history, wherein the secondary recommendation type comprises at least one of the following items: patents, treatises, and web forums; and recommending scientific and technological intelligence corresponding to the recommendation type to the target account according to the recommendation period based on the target direction corresponding to the content to be recommended.
According to an aspect of the embodiments of the present application, there is provided a computer-readable medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the big data based technology intelligence recommendation method as described in the above embodiments.
According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the big data based technology intelligence recommendation method as described in the above embodiments.
According to an aspect of embodiments herein, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the big data-based scientific and technical intelligence recommendation method provided in the various optional implementation manners.
In the technical solutions provided in some embodiments of the present application, an account attribute, a retrieval history, and a retrieval frequency in account information of a target account are obtained, a main recommendation type corresponding to the target account is determined based on the account attribute and the retrieval history, a target direction corresponding to content to be recommended is determined based on a keyword in the retrieval history, a recommendation cycle corresponding to the content to be recommended is determined based on the retrieval frequency, and finally, an intelligence technology corresponding to the main recommendation type is recommended to the target account in the recommendation cycle based on the target direction corresponding to the content to be recommended. According to the technical scheme, the recommendation type, the target direction and the recommendation period of the scientific and technological information are determined based on the search habit of the target account, then the recommendation of the scientific and technological information is carried out on the target account, and the accuracy of the recommendation of the scientific and technological information and the information utilization rate of the scientific and technological information are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 schematically shows a flowchart of a big data based scientific intelligence recommendation method according to an embodiment of the present application.
FIG. 2 schematically shows a flow chart for determining a type of primary recommendation according to an embodiment of the application.
Fig. 3 schematically shows a schematic diagram of a big data based scientific intelligence recommendation apparatus according to an embodiment of the present application.
FIG. 4 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The implementation details of the technical solution of the embodiment of the present application are set forth in detail below:
FIG. 1 shows a flow diagram of a big-data based scientific intelligence recommendation method according to an embodiment of the present application. Referring to fig. 1, the scientific and technical intelligence recommendation method based on big data at least includes steps S110 to S150, which are described in detail as follows:
in step S110, account information of a target account is obtained, where the account information includes at least one of the following information: account attributes, retrieval history, and retrieval frequency, wherein the account attributes comprise enterprises or scientific research institutions.
In one embodiment of the application, the preference of the target account is analyzed by acquiring the retrieval history and the retrieval frequency of the user in the retrieval process at ordinary times.
In one embodiment of the present application, the account information includes at least one of the following information: account attributes, search history, and search frequency, where the account information may include account attributes, and in this embodiment, the account attributes include an enterprise or a research institution, or other enterprises.
In one embodiment of the present application, acquiring account information of a target account includes:
acquiring account attributes of a target account;
and acquiring the retrieval history and the retrieval frequency of the target account within the historical time based on the set time period.
The account attribute in this embodiment may be obtained through registration information of the account, the retrieval history and the retrieval frequency in this embodiment may be obtained based on a set time period, and the retrieval history and the retrieval frequency may be information within a certain history time in a history use process. The data simplification is improved and the data volume of redundant data is reduced through the method.
In step S120, a primary recommendation type corresponding to the target account is determined based on the account attributes and the retrieval history.
In one embodiment of the present application, the primary recommendation type includes at least one of: patents, articles, and web forums, in addition to books, etc.
It should be noted that in this embodiment, the data types such as the type, the retrieval type, the target type, the preference type, the primary recommendation type, the secondary recommendation type, and the like all include at least one of the following items: patents, papers, and web forums, among others.
In an embodiment of the present application, the determining a primary recommendation type corresponding to the target account based on the account attribute and the retrieval history in step S120 includes steps S210 to S230:
step S210, determining a target type corresponding to the target account based on the account attribute.
In one embodiment of the application, the target type is used for representing a preference type of the target account in a scientific intelligence retrieval process, and in the embodiment, the corresponding target type is determined based on the account attribute of the target account.
In an embodiment of the present application, determining a target type corresponding to a target account based on the account attribute includes:
if the account attribute of the target account is an enterprise, determining that the target type is a patent or a webpage forum;
and if the account attribute of the target account is the scientific research institute, determining that the target type is a paper or a webpage forum.
In the embodiment, the target types corresponding to the enterprises are determined as patents and web forums, and the target types of the scientific research institutions are set as thesis and web forums, so that the method is close to practical application and improves the information utilization rate.
Step S220, determining the preference type of the target account based on the proportion of each type of data in the retrieval history;
in this embodiment, the determining the preference type of the target account based on the percentage of each retrieval type in the retrieval history specifically is, in an embodiment of the present application, the determining the preference type of the target account based on the percentage of each type of data in the retrieval history, and includes:
acquiring retrieval data quantity corresponding to each retrieval type in retrieval history, and determining the proportion between the retrieval data quantity corresponding to each type and total data quantity;
and determining the retrieval type corresponding to the maximum proportion as the preference type of the target account.
In this embodiment, the retrieval Data amount corresponding to each retrieval type is determined, where the retrieval Data amount corresponding to a patent is Data _ pat, the retrieval Data amount corresponding to a thesis is Data _ pap, the retrieval Data amount corresponding to a web forum is Data _ web, and the total Data amount is Data _ tat. The data amount in this embodiment may be the number of the search entries, the total number of the searched characters, or the like.
The ratio between the retrieval data amount and the total data amount corresponding to each type includes: patent proportion pro _ pat, paper proportion pro _ pap = Data _ pap/Data _ tat, and web forum proportion pro _ web, where:
pro_pat=Data_pat/Data_tat
pro_pap=Data_pap/Data_tat
pro_web=Data_web/Data_tat
in this embodiment, after the proportion between the retrieval data amount corresponding to each type and the total data amount is determined, the maximum proportion therein is determined, and the retrieval type corresponding to the maximum proportion is used as the preference type of the target account.
Step S230, determining a main recommendation type based on the weights respectively corresponding to the target type and the preference type.
In an embodiment of the present application, weights corresponding to the target type and the preference type are set based on the target type and the preference type, respectively, and in this embodiment, a target weight corresponding to the target type and a preference weight corresponding to the preference type are set as follows. Multiplying the proportion corresponding to the target type by the corresponding weight to obtain a target parameter; and multiplying the proportion corresponding to the preference type by the corresponding weight beta to obtain the preference parameter. And finally, aiming at one type, adding all parameters belonging to the type to obtain a total recommended parameter corresponding to the type, and taking the type corresponding to the maximum recommended parameter as a main recommended type.
According to the method, the target type and the user preference type determined based on the account attribute are analyzed and comprehensively calculated to obtain the main recommendation type corresponding to the user account, so that the accuracy of information recommendation is improved.
In step S130, a target direction corresponding to the content to be recommended is determined based on the keywords in the search history.
In one embodiment of the application, the corresponding target direction is determined based on the keywords in the retrieval history, so that corresponding information is recommended to the target account based on the direction.
In an embodiment of the present application, determining a target direction corresponding to a content to be recommended based on a keyword in the search history includes:
clustering the keywords in the retrieval history, and determining at least one direction corresponding to the keywords in the retrieval history;
a target direction is selected from the at least one direction.
In an embodiment of the present application, clustering analysis may be performed on keywords in the search history in a clustering manner, so as to obtain directions corresponding to the keywords, in the clustering manner in this embodiment, similarity between word vectors may be calculated in a word vector manner, the keywords with similarity smaller than a set threshold are used as mutually similar keywords and used as the same type of keywords, and the keywords are classified in this manner, so as to determine a group corresponding to each type of keyword, and determine a group name based on the keywords in the group, and used as a target direction corresponding to the content to be recommended.
In step S140, a recommendation cycle corresponding to the content to be recommended is determined based on the retrieval frequency.
In one embodiment of the application, after the retrieval frequency is obtained, the recommendation period is determined based on the retrieval frequency.
For example, in this embodiment, the retrieval frequency in a certain historical time Tim _ len is Fre _ ret, in this embodiment, the number of retrieval times in a unit time is determined first, and then the recommendation period is determined to be Ro _ cyc based on the number of retrieval times in the unit time:
wherein epsilon represents a preset recommendation factor, and the unit of the historical time Tim _ len is day.
In step S150, technology intelligence corresponding to the main recommendation type is recommended to the target account in the recommendation cycle based on the target direction corresponding to the content to be recommended.
In an embodiment of the application, after a target direction corresponding to content to be recommended is determined and the recommendation period is used, scientific and technical intelligence corresponding to the main recommendation type is recommended to the target account based on the information. By the method, scientific and technical information is recommended to the user on the basis of determining the recommendation direction, the recommendation period and the recommendation type, and the success rate of information recommendation is improved.
In one embodiment of the present application, the method further comprises: determining a secondary recommendation type corresponding to the target account based on the account attribute and the retrieval history, where the secondary recommendation type represents a recommendation type after the weight of the primary recommendation type in this embodiment, and includes at least one of the following items: patents, treatises, and web forums; and recommending scientific and technological intelligence corresponding to the recommendation type to the target account according to the recommendation period based on the target direction corresponding to the content to be recommended.
In the technical solutions provided in some embodiments of the present application, an account attribute, a retrieval history, and a retrieval frequency in account information of a target account are obtained, a main recommendation type corresponding to the target account is determined based on the account attribute and the retrieval history, a target direction corresponding to content to be recommended is determined based on a keyword in the retrieval history, a recommendation cycle corresponding to the content to be recommended is determined based on the retrieval frequency, and finally, an intelligence technology corresponding to the main recommendation type is recommended to the target account in the recommendation cycle based on the target direction corresponding to the content to be recommended. According to the technical scheme, the recommendation type, the target direction and the recommendation period of the scientific and technological information are determined based on the search habit of the target account, then the recommendation of the scientific and technological information is carried out on the target account, and the accuracy of the recommendation of the scientific and technological information and the information utilization rate of the scientific and technological information are improved.
The following describes an embodiment of an apparatus of the present application, which can be used to execute the big data based technology information recommendation method in the above embodiment of the present application. It will be appreciated that the apparatus may be a computer program (comprising program code) running on a computer device, for example an application software; the apparatus may be used to perform the corresponding steps in the methods provided by the embodiments of the present application. For the details that are not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the scientific and technical information recommendation method based on big data described above in the present application.
FIG. 3 shows a block diagram of a big-data based scientific intelligence recommendation apparatus according to an embodiment of the present application.
Referring to fig. 3, a big data-based scientific and technical intelligence recommendation apparatus 300 according to an embodiment of the present application includes:
an obtaining unit 310, configured to obtain account information of a target account, where the account information includes at least one of the following information: account attributes, retrieval history and retrieval frequency, wherein the account attributes comprise enterprises or scientific research institutions;
a type unit 320, configured to determine a primary recommendation type corresponding to the target account based on the account attribute and the retrieval history;
a direction unit 330, configured to determine a target direction corresponding to a content to be recommended based on the keyword in the search history;
a frequency unit 340, configured to determine, based on the retrieval frequency, a recommendation period corresponding to the content to be recommended;
and the recommending unit 350 is configured to recommend scientific and technical intelligence corresponding to the main recommendation type to the target account according to the recommendation cycle based on the target direction corresponding to the content to be recommended.
In some embodiments of the present application, based on the foregoing solution, the obtaining account information of the target account includes: acquiring account attributes of a target account; and acquiring the retrieval history and the retrieval frequency of the target account within the historical time based on the set time period.
In some embodiments of the present application, based on the foregoing scheme, the primary recommendation type includes at least one of: patents, treatises, and web forums; determining a primary recommendation type corresponding to the target account based on the account attributes and the retrieval history, including: determining a target type corresponding to a target account based on the account attribute; determining the preference type of the target account based on the proportion of each type of data in the retrieval history; and determining a main recommendation type based on the weights respectively corresponding to the target type and the preference type.
In some embodiments of the present application, based on the foregoing solution, the determining a target type corresponding to a target account based on the account attribute includes: if the account attribute of the target account is an enterprise, determining that the target type is a patent or a webpage forum; and if the account attribute of the target account is the scientific research institute, determining that the target type is a paper or a webpage forum.
In some embodiments of the present application, based on the foregoing solution, the determining the preference type of the target account based on the proportion of each type of data in the retrieval history includes: acquiring retrieval data quantity corresponding to each retrieval type in retrieval history, and determining the proportion between the retrieval data quantity corresponding to each type and total data quantity; and determining the retrieval type corresponding to the maximum proportion as the preference type of the target account.
In some embodiments of the present application, based on the foregoing solution, the determining a target direction corresponding to a content to be recommended based on a keyword in the search history includes: clustering the keywords in the retrieval history, and determining at least one direction corresponding to the keywords in the retrieval history; a target direction is selected from the at least one direction.
In some embodiments of the present application, based on the foregoing solution, the method further comprises: determining a secondary recommendation type corresponding to the target account based on the account attribute and the retrieval history, wherein the secondary recommendation type comprises at least one of the following items: patents, treatises, and web forums; and recommending scientific and technological intelligence corresponding to the recommendation type to the target account according to the recommendation period based on the target direction corresponding to the content to be recommended.
In the technical solutions provided in some embodiments of the present application, an account attribute, a retrieval history, and a retrieval frequency in account information of a target account are obtained, a main recommendation type corresponding to the target account is determined based on the account attribute and the retrieval history, a target direction corresponding to content to be recommended is determined based on a keyword in the retrieval history, a recommendation cycle corresponding to the content to be recommended is determined based on the retrieval frequency, and finally, an intelligence technology corresponding to the main recommendation type is recommended to the target account in the recommendation cycle based on the target direction corresponding to the content to be recommended. According to the technical scheme, the recommendation type, the target direction and the recommendation period of the scientific and technological information are determined based on the search habit of the target account, then the recommendation of the scientific and technological information is carried out on the target account, and the accuracy of the recommendation of the scientific and technological information and the information utilization rate of the scientific and technological information are improved.
FIG. 4 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
It should be noted that the computer system 400 of the electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments.
As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An Input/Output (I/O) interface 405 is also connected to the bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a Display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 401.
It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with a computer program embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
According to an aspect of the application, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method provided in the various alternative implementations described above.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method described in the above embodiments.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A scientific and technological information recommendation method based on big data is characterized by comprising the following steps:
obtaining account information of a target account, wherein the account information comprises at least one of the following information: account attributes, retrieval history and retrieval frequency, wherein the account attributes comprise enterprises or scientific research institutions;
determining a primary recommendation type corresponding to the target account based on the account attributes and a retrieval history;
determining a target direction corresponding to the content to be recommended based on the keywords in the retrieval history;
determining a recommendation cycle corresponding to the content to be recommended based on the retrieval frequency;
and recommending scientific and technological intelligence corresponding to the main recommendation type to the target account according to the recommendation period based on the target direction corresponding to the content to be recommended.
2. The method of claim 1, wherein obtaining account information for the target account comprises:
acquiring account attributes of a target account;
and acquiring the retrieval history and the retrieval frequency of the target account within the historical time based on the set time period.
3. The method of claim 1, wherein the primary recommendation type includes at least one of: patents, treatises, and web forums;
determining a primary recommendation type corresponding to the target account based on the account attributes and the retrieval history, including:
determining a target type corresponding to a target account based on the account attribute;
determining the preference type of the target account based on the proportion of each type of data in the retrieval history;
and determining a main recommendation type based on the weights respectively corresponding to the target type and the preference type.
4. The method of claim 3, wherein determining a target type corresponding to a target account based on the account attributes comprises:
if the account attribute of the target account is an enterprise, determining that the target type is a patent or a webpage forum;
and if the account attribute of the target account is the scientific research institute, determining that the target type is a paper or a webpage forum.
5. The method of claim 3, wherein determining the preferred type of the target account based on the percentage of each type of data in the retrieval history comprises:
acquiring retrieval data quantity corresponding to each retrieval type in retrieval history, and determining the proportion between the retrieval data quantity corresponding to each type and total data quantity;
and determining the retrieval type corresponding to the maximum proportion as the preference type of the target account.
6. The method of claim 1, wherein determining a target direction corresponding to the content to be recommended based on the keywords in the search history comprises:
clustering the keywords in the retrieval history, and determining at least one direction corresponding to the keywords in the retrieval history;
a target direction is selected from the at least one direction.
7. The method of claim 1, further comprising:
determining a secondary recommendation type corresponding to the target account based on the account attribute and the retrieval history, wherein the secondary recommendation type comprises at least one of the following items: patents, treatises, and web forums;
and recommending scientific and technological intelligence corresponding to the recommendation type to the target account according to the recommendation period based on the target direction corresponding to the content to be recommended.
8. The utility model provides a science and technology intelligence recommendation device based on big data which characterized in that includes:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring account information of a target account, and the account information comprises at least one of the following information: account attributes, retrieval history and retrieval frequency, wherein the account attributes comprise enterprises or scientific research institutions;
the type unit is used for determining a main recommendation type corresponding to the target account based on the account attribute and the retrieval history;
the direction unit is used for determining a target direction corresponding to the content to be recommended based on the keywords in the retrieval history;
the frequency unit is used for determining a recommendation cycle corresponding to the content to be recommended based on the retrieval frequency;
and the recommending unit is used for recommending the scientific and technological intelligence corresponding to the main recommending type to the target account according to the target direction corresponding to the content to be recommended and the recommending period.
9. A computer-readable medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the big data based technology intelligence recommendation method according to any one of claims 1 to 7.
10. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the big data based technology intelligence recommendation method of any of claims 1-7.
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