CN108897860B - Information pushing method and device, electronic equipment and computer readable storage medium - Google Patents
Information pushing method and device, electronic equipment and computer readable storage medium Download PDFInfo
- Publication number
- CN108897860B CN108897860B CN201810699428.8A CN201810699428A CN108897860B CN 108897860 B CN108897860 B CN 108897860B CN 201810699428 A CN201810699428 A CN 201810699428A CN 108897860 B CN108897860 B CN 108897860B
- Authority
- CN
- China
- Prior art keywords
- processed
- related information
- text data
- information
- preset
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The application discloses an information pushing method, an information pushing device, electronic equipment and a computer readable storage medium, wherein the information pushing method comprises the following steps: acquiring related information of an object to be processed; screening out at least one target object matched with the object to be processed based on the related information of the object to be processed; and pushing the relevant information of the screened at least one target object. According to the method and the device, the related information of the object to be processed is accurately pushed.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to an information pushing method and apparatus, an electronic device, and a computer-readable storage medium.
Background
Academic collaboration recommendation is a method for recommending researchers in the concerned field for scientific research managers or researchers by using the existing scientific and technological information resources such as papers and patents, and can help the researchers and researchers to quickly discover and understand the researchers and research contents related to the field, promote further communication collaboration, bring better knowledge and resource sharing, improve the scientific research quality, accelerate the scientific research progress and obtain larger scientific research results.
Academic collaboration is divided into two categories, namely overall collaboration and complementary collaboration, wherein the former refers to close collaboration among individuals in the whole collaboration process, mutual verification is mutually assumed, mutual respect, trust and common development are realized, and the collaborators generally belong to the common research field; the latter emphasizes the complementarity of knowledge or skills among each other, and cross-discipline collaboration is largely of this type.
For global collaboration, i.e., collaboration between scholars in the same or similar areas of research. The recommendation method of the cooperation is usually based on scientific research papers or patent data, and mainly comprises two recommendation methods based on the cooperative relationship of authors (inventors) and the research content of authors (inventors), wherein the former emphasizes the similarity of social relationship, the recommended partners often know and understand each other and are easy to generate practical cooperation, and the problem is that the cooperation of researchers with different backgrounds cannot be effectively promoted, and the latter can better avoid the recommendation problem caused by the social relationship based on the research content.
The method based on the author's cooperative relationship generally uses authors as nodes and the cooperative relationship among authors as edges to establish an author's cooperative network, and uses a social network analysis method, especially a link prediction method, to predict the possibility of generating a continuous edge in the future for the node that is not connected with an edge currently according to various indexes in the network, and to recommend the node according to the prediction result, and the basic flow is shown in fig. 1. However, the method is generally suitable for connected networks, the prediction effect on disconnected networks is relatively poor, and the recommended scientific researchers are not necessarily in the same research field and have limited help to researchers based on the real social relationship among the scientific researchers.
The content-based method is mainly based on professional knowledge content of scientific researchers, and recommendation is performed by using technologies such as data mining and text mining. The basic idea of the recommendation method is that a high-frequency word or keyword construction model is used for representing the research content of an author from the text information representation of scientific research results published by scientific researchers, and then the similarity of the research among the authors is obtained, so that recommendation is performed according to the similarity. The research content of an author is characterized mainly by applying TF-IDF or LDA topic model and the like from a keyword level, and the basic flow is shown in FIG. 2. However, the method usually needs to extract keywords or high-frequency words first and then recommend the words by using TF-IDF and the like, the method has less concern on semantic features, the selected keywords can not necessarily well represent the content of the article, and the recommendation effect can be influenced by the number of the keywords and the selection of stop words; the LDA (document topic Allocation) topic model needs to set different topic numbers in advance, selects an optimal value through multiple operations, and requires multiple iterations for each operation, which takes a long time. For different fields, the method needs to reselect different keywords or reconstruct a new model, and is generally high in complexity, low in efficiency and not suitable for processing large-scale data.
Disclosure of Invention
The application provides an information pushing method, an information pushing device, electronic equipment and a computer readable storage medium, so as to realize accurate pushing of relevant information of an object to be processed.
In a first aspect, the present application provides an information pushing method, including:
acquiring related information of an object to be processed;
screening out at least one target object matched with the object to be processed based on the related information of the object to be processed;
and pushing the relevant information of the screened at least one target object.
In a second aspect, the present application provides an information pushing apparatus, including:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring related information of an object to be processed;
the screening unit is used for screening out at least one target object matched with the object to be processed based on the relevant information of the object to be processed;
and the pushing unit is used for pushing the relevant information of the screened at least one target object.
In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the information pushing method described above.
In a fourth aspect, the present application provides an electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface are communicated with each other through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the information pushing method.
Compared with the prior art, the method has the advantages that:
acquiring relevant information of an object to be processed; screening out at least one target object matched with the object to be processed based on the acquired relevant information of the object to be processed; and then the related information of the screened at least one target object is pushed, so that the related information of the object to be processed is accurately pushed.
Drawings
FIG. 1 is a flow chart of a method for academic collaboration recommendation based on collaboration provided in the prior art of the present application;
FIG. 2 is a flow chart of a research content based academic collaboration recommendation method provided in the prior art of the present application;
FIG. 3 is a flow chart of an information pushing method provided by an embodiment of the present application;
fig. 4 is a schematic processing flow diagram of an information pushing method provided in an embodiment of the present application;
fig. 5 is a schematic flowchart of an information pushing method according to an embodiment of the present application;
FIG. 6 is a schematic flow chart of scientific research personnel library construction provided in the embodiments of the present application;
FIG. 7 is a flowchart illustrating a sparse and distributed representation generation process for text according to an embodiment of the present disclosure;
FIG. 8 is a schematic flow chart of scientific research personnel feature library construction provided in the embodiments of the present application;
FIG. 9 is a schematic flow chart of academic collaboration recommendation provided by embodiments of the present application;
FIG. 10 is a schematic structural diagram of an information pushing apparatus provided in an embodiment of the present application;
fig. 11 is a schematic structural diagram of an electronic device based on a data pushing method according to an embodiment of the present application.
Detailed Description
The present application provides an information pushing method, an information pushing apparatus, and a storage medium, and the following describes in detail a specific embodiment of the present application with reference to the accompanying drawings.
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
It will be understood by those within the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
As shown in fig. 3, a schematic flow chart of an information pushing method provided by the present application is shown, where the method includes the following steps:
step S301, acquiring relevant information of an object to be processed;
step S302, screening out at least one target object matched with the object to be processed based on the related information of the object to be processed;
step S303, pushing the relevant information of the screened at least one target object.
In the embodiment, the relevant information of the object to be processed is obtained; screening out at least one target object matched with the object to be processed based on the acquired relevant information of the object to be processed; and then the related information of the screened at least one target object is pushed, so that the related information of the object to be processed is accurately pushed.
Based on the technical solution provided by the embodiment of the present application, a detailed explanation is given below on the technical solution by using a specific embodiment, as shown in fig. 4, for a specific processing flow chart of the information push method provided by the embodiment of the present application, the method includes:
step S401, acquiring relevant information of the object to be processed.
In this step, obtaining the relevant information of the object to be processed includes:
acquiring the identity identification information of the object to be processed;
and inquiring a preset object feature database based on the identity identification information to acquire the relevant information of the object to be processed.
Step S402, determining a similarity value based on the relevant information of the object to be processed.
In this step, determining a similarity value based on the related information of the object to be processed includes:
and traversing the object feature database based on the related information of the object to be processed, and determining the similarity value of the related information of the object to be processed and the related information corresponding to each object in the object feature database.
Step S403, screening out at least one target object matching the object to be processed based on the determined similarity value.
In this step, the screening process may include any one of the following methods:
(1) screening the determined similarity values based on a preset similarity threshold value;
and selecting objects respectively corresponding to the similarity values larger than a preset similarity threshold value as target objects.
In the method, a preset similarity threshold is configured, and the magnitude relation between each similarity value and the similarity threshold is determined based on the comparison between the configured preset similarity threshold and each determined similarity value; and screening out the similarity values larger than the preset similarity threshold value, and determining the object corresponding to each screened similarity value as the target object.
(2) Sorting is carried out based on the determined similarity values;
and sequentially selecting the objects respectively corresponding to the preset number of similarity values as target objects based on the sequence of the similarity values.
In this way, sorting is performed based on the determined similarity values, and the sorting mode may be sequentially arranged from large to small, or sequentially arranged from small to large, although the specific arrangement mode is not limited; the similarity values are sequentially arranged from big to small in an example, the quantity configuration is selected based on the preset similarity values, the preset quantity of similarity values are sequentially selected from the similarity values arranged according to the big and small sequence, and the objects corresponding to the screened similarity values are determined as target objects.
Wherein, for the object feature database, the construction process comprises:
determining related information of the object based on a corresponding relation between a preset object and text data;
and constructing an object feature database based on the determined related information of each object.
Wherein, the determining the relevant information of the object based on the corresponding relation between the preset object and the text data comprises:
determining text data of the object based on a preset corresponding relation between the object and the text data;
determining sparse distributed representations corresponding to words composing the text data based on the text data;
and combining the sparse distributed representations corresponding to the words forming the text data to obtain the relevant information of the object.
Further, the related information in the present application is a sparse distributed characterization vector of the current object.
And step S404, pushing the relevant information of the screened at least one target object.
Specifically, the relevant information of each screened target object is pushed, so that the receiving end can select a required target object as a partner for the object to be processed based on the relevant information of each screened target object.
According to the method and the device, corresponding professional matching and recommendation are carried out based on the relevant information of the object to be processed, and accurate pushing of the relevant information of the target object is achieved.
For the technical solution provided in the embodiment of the present application, a specific embodiment is described in detail below, in which the information pushing method may be divided into five processing steps, as shown in fig. 5, and specifically includes:
the first step, data preprocessing. Firstly, acquiring all thesis and patent data of a certain field within a certain time from a database, and removing repeated data; secondly, extracting the thesis text data (title, abstract, text) and the patent text data (title, abstract, claim and specification); and finally, extracting data of the author (inventor), and storing the corresponding relation between the author (inventor) and the text.
And secondly, establishing a scientific research personnel library. The extracted author name and inventor name are first disambiguated separately. The author name disambiguation can be carried out by using the thesis data, the inventor name disambiguation can be carried out by using the patent data, then authors and inventors belonging to the same organization and the same name are merged, namely the authors and the inventors are the same scientific research personnel, and finally all the authors and the inventors are merged to form a scientific research personnel library.
In this step, researchers often include authors of treatises and inventors of patents. Firstly, extracting the name of an author from a thesis, and disambiguating the name of the author by using an author name disambiguation algorithm and thesis data; the name of the inventor is extracted from the patent, and the name of the inventor is disambiguated by using an inventor name disambiguation algorithm and patent data. Merging the author names and the inventor names after disambiguation, merging the scientific researchers in the same institution and the same name into a scientific researcher library for processing by utilizing the constructed scientific research institution library, and forming a scientific researcher library by the processed data; the name, published papers, patent conditions and cooperation relationships of the researchers are recorded in the researcher library, and the processing flow is as shown in fig. 6.
And thirdly, establishing a text feature library. And combining the extracted thesis text data and patent text data, still keeping the relation between scientific research personnel and text contents, and representing the text by using sparse distributed representation to form a text feature library.
In this step, the generation of the text feature library is based on the merged thesis text data and patent text data. The article text data extracts the title, abstract or text of the article, and the patent text data extracts the title, abstract, claim and patent specification of the patent. The abstract or the text can be independently selected as a text, or a topic can be added, or the abstract or the text and the topic are combined together to be used as a text of a thesis, the method selected by the invention does not need to carry out word segmentation, stop word removal, word frequency statistics and other processing on text data, and can directly generate sparse distributed representation from the specified text, specifically: according to the range of the selected text, determining a larger corpus containing the text, cutting all the corpuses in the corpus into independent text segments, arranging the text segments in a two-dimensional matrix, enabling the text segments with the same expression theme (the text segments contain the same number of words with very large number) to be located at the same position in the matrix, enabling the text segments with the similar theme to be located at the same position on the matrix, enabling each word in the corpus to correspond to the two-dimensional matrix, wherein the numerical value of the corresponding position of the text segment containing the word is 1, otherwise, the numerical value is 0, and converting the obtained matrix into a line, namely a vector form, wherein the vector is high-dimensional and sparse, namely, the sparse distributed representation of the word. And combining the sparse distributed representations of each word forming the text, ensuring that the combined sparsity (the proportion of components with the value of 1) is in a limited range, and when the sparsity exceeds a threshold value, reserving the sparsity according to the occurrence frequency of 1 in each position to ensure that the sparsity is in the limited range, so as to obtain the sparse distributed representations of all the texts, forming a text feature library, wherein the processing flow is shown in fig. 7.
And fourthly, establishing a feature library of scientific research personnel. Firstly, obtaining all texts (published paper texts or texts of applied patents) related to each scientific researcher according to the relation between the scientific researcher and the texts in a text feature library, and then performing joint operation on Sparse Distributed Representations (SDRs) of the texts to obtain sparse distributed representations of the scientific researcher so as to represent research contents of the scientific researcher; the sparsely distributed representations of all researchers constitute a researcher feature library.
In this step, sparse distributed representations of all texts (texts of papers and patents applied by the papers) belonging to each researcher are obtained by using the constructed researcher library and the text feature library, the sparse distributed representations of all texts of each researcher are combined, the sparsity degree of the synthesized sparse distributed representation vectors is ensured, when the vectors are not sparse enough (excessive "1"), the "1" at the position where the vectors repeatedly appear in the multiple text sparse distributed representation vectors is preferentially reserved, the finally obtained sparse distributed representation vectors are used as the sparse distributed representations of the researcher, and the sparse distributed representations of all the researchers form the researcher feature library, as shown in fig. 8.
And fifthly, performing academic cooperation recommendation. And performing similarity calculation according to sparse distributed representation of scientific researchers, performing descending arrangement according to calculation results, and selecting the scientific researchers corresponding to the first N bits in the arrangement as results for recommendation.
In the step, after the characteristic library of the scientific researchers is obtained, sparse distributed characterization vectors of the scientific researchers are extracted from the characteristic library, and sequencing and recommendation are performed according to the similarity of the sparse distributed characterization vectors.
In a specific recommendation process, if a collaborator is selected for a researcher a, each researcher who does not collaborate with the researcher a is selected from the researcher feature library to perform similarity calculation, and as each bit of the sparse distributed representation vector has a specific semantic meaning, the more "1" at the same position indicates that the vectors are more similar semantically, the number of the same positions, namely the number of overlaps, of the "1" can be directly adopted to perform similarity calculation, or conventional similarity metrics such as Jaccard similarity, euclidean distance and the like can be selected, and the similarity calculation method is not limited. Of course, regardless of the method, it is obvious that the higher the similarity is, the more similar the research content of the researcher is, and the more meaningful the recommended subject is, as shown in fig. 9.
Based on the information pushing method provided by the present application, the present application further provides an information pushing apparatus, as shown in fig. 10, including:
an acquisition unit 1001 configured to acquire related information of an object to be processed;
the screening unit 1002 is configured to screen out at least one target object matched with an object to be processed based on relevant information of the object to be processed;
a pushing unit 1003, configured to push the relevant information of the screened at least one target object.
An obtaining unit 1001, specifically configured to obtain identity information of the object to be processed; and inquiring a preset object feature database based on the identity identification information to acquire the relevant information of the object to be processed.
A screening unit 1002 for determining a similarity value based on the related information of the object to be processed; and screening out at least one target object matched with the object to be processed based on the determined similarity value.
The screening unit 1002 is specifically configured to traverse an object feature database based on related information of an object to be processed, and determine similarity values between the related information of the object to be processed and related information corresponding to each object in the object feature database.
The screening unit 1002 is further specifically configured to screen each determined similarity value based on a preset similarity threshold; and selecting objects respectively corresponding to the similarity values larger than a preset similarity threshold value as target objects.
The screening unit 1002 is further specifically configured to sort based on the determined similarity values; and sequentially selecting the objects respectively corresponding to the preset number of similarity values as target objects based on the sequence of the similarity values.
And the related information is a sparse distributed characterization vector of the current object.
An embodiment of the present application provides an electronic device, as shown in fig. 11, an electronic device 2000 shown in fig. 11 includes: a processor 2001 and a transceiver 2004. The processor 2001 is coupled to the transceiver 2004, such as via the bus 2002. Optionally, electronic device 2000 may also include memory 2003. It should be noted that the transceiver 2004 is not limited to one in practical applications, and the structure of the electronic device 2000 is not limited to the embodiment of the present application.
The processor 2001 is applied in the embodiment of the present application to implement the function of the screening unit 1002 shown in fig. 10. The transceiver 2004 includes a receiver and a transmitter, and the transceiver 2004 is applied to the embodiment of the present application to realize the functions of the obtaining unit 1001 shown in fig. 3.
The processor 2001 may be a CPU, general purpose processor, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 2001 may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs and microprocessors, and the like.
The memory 2003 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an EEPROM, a CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
Optionally, the memory 2003 is used for storing application program code for performing the disclosed aspects, and is controlled in execution by the processor 2001. The processor 2001 is configured to execute application program codes stored in the memory 2003 to implement the actions of the information pushing apparatus provided in the embodiment shown in fig. 10.
The embodiment of the application provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the information push method is implemented.
It will be understood by those within the art that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by computer program instructions. Those skilled in the art will appreciate that the computer program instructions may be implemented by a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, implement the aspects specified in the block or blocks of the block diagrams and/or flowchart illustrations disclosed herein.
The modules of the device can be integrated into a whole or can be separately deployed. The modules can be combined into one module, and can also be further split into a plurality of sub-modules.
Those skilled in the art will appreciate that the drawings are merely schematic representations of one preferred embodiment and that the blocks or flow diagrams in the drawings are not necessarily required to practice the present application.
Those skilled in the art will appreciate that the modules in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, and may be correspondingly changed in one or more devices different from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
The above application serial numbers are for descriptive purposes only and do not represent the merits of the embodiments.
The disclosure of the present application is only a few specific embodiments, but the present application is not limited to these, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present application.
Claims (8)
1. An information pushing method, comprising:
acquiring related information of an object to be processed;
the acquiring of the relevant information of the object to be processed includes: acquiring identity identification information of an object to be processed; inquiring a preset object feature database based on the identity identification information to acquire related information of the object to be processed;
the construction process of the object feature database comprises the following steps: determining related information of the object based on a corresponding relation between a preset object and text data; constructing an object feature database based on the determined relevant information of each object;
the determining of the relevant information of the object based on the corresponding relation between the preset object and the text data includes: determining text data of the object based on a preset corresponding relation between the object and the text data; determining sparse distributed representations corresponding to words composing the text data based on the text data; combining sparse distributed representations corresponding to words forming the text data to obtain related information of the object;
the related information is a sparse distributed characterization vector of a corresponding object; the sparse distributed characterization vector corresponding to each word of the text data is a high-dimensional binary vector determined based on a two-dimensional matrix comprising each word of the text data;
screening out at least one target object matched with the object to be processed based on the related information of the object to be processed;
and pushing the relevant information of the screened at least one target object.
2. The method of claim 1, wherein the screening out at least one target object matching the object to be processed based on the related information of the object to be processed comprises:
determining a similarity value based on the related information of the object to be processed;
and screening out at least one target object matched with the object to be processed based on the determined similarity value.
3. The method of claim 2, wherein determining the similarity value based on the related information of the object to be processed comprises:
traversing an object feature database based on the related information of the object to be processed, and determining the similarity value between the related information of the object to be processed and the related information corresponding to each object in the object feature database.
4. The method according to claim 3, wherein screening out at least one target object matching the object to be processed based on the determined similarity value specifically comprises:
screening the determined similarity values based on a preset similarity threshold value;
and selecting objects respectively corresponding to the similarity values larger than a preset similarity threshold value as target objects.
5. The method according to claim 3, wherein screening out at least one target object matching the object to be processed based on the determined similarity value specifically comprises:
sorting is carried out based on the determined similarity values;
and sequentially selecting the objects respectively corresponding to the preset number of similarity values as target objects based on the sequence of the similarity values.
6. An information pushing apparatus, comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring related information of an object to be processed;
the acquiring of the relevant information of the object to be processed includes: acquiring identity identification information of an object to be processed; inquiring a preset object feature database based on the identity identification information to acquire related information of the object to be processed;
the construction process of the object feature database comprises the following steps: determining related information of the object based on a corresponding relation between a preset object and text data; constructing an object feature database based on the determined relevant information of each object;
the determining of the relevant information of the object based on the corresponding relation between the preset object and the text data includes: determining text data of the object based on a preset corresponding relation between the object and the text data; determining sparse distributed representations corresponding to words composing the text data based on the text data; combining sparse distributed representations corresponding to words forming the text data to obtain related information of the object;
the related information is a sparse distributed characterization vector of a corresponding object; the sparse distributed characterization vector corresponding to each word of the text data is a high-dimensional binary vector determined based on a two-dimensional matrix comprising each word of the text data;
the screening unit is used for screening out at least one target object matched with the object to be processed based on the related information of the object to be processed;
and the pushing unit is used for pushing the relevant information of the screened at least one target object.
7. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the information push method according to any one of claims 1 to 5.
8. An electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the information pushing method in any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810699428.8A CN108897860B (en) | 2018-06-29 | 2018-06-29 | Information pushing method and device, electronic equipment and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810699428.8A CN108897860B (en) | 2018-06-29 | 2018-06-29 | Information pushing method and device, electronic equipment and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108897860A CN108897860A (en) | 2018-11-27 |
| CN108897860B true CN108897860B (en) | 2022-05-27 |
Family
ID=64347374
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810699428.8A Active CN108897860B (en) | 2018-06-29 | 2018-06-29 | Information pushing method and device, electronic equipment and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108897860B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110941662A (en) * | 2019-06-24 | 2020-03-31 | 上海市研发公共服务平台管理中心 | Graphical method, system, storage medium and terminal for scientific research cooperative relationship |
| CN111931059A (en) * | 2020-08-19 | 2020-11-13 | 创新奇智(成都)科技有限公司 | Object determination method and device and storage medium |
| CN112202889B (en) * | 2020-09-30 | 2023-05-23 | 深圳前海微众银行股份有限公司 | Information pushing method, device and storage medium |
| CN112612817B (en) * | 2020-12-07 | 2024-02-27 | 深圳价值在线信息科技股份有限公司 | Data processing method, device, terminal equipment and computer readable storage medium |
| CN114579871A (en) * | 2022-05-06 | 2022-06-03 | 南京因由数字科技有限公司 | Recommendation method and device based on patent information, electronic equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106815297A (en) * | 2016-12-09 | 2017-06-09 | 宁波大学 | A kind of academic resources recommendation service system and method |
| CN107465766A (en) * | 2017-09-21 | 2017-12-12 | 掌阅科技股份有限公司 | Information-pushing method, electronic equipment and computer-readable storage medium |
-
2018
- 2018-06-29 CN CN201810699428.8A patent/CN108897860B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106815297A (en) * | 2016-12-09 | 2017-06-09 | 宁波大学 | A kind of academic resources recommendation service system and method |
| CN107465766A (en) * | 2017-09-21 | 2017-12-12 | 掌阅科技股份有限公司 | Information-pushing method, electronic equipment and computer-readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108897860A (en) | 2018-11-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108897860B (en) | Information pushing method and device, electronic equipment and computer readable storage medium | |
| Ferreira et al. | Emergent: a novel data-set for stance classification | |
| CN104376406B (en) | A kind of enterprise innovation resource management and analysis method based on big data | |
| US9213943B2 (en) | Parameter inference method, calculation apparatus, and system based on latent dirichlet allocation model | |
| CN111190997B (en) | Question-answering system implementation method using neural network and machine learning ordering algorithm | |
| CN111831802B (en) | Urban domain knowledge detection system and method based on LDA topic model | |
| CN110909160A (en) | Regular expression generation method, server and computer readable storage medium | |
| CN112749326A (en) | Information processing method, information processing device, computer equipment and storage medium | |
| CN110910175B (en) | Image generation method for travel ticket product | |
| CN111652468A (en) | Business process generation method and device, storage medium and computer equipment | |
| CN112612761B (en) | Data cleaning method, device, equipment and storage medium | |
| CN113722438A (en) | Sentence vector generation method and device based on sentence vector model and computer equipment | |
| CN116881430B (en) | Industrial chain identification method and device, electronic equipment and readable storage medium | |
| Vishwakarma et al. | A comparative study of K-means and K-medoid clustering for social media text mining | |
| US10853429B2 (en) | Identifying domain-specific accounts | |
| CN104899310A (en) | Information ranking method, and method and device for generating information ranking model | |
| CN112015895A (en) | Patent text classification method and device | |
| CN117520533A (en) | Chart generation method | |
| CN115329078B (en) | Text data processing method, device, equipment and storage medium | |
| CN116049376A (en) | Method, device and system for retrieving and replying information and creating knowledge | |
| CN116595191A (en) | Construction method and device of interactive low-code knowledge graph | |
| CN116304155A (en) | Three-dimensional member retrieval method, device, equipment and medium based on two-dimensional picture | |
| WO2021257195A1 (en) | Topic graph-based comment generation | |
| Yu et al. | Mining hidden interests from twitter based on word similarity and social relationship for OLAP | |
| CN113779473A (en) | Internet big data processing method and system based on artificial intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |