CN112446743B - Advertisement recommendation method and terminal based on block chain - Google Patents
Advertisement recommendation method and terminal based on block chain Download PDFInfo
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Abstract
The invention discloses an advertisement recommendation method and a terminal based on a block chain, which are used for acquiring a first advertisement and acquiring an advertisement label and an advertisement weight value according to an advertisement brand of the first advertisement; storing the first advertisement in the main information block chain, taking the block mark of the main information block in the main information block chain as block content to generate a tag block, and storing the tag block; when pushing is needed, acquiring a user information label of a user to be recommended to obtain a corresponding label block chain, and then obtaining a probability value to be recommended of each block identifier in a block chain set to be recommended according to the user information weight value and the advertisement weight value; and finally, selecting the advertisement to be recommended with the preset recommendation number according to the probability value to be recommended, and pushing the advertisement to be recommended to the user to be recommended according to the preset pushing requirement. The advertisement recommendation method and the advertisement recommendation device can reduce the advertisement recommendation cost and improve the advertisement recommendation effect.
Description
Technical Field
The invention relates to the technical field of information disclosure, in particular to an advertisement recommendation method and terminal based on a block chain.
Background
Advertising, as the name suggests, is advertising and tells the public of something to the public. Economical advertising refers to advertising for the purpose of commercial promotion, typically commercial advertising, which is a means of distributing information of goods or services to consumers or users through advertising media in a paid manner for the purpose of promoting the goods or services. Commercial advertisements are such economical advertisements.
The nature of the commercial also is to obtain more money, which on the one hand requires a reduction in the expenditure of the commercial and on the other hand requires a better promotional effect to make the product better marketed. Thus, if the cost of a lower economic advertisement is increased, the promotion effect may be affected, and if the cost of the economic advertisement is increased, the promotion effect may be improved, which may be uneconomical.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: a block chain-based advertisement recommendation method and a terminal are provided, so that advertisement recommendation cost is reduced and advertisement recommendation effect is improved.
In order to solve the technical problems, the invention adopts the following technical scheme:
an advertisement recommendation method based on a block chain comprises the following steps:
s1, acquiring a first advertisement, obtaining an advertisement brand of the first advertisement, and collecting a plurality of advertisement labels corresponding to the advertisement brand and advertisement weight values of each advertisement label;
s2, generating a main information block by the first advertisement and storing the main information block into a main information block chain, and acquiring a block identifier of the main information block in the main information block chain;
s3, taking the block mark of the main information block in the main information block chain as block content to generate a tag block, respectively storing the tag block into a tag block chain corresponding to each first advertisement tag, taking the advertisement weight value of the advertisement tag as the advertisement weight value of the block mark corresponding to the first advertisement, and corresponding one advertisement tag to each tag block chain;
s4, obtaining user information labels of users to be recommended, obtaining each label block chain corresponding to each user information label to obtain a block chain set to be recommended, and obtaining a probability value to be recommended of each block label in the block chain set to be recommended according to a user information weight value of each user information label and an advertisement weight value of each block label in each block label block chain to be recommended in the block chain set to be recommended, wherein the user information labels are user interest information obtained according to historical access data of the users;
s5, selecting the blocks to be recommended identification of the preset recommendation number according to the probability value to be recommended, acquiring corresponding advertisements to be recommended from the main information block chain according to the blocks to be recommended identification, and pushing the advertisements to be recommended to the users to be recommended according to preset pushing requirements.
Further, the step S5 specifically includes the following steps:
s51, marking all the block identifiers with the probability value to be recommended being greater than or equal to a first preset value in the tag block chain to be recommended as recommendation, and marking all the blocks as recommendedThe block identifiers of the to-be-recommended tag blockchains with the probability value smaller than the first preset value and larger than or equal to the second preset value are marked as to-be-recommended, the block identifiers of all the to-be-recommended tag blockchains with the probability value smaller than the second preset value are marked as not recommended, and the first preset value is (0.6P) max ，0.9P max ) The second preset value is (0.2P max ，0.4P max ) The P is max The maximum value in the probability value to be recommended is the maximum value;
s52, judging whether the number of the block identifiers marked as recommended meets the preset recommended number, if so, randomly selecting the block identifiers with the preset recommended number from the block identifiers marked as recommended as block identifiers to be recommended, and then executing the step S54, and if not, executing the step S53;
s53, taking the block identifiers marked as recommended as block identifiers to be recommended, obtaining the residual recommended number according to the preset recommended number and the block identifiers to be recommended, selecting the block identifiers with the residual recommended number from the block identifiers marked as the block identifiers to be recommended, and executing the step S54;
s54, corresponding advertisements to be recommended are obtained from the main information block chain according to the block identifiers to be recommended, and the advertisements to be recommended are pushed to the users to be recommended according to preset pushing requirements.
Further, the step S3 specifically includes the following steps:
s31, taking the advertisement brand of the first advertisement and the block mark of the main information block in the main information block chain as block contents to generate tag blocks, respectively storing the tag blocks into tag block chains corresponding to each first advertisement tag, taking the advertisement weight value of the advertisement tag as the advertisement weight value of the block mark corresponding to the first advertisement, and corresponding one advertisement tag to each tag block chain;
s32, judging whether the advertisement brand of the previous tag block in the tag block chain is the same as the advertisement brand of the newly generated tag block, if so, modifying a position symbol corresponding to the previous tag block on a tag sequence corresponding to the tag block chain into an invalid tag, and then executing a step S33, otherwise, directly executing the step S33, wherein each tag block chain corresponds to a tag sequence sequentially corresponding to the arrangement sequence of each tag block;
s33, a position symbol of a valid tag is newly added to a position corresponding to the newly generated tag block on a tag sequence corresponding to the tag block chain;
the step S4 specifically includes the following steps:
s41, acquiring user information labels of users to be recommended, and acquiring each label blockchain corresponding to each user information label to obtain a blockchain set to be recommended, wherein the user information labels are user interest information obtained according to historical access data of the users;
s42, according to the user information weight value of each user information tag and the advertisement weight value of each effective block identifier of each position symbol in the block chain set to be recommended, which is the effective tag, a pre-recommendation probability value of each effective block identifier in the block chain set to be recommended is obtained, and the pre-recommendation probability values of the same effective block identifier in the block chain set to be recommended are added to obtain a pre-recommendation probability value of each effective block identifier in the block chain set to be recommended.
Further, the first preset value is 0.8P max The second preset value is 0.2P max 。
Further, the step S1 specifically includes the following steps:
and acquiring a first advertisement to obtain an advertisement brand of the first advertisement, and collecting the first N advertisement labels corresponding to the advertisement brand and advertisement weight values of each advertisement label, wherein N is (2, 10).
Further, N is 5.
Further, the step S5 specifically includes the following steps:
and sequentially selecting the blocks to be recommended with preset recommending number according to the probability value to be recommended from large to small, acquiring corresponding advertisements to be recommended from the main information block chain according to the blocks to be recommended, and pushing the advertisements to be recommended to the users to be recommended according to preset pushing requirements.
Further, the step S4 specifically includes the following steps:
obtaining user information labels of users to be recommended, obtaining each label block chain corresponding to each user information label to obtain a set of block chains to be recommended, obtaining a probability value to be recommended of each block identifier in the set of block chains to be recommended according to user information weight values of each user information label and advertisement weight values and advertisement score values of each block identifier in each block chain to be recommended in the set of block chains to be recommended, wherein the user information labels are user interest information obtained according to historical access data of the users, and the advertisement score values of the block identifiers are score average values received by advertisements corresponding to the block identifiers.
Further, the step S4 further includes the following steps:
and if the advertisement score value of the block identifier is lower than a lowest threshold value, marking the block identifier with the advertisement score value lower than the lowest threshold value as non-recommendation.
In order to solve the technical problems, the invention adopts another technical scheme that:
a blockchain-based advertising recommendation terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of a blockchain-based advertising recommendation method as described above when the computer program is executed.
The invention has the beneficial effects that: when a first advertisement is stored, label extraction and corresponding weight value acquisition are carried out according to advertisement brands, then the first advertisement is stored in a main information blockchain, and block identifiers in the main information blockchain are stored in the label blockchain corresponding to each first advertisement label. At this time, if the advertisement recommendation of the user is required, a user information label of the user to be recommended is obtained, the corresponding label block chain is obtained by fast traversing the user information label, then the probability value to be recommended of each block mark in the block chain set to be recommended can be obtained by calculating according to the weight value generated in advance, and the advertisement recommendation is carried out according to the probability value to be recommended, so that the user can receive advertisements more interested in the user, and the advertisement recommendation effect can be improved while the advertisement recommendation cost is reduced.
Drawings
FIG. 1 is a flowchart of a block chain based advertisement recommendation method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an advertisement recommendation terminal based on a blockchain according to an embodiment of the present invention.
Description of the reference numerals:
1. an advertisement recommendation terminal based on a block chain; 2. a processor; 3. a memory.
Detailed Description
In order to describe the technical contents, the achieved objects and effects of the present invention in detail, the following description will be made with reference to the embodiments in conjunction with the accompanying drawings.
Example 1
Referring to fig. 1 to 2, the advertisement recommendation method based on blockchain provided in this embodiment includes the steps of:
s1, acquiring a first advertisement to obtain an advertisement brand of the first advertisement, and collecting a plurality of advertisement labels corresponding to the advertisement brand and advertisement weight values of each advertisement label;
wherein, the number of the advertisement labels is N, N is (2, 10), and in the embodiment, N is 5.
In this embodiment, the tag extraction and the weight value extraction of the first advertisement may be implemented by using an existing algorithm, for example, the advertisement brand may be obtained by obtaining propaganda information and user evaluation information from a public website, and then an advertisement weight value may be attached to the 5 advertisement tags according to the occurrence frequency.
S2, generating a main information block by the first advertisement and storing the main information block into a main information block chain, and acquiring a block identifier of the main information block in the main information block chain;
s3, taking the block mark of the main information block in the main information block chain as block content to generate a tag block, respectively storing the tag block into a tag block chain corresponding to each first advertisement tag, taking the advertisement weight value of the advertisement tag as the advertisement weight value of the block mark corresponding to the first advertisement, and corresponding one advertisement tag in each tag block chain;
thus, in this embodiment, assuming that the advertisement tags of the first advertisement are A, B and C, and the block identifier corresponding to the first advertisement in step S2 is a, the block identifiers a are stored in the a-blockchain, the B-blockchain, and the C-blockchain, and each block identifier a has the advertisement weight value corresponding to the advertisement tags A, B and C.
It should be appreciated that if the corresponding tag blockchain does not exist, a new piece of tag blockchain is automatically generated.
S4, obtaining user information labels of users to be recommended, obtaining each label block chain corresponding to each user information label to obtain a set of block chains to be recommended, and obtaining a probability value to be recommended of each block label in the set of block chains to be recommended according to a user information weight value of each user information label and an advertisement weight value of each block label in each block label block chain to be recommended in the set of block chains to be recommended, wherein the user information labels are user information of interest obtained according to historical access data of the users;
in this embodiment, the step S4 specifically includes the following steps:
acquiring user information labels of users to be recommended, acquiring each label blockchain corresponding to each user information label to obtain a blockchain set to be recommended, and acquiring a probability value to be recommended of each block identifier in the blockchain set to be recommended according to the user information weight value of each user information label and the advertisement weight value and the advertisement score value of each block identifier in each blockchain to be recommended under the blockchain set to be recommended, wherein the user information labels are user interest information acquired according to historical access data of the users, and the advertisement score values of the block identifiers are score average values received by advertisements corresponding to the block identifiers;
and if the advertisement score value of the block identifier is lower than the lowest threshold value, marking the block identifier with the advertisement score value lower than the lowest threshold value as non-recommendation.
In this embodiment, there are many tag blockchains corresponding to the advertisement, such as a blockchain, B blockchain, C blockchain, D blockchain, etc., and if the user information tag is B and D, only the block identifiers of the B blockchain and the D blockchain need to be determined, and the user information tag is B and D, where the user information weight values of the user are calculated in advance, and the block identifiers of the B blockchain and the D blockchain are also calculated in advance, so that the probability values to be recommended for all the block identifiers of the B blockchain and the D blockchain can be obtained quickly.
The advertisement score value of the block identifier is lower than the lowest threshold, so that the user evaluation of the advertisement corresponding to the block identifier is too low, and even if the matching degree with the user is high, the user experience is easily affected due to the fact that the advertisement quality is too poor, and accordingly the advertisement score value is taken as a calculation index, and the block identifier lower than the lowest threshold is marked as non-recommended, so that the effectiveness of advertisement recommendation is guaranteed.
S5, selecting the blocks to be recommended with the preset recommendation number according to the probability value to be recommended, acquiring corresponding advertisements to be recommended from the main information block chain according to the blocks to be recommended, and pushing the advertisements to be recommended to the users to be recommended according to the preset pushing requirements.
In this embodiment, the step S5 specifically includes the following steps:
and sequentially selecting the blocks to be recommended with preset recommendation numbers from large to small according to the probability value to be recommended, acquiring corresponding advertisements to be recommended from the main information block chain according to the blocks to be recommended, and pushing the advertisements to be recommended to the users to be recommended according to preset pushing requirements.
And sending the advertisements to be recommended corresponding to the block identifiers to be recommended with the maximum probability value to be recommended to the users to be recommended, so that the advertisements interested by the users are pushed under the condition of small recommendation calculation amount.
Example two
Referring to fig. 1 to 2, in the advertisement recommendation method based on blockchain provided in the present embodiment, based on the first embodiment, step S5 is replaced with the following steps:
s51, marking the block identifiers with the probability value to be recommended being greater than or equal to a first preset value in all the tag block chains to be recommended as recommended, marking the block identifiers with the probability value to be recommended being less than the first preset value and greater than or equal to a second preset value in all the tag block chains to be recommended as waiting for recommendation, marking the block identifiers with the probability value to be recommended being less than the second preset value in all the tag block chains to be recommended as not recommended, wherein the first preset value is (0.6P max ，0.9P max ) The second preset value is (0.2P max ，0.4P max )，P max The maximum value in the probability values to be recommended;
in this embodiment, the first preset value is 0.8P max The second preset value is 0.2P max Namely, the block mark at the head part of the probability value to be recommended is an advertisement of interest to the user and is listed as a recommendation option, and the block mark at the tail part of the probability value to be recommended is an advertisement of no interest to the user and is listed as a non-recommendation option; while for at 0.2P max -0.8P max Is an advertisement that the user may be interested in, and is thus marked as waiting for a recommendation.
S52, judging whether the number of the block identifiers marked as recommended meets the preset recommended number, if so, randomly selecting the block identifiers with the preset recommended number from the block identifiers marked as recommended as block identifiers to be recommended, then executing the step S54, and if not, executing the step S53;
s53, taking block identifiers marked as recommendation as block identifiers to be recommended, obtaining the residual recommendation number according to the preset recommendation number and the block identifiers to be recommended, selecting the block identifiers with the residual recommendation number from the block identifiers marked as the block identifiers to be recommended, and executing the step S54;
s54, corresponding advertisements to be recommended are obtained from the main information block chain according to the block identifiers to be recommended, and the advertisements to be recommended are pushed to users to be recommended according to preset pushing requirements.
For steps S52 to S54, if the number of advertisements of interest of the user exceeds the preset number of recommendations, then the block identifiers of the preset number of recommendations are randomly selected from the block identifiers as block identifiers to be recommended, and if the number of advertisements of interest is insufficient, then the block identifiers of the remaining number of recommendations are randomly selected from the advertisements of interest as block identifiers to be recommended, so that compared with the situation that the advertisements of interest of the user are directly selected from large to small, more advertisements are more likely to be seen by other users while the advertisements of interest of the user are received, and all the advertisements are guaranteed to be recommended to the user more evenly; when some advertisements with lower original probability are recommended to the user, if the user shows interest, the probability value corresponding to the user information label is transmitted and changed, so that the situation that the user is limited in the interested field is avoided, and the advertisement recommendation has more diversity and possibility.
Example III
Referring to fig. 1 to 2, in the advertisement recommendation method based on a blockchain provided in this embodiment, based on the first embodiment, the step S3 specifically includes the following steps:
s31, taking the block marks of the advertisement brands and the main information blocks of the first advertisements in the main information block chain as block contents to generate tag blocks, respectively storing the tag blocks into tag block chains corresponding to each first advertisement tag, taking the advertisement weight values of the advertisement tags as advertisement weight values of the block marks corresponding to the first advertisements, and corresponding one advertisement tag to each tag block chain;
in this embodiment, the advertising brands are also stored in the label block.
S32, judging whether the advertisement brand of the previous label block in the label block chain is the same as the advertisement brand of the newly generated label block, if so, modifying the position symbol corresponding to the previous label block on the label sequence corresponding to the label block chain into an invalid label, and then executing the step S33, otherwise, directly executing the step S33, wherein each label block chain is corresponding to a label sequence sequentially corresponding to the arrangement sequence of each label block;
s33, the position symbol of the valid tag is newly added to the position corresponding to the newly generated tag block on the tag sequence corresponding to the tag block chain.
Therefore, in this embodiment, if there are multiple tag blocks of the same advertisement brand for the advertisement, the advertisement is an iterative advertisement, so that the old advertisement is deleted, and two new and old advertisements of one advertisement brand are avoided to appear simultaneously, so as to ensure the advertisement recommendation effect.
In this embodiment, the step S4 specifically includes the following steps:
the step S4 specifically comprises the following steps:
s41, acquiring user information labels of users to be recommended, and acquiring each label blockchain corresponding to each user information label to obtain a blockchain set to be recommended, wherein the user information labels are user interest information obtained according to historical access data of the users;
s42, according to the user information weight value of each user information tag and the advertisement weight value of the effective block identifier of each position symbol of each to-be-recommended tag block chain in the to-be-recommended block chain set, obtaining a pre-recommendation probability value of each effective block identifier in the to-be-recommended block chain set, and adding the pre-recommendation probability values of the same effective block identifier in the to-be-recommended block chain set to obtain the to-be-recommended probability value of each effective block identifier in the to-be-recommended block chain set.
In this embodiment, only the B blockchain includes the block identification a, and the D blockchain does not include the block identification a, so that the pre-recommended probability value of the block identification a is the pre-recommended probability value of the block identification a on the B blockchain.
In yet other equivalent embodiments, if the set of blockchains to be recommended includes an A blockchain, a B blockchain, and a D blockchain, then both the A blockchain and the B blockchain include a blockidentifier a, whereby the pre-recommendation probability value for the blockidentifier a is the sum of the pre-recommendation probability value for the blockidentifier a on the A blockchain and the pre-recommendation probability value for the blockidentifier a on the B blockchain.
Therefore, the advertisement is divided into labels and stored in a secondary association mode, the probability value is added to reflect the interested degree of the advertisement to the user, full-text traversal and calculation of the advertisement are not needed in the process, and therefore the calculated amount is further reduced on the basis of guaranteeing the true reflection of the interested user, and the advertisement recommendation in the embodiment can improve the advertisement recommendation effect while reducing the advertisement recommendation cost.
Example IV
Referring to fig. 4, a blockchain-based advertisement recommendation terminal 1 includes a memory 3, a processor 2, and a computer program stored in the memory 3 and executable on the processor 2, wherein the processor 2 implements the steps of the blockchain-based advertisement recommendation method according to any one of the first to second embodiments when executing the computer program.
In summary, in the advertisement recommendation method and terminal based on the blockchain, when the first advertisement is stored, the label extraction and the corresponding weight value acquisition are performed according to the advertisement brand, then the first advertisement is stored in the main information blockchain, and the block identification in the main information blockchain is stored in the label blockchain corresponding to each first advertisement label. At this time, if the advertisement recommendation of the user is required, a user information label of the user to be recommended is obtained, the user information label is quickly traversed to obtain a corresponding label block chain, and then the advertisement recommendation is selected and carried out according to advertisement brands, the preset recommendation number and probability values corresponding to block identifiers, so that the user can receive advertisements more interested by the user, and the advertisement recommendation effect can be improved while the advertisement recommendation cost is reduced; meanwhile, when the message to be recommended is selected according to the probability value to be recommended, random selection is added, so that advertisement recommendation has more diversity and possibility.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent changes made by the specification and drawings of the present invention, or direct or indirect application in the relevant art, are included in the scope of the present invention.
Claims (8)
1. An advertisement recommendation method based on a block chain is characterized by comprising the following steps:
s1, acquiring a first advertisement, obtaining an advertisement brand of the first advertisement, and collecting a plurality of advertisement labels corresponding to the advertisement brand and advertisement weight values of each advertisement label;
s2, generating a main information block by the first advertisement and storing the main information block into a main information block chain, and acquiring a block identifier of the main information block in the main information block chain;
s3, taking the block mark of the main information block in the main information block chain as block content to generate a tag block, respectively storing the tag block into a tag block chain corresponding to each first advertisement tag, taking the advertisement weight value of the advertisement tag as the advertisement weight value of the block mark corresponding to the first advertisement, and corresponding one advertisement tag to each tag block chain;
s4, obtaining user information labels of users to be recommended, obtaining each label block chain corresponding to each user information label to obtain a block chain set to be recommended, and obtaining a probability value to be recommended of each block label in the block chain set to be recommended according to a user information weight value of each user information label and an advertisement weight value of each block label in each block label block chain to be recommended in the block chain set to be recommended, wherein the user information labels are user interest information obtained according to historical access data of the users;
s5, selecting the block identifiers to be recommended of a preset recommendation number according to the probability value to be recommended, acquiring corresponding advertisements to be recommended from the main information block chain according to the block identifiers to be recommended, and pushing the advertisements to be recommended to the users to be recommended according to preset pushing requirements;
the step S5 specifically includes the following steps:
s51, marking all the block identifiers with the probability value of being greater than or equal to a first preset value in the tag chain to be recommended as recommended, marking all the block identifiers with the probability value of being less than the first preset value and greater than or equal to a second preset value in the tag chain to be recommended as waiting for recommendation, marking all the block identifiers with the probability value of being less than the second preset value in the tag chain to be recommended as not recommended, wherein the first preset value is (0.6P max ，0.9P max ) The second preset value is (0.2P max ，0.4P max ) The P is max The maximum value in the probability value to be recommended is the maximum value;
s52, judging whether the number of the block identifiers marked as recommended meets the preset recommended number, if so, randomly selecting the block identifiers with the preset recommended number from the block identifiers marked as recommended as block identifiers to be recommended, and then executing the step S54, and if not, executing the step S53;
s53, taking the block identifiers marked as recommended as block identifiers to be recommended, obtaining the residual recommended number according to the preset recommended number and the block identifiers to be recommended, selecting the block identifiers with the residual recommended number from the block identifiers marked as the block identifiers to be recommended, and executing the step S54;
s54, acquiring corresponding advertisements to be recommended from the main information block chain according to the block identifiers to be recommended, and pushing the advertisements to be recommended to the users to be recommended according to preset pushing requirements;
the step S3 specifically comprises the following steps:
s31, taking the advertisement brand of the first advertisement and the block mark of the main information block in the main information block chain as block contents to generate tag blocks, respectively storing the tag blocks into tag block chains corresponding to each first advertisement tag, taking the advertisement weight value of the advertisement tag as the advertisement weight value of the block mark corresponding to the first advertisement, and corresponding one advertisement tag to each tag block chain;
s32, judging whether the advertisement brand of the previous tag block in the tag block chain is the same as the advertisement brand of the newly generated tag block, if so, modifying a position symbol corresponding to the previous tag block on a tag sequence corresponding to the tag block chain into an invalid tag, and then executing a step S33, otherwise, directly executing the step S33, wherein each tag block chain corresponds to a tag sequence sequentially corresponding to the arrangement sequence of each tag block;
s33, a position symbol of a valid tag is newly added to a position corresponding to the newly generated tag block on a tag sequence corresponding to the tag block chain;
the step S4 specifically includes the following steps:
s41, acquiring user information labels of users to be recommended, and acquiring each label blockchain corresponding to each user information label to obtain a blockchain set to be recommended, wherein the user information labels are user interest information obtained according to historical access data of the users;
s42, according to the user information weight value of each user information tag and the advertisement weight value of each effective block identifier of each position symbol in the block chain set to be recommended, which is the effective tag, a pre-recommendation probability value of each effective block identifier in the block chain set to be recommended is obtained, and the pre-recommendation probability values of the same effective block identifier in the block chain set to be recommended are added to obtain a pre-recommendation probability value of each effective block identifier in the block chain set to be recommended.
2. The blockchain-based advertisement recommendation method of claim 1, wherein the first preset value is 0.8P max The second preset value is 0.2P max 。
3. The method for recommending advertisements based on blockchain as in claim 1, wherein the step S1 specifically comprises the steps of:
and acquiring a first advertisement to obtain an advertisement brand of the first advertisement, and collecting the first N advertisement labels corresponding to the advertisement brand and advertisement weight values of each advertisement label, wherein N is (2, 10).
4. The blockchain-based advertisement recommendation method of claim 3, wherein N is 5.
5. The blockchain-based advertisement recommendation method according to claim 1, wherein the step S5 specifically includes the steps of:
and sequentially selecting the blocks to be recommended with preset recommending number according to the probability value to be recommended from large to small, acquiring corresponding advertisements to be recommended from the main information block chain according to the blocks to be recommended, and pushing the advertisements to be recommended to the users to be recommended according to preset pushing requirements.
6. The blockchain-based advertisement recommendation method of claim 5, wherein the step S4 specifically includes the steps of:
obtaining user information labels of users to be recommended, obtaining each label block chain corresponding to each user information label to obtain a set of block chains to be recommended, obtaining a probability value to be recommended of each block identifier in the set of block chains to be recommended according to user information weight values of each user information label and advertisement weight values and advertisement score values of each block identifier in each block chain to be recommended in the set of block chains to be recommended, wherein the user information labels are user interest information obtained according to historical access data of the users, and the advertisement score values of the block identifiers are score average values received by advertisements corresponding to the block identifiers.
7. The blockchain-based advertisement recommendation method of claim 6, wherein the step S4 further comprises the steps of:
and if the advertisement score value of the block identifier is lower than a lowest threshold value, marking the block identifier with the advertisement score value lower than the lowest threshold value as non-recommendation.
8. A blockchain-based advertising recommendation terminal comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized by: the processor, when executing the computer program, implements the steps of a blockchain-based advertisement recommendation method as defined in any of claims 1-7.
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| CN104965890A (en) * | 2015-06-17 | 2015-10-07 | 深圳市腾讯计算机系统有限公司 | Advertisement recommendation method and apparatus |
| CN108764975A (en) * | 2018-05-13 | 2018-11-06 | 深圳纬目信息技术有限公司 | A kind of method of advertisement spreading and device based on block chain |
| CN111582913A (en) * | 2020-04-21 | 2020-08-25 | 北京龙云科技有限公司 | Advertisement recommendation method and device |
| CN111667300A (en) * | 2020-05-18 | 2020-09-15 | 范国闯 | Automatic advertisement trading and putting method and system based on block chain intelligent contract |
| CN111767466A (en) * | 2020-09-01 | 2020-10-13 | 腾讯科技（深圳）有限公司 | Recommendation information recommendation method and device based on artificial intelligence and electronic equipment |
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| US20090222316A1 (en) * | 2008-02-28 | 2009-09-03 | Yahoo!, Inc. | Method to tag advertiser campaigns to enable segmentation of underlying inventory |
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| CN104965890A (en) * | 2015-06-17 | 2015-10-07 | 深圳市腾讯计算机系统有限公司 | Advertisement recommendation method and apparatus |
| CN108764975A (en) * | 2018-05-13 | 2018-11-06 | 深圳纬目信息技术有限公司 | A kind of method of advertisement spreading and device based on block chain |
| CN111582913A (en) * | 2020-04-21 | 2020-08-25 | 北京龙云科技有限公司 | Advertisement recommendation method and device |
| CN111667300A (en) * | 2020-05-18 | 2020-09-15 | 范国闯 | Automatic advertisement trading and putting method and system based on block chain intelligent contract |
| CN111767466A (en) * | 2020-09-01 | 2020-10-13 | 腾讯科技（深圳）有限公司 | Recommendation information recommendation method and device based on artificial intelligence and electronic equipment |
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