CN109190049B - Keyword recommendation method, system, electronic device and computer readable medium - Google Patents
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Abstract
The present disclosure provides a keyword recommendation method, system, electronic device, and computer-readable medium, the keyword recommendation method including: acquiring an incidence relation between search keywords according to historical search data; extracting keywords corresponding to the browsing behavior according to the browsing behavior of the user; and generating an associated search word according to the keyword association relation and the extracted keywords corresponding to the user browsing behaviors, and displaying the associated search word as a recommended word in a search box of a browsing page. The method and the device achieve at least one of multiple purposes of improving browsing efficiency, combining browsing interest of the user to conduct browsing guidance, pushing accurate information, increasing user experience and the like.
Description
Technical Field
The disclosure relates to the technical field of computers, internet, big data and the like, in particular to a keyword recommendation method, a keyword recommendation system, electronic equipment and a computer readable medium.
Background
At present, when a user browses network contents, browsing requirements are usually changeable, in order to improve browsing efficiency and accuracy, search boxes are designed in some network content pages, so that the user can search contents expected to be obtained, but in the prior art, search recommended words are usually not presented in search empty boxes designed in the network content pages, the user is usually required to manually input the search words for searching, and the problem that the user manually input keywords have the problem that sometimes the user cannot necessarily select accurate keywords, the situation that the words are not satisfactory possibly exists, the browsing efficiency of the user on the contents is seriously influenced by manual input, and the user experience is reduced; in addition, in most cases, the browsing requirements of the user are variable and sometimes even uncertain when browsing the content, and the way of manually inputting the keywords is not favorable for guiding the user to browse the content.
Disclosure of Invention
In view of the problems in the prior art, an object of the present disclosure is to provide a keyword recommendation method, system, device and computer readable medium, which can achieve at least one of the objectives of improving browsing efficiency, browsing guidance in combination with browsing interest of a user, accurate information push, and increasing user experience.
To achieve the purpose, the following technical scheme is adopted in the disclosure:
as a first aspect of the present disclosure, there is provided a keyword recommendation method including:
acquiring an incidence relation between search keywords according to historical search data;
extracting keywords corresponding to the browsing behavior according to the browsing behavior of the user;
and generating an associated search word according to the keyword association relation and the extracted keywords corresponding to the user browsing behaviors, and displaying the associated search word as a recommended word in a search box of a browsing page.
As a second aspect of the present disclosure, there is provided a keyword recommendation system including:
the keyword extraction and analysis module is used for acquiring the incidence relation among the search keywords according to historical search data;
the user browsing behavior keyword extraction module is used for extracting keywords corresponding to the browsing behavior according to the user browsing behavior;
and the search word recommending module is used for generating an associated search word according to the keyword association relationship provided by the keyword extracting and analyzing module and the keyword corresponding to the user browsing behavior extracted by the user browsing behavior keyword extracting module, and displaying the associated search word as a recommended word in a search box of a browsing page.
As a third aspect of the present disclosure, there is provided an electronic apparatus including: a processor and a memory, the memory having a medium with program code stored therein, the electronic device being capable of performing the method of the embodiments of the disclosure when the processor reads the program code stored in the medium.
As a fourth aspect of the present disclosure, there is provided a computer readable medium having stored thereon computer readable instructions executable by a processor to implement a method according to an embodiment of the present disclosure.
Compared with the prior art, the method has the following beneficial effects:
according to the method and the device, the keyword incidence relation is established according to the historical search data of the user, the keyword corresponding to the browsing behavior is extracted through the browsing behavior of the user, and the incidence search word of the keyword corresponding to the extracted browsing behavior of the user is displayed in the search box of the browsed page, so that the search keyword with the highest possibility of being used by the user is automatically displayed in the search box in the page when the user browses a certain page, the step of manually inputting the search keyword by the user is saved, the situation that the word possibly exists in the process that the user selects the keyword by himself is not satisfactory is reduced, the browsing behavior of the user can be guided under certain specific conditions, the browsing efficiency is improved, and the user experience is improved.
Drawings
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a keyword recommendation system according to an embodiment of the disclosure;
FIG. 3 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
The present disclosure is described in further detail below. The following examples are merely illustrative of the present disclosure and do not represent or limit the scope of the claims that follow.
Detailed Description
The technical scheme of the disclosure is further explained by the specific implementation mode in combination with the attached drawings.
To better illustrate the present disclosure, and to facilitate an understanding of the technical solutions of the present disclosure, typical but non-limiting examples of the present disclosure are as follows: it should be specifically noted that the embodiments listed in the description of the present disclosure are only exemplary embodiments given for convenience of description, and should not be construed as the only correct embodiments of the present disclosure, nor as a restrictive description of the scope of the present disclosure.
Fig. 1 is a schematic flowchart of a keyword recommendation method according to an embodiment of the present disclosure, where the keyword recommendation method includes: s1: acquiring an incidence relation between search keywords according to historical search data;
s2: extracting keywords corresponding to the browsing behavior according to the browsing behavior of the user;
s3: and generating an associated search word according to the keyword association relation and the extracted keywords corresponding to the user browsing behaviors, and displaying the associated search word as a recommended word in a search box of a browsing page.
The historical search data in the incidence relation among the search keywords obtained according to the historical search data is not limited to the historical data of the current user, and can be the historical search data of all search users, the historical search data of part of specific user groups, and the historical search data which is extracted from the historical search data of all search users and meets specific characteristics.
As a preferred embodiment, the step of obtaining the association relationship between the search keywords according to the historical search data includes: and acquiring the incidence relation among the search keywords according to at least one of the webpage accessed by the user, the clicked content, the used search words, the webpage accessed by the user, the clicked content and/or the sequence of the used search words.
For example: the user firstly visits the related webpage of the 'problem product', further clicks that the user proportion of 'problem product going' in the webpage is 85 percent, the user proportion of 'product safety problem' is 80 percent, the user proportion of 'XXX responsible person for introducing XXXXXX related situation' is 60 percent, the user proportion of 'product supervision system' is 40 percent, meanwhile, the user proportion of search keywords 'AA product' is 95 percent, the user proportion of 'BB product' is 90 percent, the user proportion of 'adverse reaction + product' is 96 percent, the user proportion of 'production and sale false product crime' is 30 percent, and the user proportion of 'compensation' is 20 percent when browsing the webpage content, and then the historical data of the user such as the access data and the search data can be used for learning that the 'problem product', 'problem product going' to the problem product safety problem ',' related situation For example, more than 90% of users who visit the relevant webpage of the problem product further visit the relevant webpages of the AA product, the BB product, the adverse reaction plus product and the like, the correlation relationship can be considered to be extremely strong correlation relationship between the problem product and the keywords of the AA product, the BB product, the adverse reaction plus product and the like, and in some preferred embodiments, the correlation relationship can be marked by grade or definite quantitative digital marking, for example, the correlation relationship between the problem product and the AA product, the BB product, the adverse reaction plus product is marked as extremely strong, The association relation can also adopt a digital quantitative labeling mode under the condition that specific conditions are met, for example, the association degree of the problem product and the AA product is labeled as 95%, the association degree of the problem product and the BB product is labeled as 90%, and the association degree of the problem product and the adverse reaction plus product is labeled as 96%. In still other more preferred embodiments, the association relationship between any two keywords such as "AA product", "BB product", "adverse reaction + product" and the like can be calculated by a specific association algorithm. It should be noted that in some preferred embodiments, the relationship is directional, that is: the 95% association of the "question product" with the "AA product" does not indicate that the 95% association of the "AA product" with the "question product" is 95%, that is, 95% of users who access the "question product" will access the "AA product" related web page, but does not indicate that 95% of users who access the "AA product" related web page will access the "question product" related web page. The directional index can be obtained through related historical data such as web pages visited by users, contents clicked and/or the sequence of used search words.
The association relationship between the keywords can be obtained through historical search data, however, the calculation amount required for calculating the association relationship between all the keywords is too large, resources are consumed, and efficiency is affected, and in the case that some historical data is missing, it may be difficult to calculate the association relationship between all the keywords. As a preferred embodiment, the core words may be determined first according to the webpage accessed by the user, the clicked content and/or the used search terms, and the sequence of the webpage accessed by the user, the clicked content and/or the used search terms; or, taking the aforementioned example as an example, the "problem product" may be determined as a core word, then only the association relationship between the "problem product" and the keywords such as "problem product going direction", "product security problem", "related situation", "product regulatory system", "AA product", "BB product", "adverse reaction + product", "production and sale pseudo-inferior product crime", "compensation" and the like is calculated according to the historical data, and then the association relationship is generated into an association relationship list to facilitate system invocation.
In order to improve the utilization efficiency of the list, some repeated, redundant and/or low-relevance data can be removed by adopting a data cleaning, deduplication and filtering mode.
As a further preferred embodiment, the step of extracting the keyword corresponding to the browsing behavior of the user according to the browsing behavior of the user includes: and extracting keywords corresponding to the browsing behavior according to the specific browsing content, the browsing duration and the search words used during browsing of the user. For example: the user browses the related webpage content of the 'problem product', mentions 'AA product', 'BB product', 'adverse reaction', 'safety' and 'marketing company' in the article, and the user further uses the keywords or clicks the related webpage linking 'AA product', 'BB product' and 'adverse reaction', and uses the related webpage content to browse the related webpage content for a long time, so that the 'problem product' and the 'AA product', 'BB product' and 'adverse reaction' are considered to have strong relevance.
As a preferred embodiment, the association relationship may be marked by qualitative and quantitative labels, and the qualitative may be an association level, for example: strong, general, poor, weak, etc., although the association level may be any other suitable character, such as any recognizable character such as A, B, C, D …, 1, 2, 3, 4 …, etc., during a particular use. For the quantitative labeling mode, the quantitative labeling mode can be obtained by calculating relevant data such as specific user access probability, specific webpage browsing duration and the like.
In step S2: extracting keywords corresponding to the browsing behavior according to the browsing behavior of the user; the user here refers to a current user, for example, the current user browses the content of the related web page "XXX biometric ineligible XXX product flow", and it is assumed that the keyword corresponding to the browsing behavior extracted from the browsing behavior is "problem product". Then, in step S3, an associated search term is generated according to the keyword association relationship and the extracted keyword corresponding to the user browsing behavior, for example: "AA product", "BB product", "adverse reaction", "safety"; and meanwhile, the related search words are displayed in a search box of a browsing page as recommended words, and under the condition, most users do not need to manually input keywords further, and the purpose of further browsing can be realized by directly clicking related keyword search.
As a preferred embodiment, the step of extracting the keywords corresponding to the browsing behavior according to the browsing behavior of the user is to extract the corresponding keywords by semantic analysis of the content of the specific page browsed by the user. The semantic analysis means that a computer analyzes and extracts related keywords according to core meanings to be expressed by the content of the whole article, and specifically, the keywords can be extracted according to word frequency, positions, interval relations with certain specific words and the like of specific terms used in the whole article. For example, if the number of times of "product" in a certain article is the largest and the term of "unqualified" is also present in the title, the keyword corresponding to the browsing behavior may be extracted as "problem product". Of course, the above description is only illustrative, and the number of keywords extracted for a certain browsing behavior is not limited to one, and a plurality of corresponding keywords may be selected or set as needed.
When a real user browses a search, browsing requirements are uncertain, searching requirements are variable, and keywords which need to be used may need to be frequently rewritten and replaced. In some preferred embodiments, the keywords recommended to the search box in the browsing page are real-time, and the recommended keywords are updated in real-time according to changes in browsing behavior and content. The method has the advantages that the keywords closely related to the interest points of the user are recommended in real time according to the real-time dynamic change of the browsing behavior of the user in the search box, the search requirement of the user is greatly facilitated, and meanwhile, the function of timely and purposefully guiding the user to browse the specific content can be achieved. The idea of guiding the browsing behavior of the user is mentioned here, which can be realized by setting specific key related words in the disclosure, that is, according to the requirement of guiding the browsing behavior, related words of the keywords corresponding to the browsing behavior are purposefully rewritten, and the time of displaying the related words in the search box is controlled by adjusting the degree of the relationship.
As another aspect of the present disclosure, the present disclosure also provides a system corresponding to the foregoing method steps, and many technical details submitted in the foregoing description of the method are also included in the system corresponding to the method, and are not expanded in the description of the system for economy.
The system disclosed in the present disclosure may be a physical hardware system, a virtual system formed by software functional modules, or a system formed by combining hardware and software, but those skilled in the art should understand that the function of the virtual system formed by any software functional module can be implemented without the support of a physical hardware device.
Fig. 2 is a schematic structural diagram of a keyword recommendation system according to the present disclosure, where the keyword recommendation system 100 includes:
the keyword extraction and analysis module 101 is used for obtaining the incidence relation among the search keywords according to historical search data;
a keyword extraction module 102 for extracting a keyword corresponding to a browsing behavior according to the browsing behavior of the user;
and the search word recommending module 103 is configured to generate an associated search word according to the keyword association relationship provided by the keyword extracting and analyzing module and the keyword corresponding to the user browsing behavior extracted by the user browsing behavior keyword extracting module, and display the associated search word as a recommended word in a search box of a browsing page.
As a preferred embodiment, the keyword extraction and analysis module 101 may obtain an association relationship between search keywords according to at least one of a webpage accessed by a user, a clicked content, a used search term, a webpage accessed by the user, a clicked content, and/or a sequence of the used search term.
As a more preferred embodiment, the keyword extraction and analysis module 101 includes: the core word extraction submodule is used for determining the core words according to at least one of the webpage accessed by the user, the clicked content, the used search words, the webpage accessed by the user, the clicked content and/or the used search word sequence;
the incidence relation extraction submodule is used for extracting the collaborative relation between the core word and other key words;
and the incidence relation list generating submodule is used for generating an incidence relation list between the core word and other key words.
As a further preferred embodiment, the association relation list generation sub-module includes: and the data cleaning, deduplication and filtering unit is used for removing some repeated, redundant and/or low-relevance data in a data cleaning, deduplication and filtering manner so as to improve the utilization efficiency of the list.
As a preferred embodiment, the keyword extraction and analysis module 101 can further extract keywords corresponding to the browsing behavior according to the specific browsing content, browsing duration and search terms used during browsing of the user. Further, extracting the keyword corresponding to the browsing behavior according to the browsing behavior of the user includes: and extracting corresponding keywords according to the title and the content word frequency of the specific page content browsed by the user.
As a preferred embodiment, the keyword extraction module 102 for user browsing behavior includes a semantic analysis sub-module, which extracts keywords corresponding to the user browsing behavior by performing semantic analysis on the content of a specific page browsed by the user. The semantic analysis means that a computer analyzes and extracts related keywords according to core meanings to be expressed by the content of the whole article, and specifically, the keywords can be extracted according to word frequency, positions, interval relations with certain specific words and the like of specific terms used in the whole article. For example, if the number of times of "product" in a certain article is the largest and the term of "unqualified" is also present in the title, the keyword corresponding to the browsing behavior may be extracted as "problem product". Of course, the above description is only illustrative, and the number of keywords extracted for a certain browsing behavior is not limited to one, and a plurality of corresponding keywords may be selected or set as needed.
When a real user browses a search, browsing requirements are uncertain, searching requirements are variable, and keywords which need to be used may need to be frequently rewritten and replaced. As a preferred embodiment, the search word recommendation module 103 can display the recommendation word in the search box of the browsing page in real time, and further, can dynamically update the recommendation word in the search box in real time according to the subsequent web page access behavior of the user. The method has the advantages that the keywords closely related to the interest points of the user are recommended in real time according to the real-time dynamic change of the browsing behavior of the user in the search box, the search requirement of the user is greatly facilitated, and meanwhile, the function of timely and purposefully guiding the user to browse the specific content can be achieved. The idea of guiding the browsing behavior of the user is mentioned here, which can be realized by setting specific key related words in the disclosure, that is, according to the requirement of guiding the browsing behavior, related words of the keywords corresponding to the browsing behavior are purposefully rewritten, and the time of displaying the related words in the search box is controlled by adjusting the degree of the relationship.
As another aspect of the present disclosure, there is also provided an electronic apparatus including: a processor and a memory, the memory having a medium (computer-readable storage medium) with program code stored therein, the electronic device being capable of performing the following method steps when the processor reads the program code stored in the medium: acquiring an incidence relation between search keywords according to historical search data; extracting keywords corresponding to the browsing behavior according to the browsing behavior of the user; and generating an associated search word according to the keyword association relation and the extracted keywords corresponding to the user browsing behaviors, and displaying the associated search word as a recommended word in a search box of a browsing page.
Further: the electronic device is also capable of performing any of the other methods described in this disclosure when the processor reads the program code stored in the medium.
Fig. 4 is a schematic diagram illustrating a computer-readable storage medium according to an embodiment of the present disclosure. As shown in fig. 4, a computer-readable storage medium 300 having non-transitory computer-readable instructions 301 stored thereon according to an embodiment of the present disclosure. The non-transitory computer readable instructions 301, when executed by a processor, perform all or a portion of the steps of the keyword recommendation method of the embodiments of the disclosure previously described.
Fig. 3 is a schematic diagram illustrating a hardware structure of an electronic device according to an embodiment of the present disclosure. The electronic device may be implemented in various forms, and the electronic device in the present disclosure may include, but is not limited to, mobile terminal devices such as a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation apparatus, a vehicle-mounted terminal device, a vehicle-mounted display terminal, a vehicle-mounted electronic rear view mirror, and the like, and fixed devices such as a digital TV, a desktop computer, and the like.
As shown in fig. 3, the electronic device 1100 may include a processor 1120, an input unit 1130, a memory 1140, an output unit 1150, and the like. Fig. 3 shows an electronic device having various components, but it is understood that not all of the illustrated components are required to be implemented. More or fewer components may alternatively be implemented.
Among other things, the processor 1120 is used for executing the methods disclosed in the present disclosure, the input unit 1130 may generate key input data according to a command input by a user to control various operations of the electronic device, and the output unit 1150 provides an output signal. The memory 1140 may store software programs or the like for processing and controlling operations performed by the processor 1120, or may temporarily store data that has been output or is to be output. Memory 1140 may include at least one type of storage medium. Also, the electronic apparatus 1100 may cooperate with a network storage device that performs a storage function of the memory 1140 by way of a network connection. The processor 1120 generally controls the overall operation of the electronic device.
Various embodiments of the keyword recommendation method presented in the present disclosure may be implemented using a computer-readable medium, such as computer software, hardware, or any combination thereof.
For a hardware implementation, various embodiments of the keyword recommendation method proposed by the present disclosure may be implemented by using at least one of Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), processors, controllers, microprocessors, microcontrollers, electronic units designed to perform the functions described herein, and in some cases, various embodiments of the keyword recommendation method proposed by the present disclosure may be implemented in the processor 1120. For software implementation, various embodiments of the keyword recommendation method presented in the present disclosure may be implemented with a separate software module that allows at least one function or operation to be performed. The software codes may be implemented by software applications (or programs) written in any suitable programming language, which may be stored in memory 1140 and executed by processor 1120.
The applicant declares that the present disclosure illustrates the detailed structural features of the present disclosure through the above-mentioned embodiments, but the present disclosure is not limited to the above-mentioned detailed structural features, i.e. it does not mean that the present disclosure must rely on the above-mentioned detailed structural features for implementation. It will be apparent to those skilled in the art that any modification of the present disclosure, equivalent substitutions of selected elements of the disclosure, additions of auxiliary elements, selection of particular means, etc., are within the scope and disclosure of the present disclosure.
The preferred embodiments of the present disclosure have been described in detail above, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (10)
1. A keyword recommendation method comprises the following steps:
acquiring an incidence relation between search keywords according to historical search data;
extracting keywords corresponding to the browsing behavior according to the browsing behavior of the user, wherein the extracting includes: extracting keywords corresponding to the browsing behavior according to the specific browsing content, the browsing duration and the search words used during browsing of the user;
and generating an associated search word according to the keyword association relation and the extracted keywords corresponding to the user browsing behaviors, and displaying the associated search word as a recommended word in a search box of a browsing page in real time.
2. The method of claim 1, wherein the step of obtaining the association between the search keywords according to the historical search data comprises:
and acquiring the association relation among the search keywords according to at least one of the webpage accessed by the user, the clicked content, the used search words, the accessed precedence relation, the clicked precedence relation and the used precedence relation.
3. The method of claim 1, wherein the step of obtaining the association between the search keywords according to the historical search data comprises:
determining a core word according to at least one of a webpage accessed by a user, clicked content, used search words, the accessed precedence relationship, the clicked precedence relationship and the used precedence relationship;
extracting the collaborative relationship between the core word and other key words;
and generating an incidence relation list between the core word and other key words.
4. The method of claim 3, wherein generating the list of associations between the core word and other keywords comprises: and (5) data cleaning.
5. The method as claimed in claim 1, wherein the step of extracting the keyword corresponding to the browsing behavior of the user according to the browsing behavior comprises:
and extracting corresponding keywords according to the title and the content word frequency of the specific page content browsed by the user.
6. The method as claimed in claim 1, wherein the step of extracting the keyword corresponding to the browsing behavior of the user according to the browsing behavior comprises:
and extracting the corresponding keywords in a semantic analysis mode of the content of the specific page browsed by the user.
7. The method of claim 1, further comprising: and dynamically updating the recommended words in the search box in real time according to the subsequent webpage access behaviors of the user.
8. A keyword recommendation system comprising:
the keyword extraction and analysis module is used for acquiring the incidence relation among the search keywords according to historical search data;
the user browsing behavior keyword extraction module is used for extracting keywords corresponding to the browsing behavior according to the user browsing behavior, and comprises: extracting keywords corresponding to the browsing behavior according to the specific browsing content, the browsing duration and the search words used during browsing of the user;
and the search word recommending module is used for generating an associated search word according to the keyword association relationship provided by the keyword extracting and analyzing module and the keyword corresponding to the user browsing behavior extracted by the user browsing behavior keyword extracting module, and displaying the associated search word as a recommended word in a search box of a browsing page.
9. An electronic device, comprising: a processor and a memory, the memory having a medium with program code stored therein, the electronic device being capable of performing the method of any of claims 1-7 when the processor reads the program code stored in the medium.
10. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of any one of claims 1 to 7.
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| CN112445968B (en) * | 2019-09-03 | 2023-04-21 | 百度(中国)有限公司 | Information pushing method, device, equipment and computer readable storage medium |
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| CN113111286B (en) * | 2021-05-12 | 2023-07-18 | 抖音视界有限公司 | Information display method and device and computer storage medium |
| CN113360779B (en) * | 2021-08-09 | 2023-01-20 | 智者四海（北京）技术有限公司 | Content recommendation method and device, computer equipment and readable medium |
| CN114596138A (en) * | 2022-03-07 | 2022-06-07 | 北京字节跳动网络技术有限公司 | Information recommendation method and device, computer equipment and storage medium |
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