CN114266606A - Short video advertisement putting optimization evaluation method and system - Google Patents

Short video advertisement putting optimization evaluation method and system Download PDF

Info

Publication number
CN114266606A
CN114266606A CN202210203897.2A CN202210203897A CN114266606A CN 114266606 A CN114266606 A CN 114266606A CN 202210203897 A CN202210203897 A CN 202210203897A CN 114266606 A CN114266606 A CN 114266606A
Authority
CN
China
Prior art keywords
delivery
level
short video
social
advertisement
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.)
Granted
Application number
CN202210203897.2A
Other languages
Chinese (zh)
Other versions
CN114266606B (en
Inventor
潘小平
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Peiruiweihang Interconnection Technology Co ltd
Original Assignee
Beijing Peiruiweihang Interconnection Technology Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beijing Peiruiweihang Interconnection Technology Co ltd filed Critical Beijing Peiruiweihang Interconnection Technology Co ltd
Priority to CN202210203897.2A priority Critical patent/CN114266606B/en
Publication of CN114266606A publication Critical patent/CN114266606A/en
Application granted granted Critical
Publication of CN114266606B publication Critical patent/CN114266606B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Abstract

The invention discloses a short video advertisement putting optimization evaluation method and a system, comprising the following steps: step S1, acquiring a social network representing the hierarchical social relationship of the target user according to the social communication data, and clustering and quantizing the social network into a plurality of hierarchical topologies; and step S2, carrying out similarity matching on the consumption attributes of the target users and the product attributes of the short video advertisements to be launched in sequence, and taking the short video advertisements to be launched with high similarity matching degree as the quasi-launched short video advertisements which are launched to the target users in an increment launching level in increments according to launching priorities. The method constructs the relevance of the delivery increment between the delivery levels with the high delivery priority and the low delivery priority based on the advertisement conversion rate of the delivery level with the high delivery priority, and then adjusts the delivery amount of the delivery level with the low delivery priority based on the relevance so as to realize the optimization of the advertisement conversion rate and the delivery expenditure.

Description

Short video advertisement putting optimization evaluation method and system
Technical Field
The invention relates to the technical field of advertisement putting, in particular to a short video advertisement putting optimization evaluation method and system.
Background
In recent years, online advertising has gained increasing weight throughout the advertising industry. The real-time bidding advertisement in the online advertisement has a high proportion year by year due to the good conversion effect. The DSP (Demand-Side Platform) is used as a Demand party Platform, and bid attempt is carried out on each exposure through an advertisement trading Platform. For each bidding request, the DSP tries to map users browsing a media website and App to users which can be identified by the DSP according to Cookie Mapping or device information, and then performs flow screening, click rate/conversion rate estimation and the like according to user figures mined from user historical behaviors by the DSP so as to contribute to maximization of the ROI.
The prior art has the problems that the acquired data are relatively simple, only the user data in the station is known, and the access behaviors of the user on other websites are unknown; on the other hand, after the user data is dense enough, the descriptive label attribute of the user shows higher stability, but the above method has the defect of poor generalization capability, thereby reducing the adaptability of the delivery evaluation.
Disclosure of Invention
The invention aims to provide a short video advertisement putting optimization evaluation method and a short video advertisement putting optimization evaluation system, which aim to solve the technical problem that the adaptability of putting evaluation is reduced due to the defect of poor generalization capability in the prior art.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a short video advertisement putting optimization evaluation method comprises the following steps:
step S1, acquiring a social network representing the hierarchical social relationship of the target user according to the social communication data, clustering and quantizing the social network into a plurality of hierarchical topologies, and marking the plurality of hierarchical topologies as incremental release levels with release priorities;
step S2, similarity matching is carried out on the consumption attributes of the target users and the product attributes of the short video advertisements to be launched in sequence, and the short video advertisements to be launched with high similarity matching degree are used as quasi-launched short video advertisements which are launched to the increment launching level of the target users in an increment launching mode according to launching priorities;
step S3, counting the advertisement conversion rate of each delivery level in the incremental delivery levels according to the delivery priority, constructing the delivery incremental relevance between the delivery levels with high delivery priority and low delivery priority based on the advertisement conversion rate of the delivery level with high delivery priority, and adjusting the delivery amount of the delivery level with low delivery priority based on the relevance until the delivery of the quasi-delivery short video advertisement in all the delivery levels is completed according to the delivery amount, so as to realize the incremental adjustment of the delivery amount of the short video advertisement to achieve the optimization of the advertisement conversion rate and the delivery expenditure.
As a preferred aspect of the present invention, the obtaining a social networking hierarchy network representing a hierarchical social relationship of a target user according to social communication data includes:
setting a communication data volume threshold value representing a normalized social relationship, extracting a communication object of which the social communication data volume with a target user exceeds the communication data volume threshold value from the social communication data, and constructing the communication object as a level 1 topological node representing the level 1 social relationship of the target user;
extracting communication objects with the social communication data quantity of the 1 st to 5 th level topology nodes exceeding a communication data quantity threshold value from the social communication data in sequence, and respectively constructing the communication objects as 2 nd to 6 th level topology nodes representing the 2 nd to 6 th level social relations of the target user;
quantizing the target user into 0 th-level topological nodes, sequentially connecting 0 th-6 th-level topological nodes to obtain the social network, and taking the connection relation of the topological nodes as the edge relation of the social network.
As a preferred aspect of the present invention, the quantizing the clustering of the social networking hierarchy network into a plurality of hierarchical topologies includes:
clustering operation is carried out on the social hierarchical network based on modularity to obtain a plurality of topological communities, the topological communities where target users are located in the topological communities are used as the level 1 topology, and the rest topological communities are arranged in a reverse order according to the degree of a margin coefficient of the level 1 topology to be used as the level 1 topology
Figure 693990DEST_PATH_IMAGE001
The hierarchical topology, wherein m is characterized as the total number of topological communities, and j is a metering constant;
preferably, the second step is sequentially calculated based on the amount of the side relation coefficient
Figure 911344DEST_PATH_IMAGE002
The social affinity of the hierarchical topology to the level 1 topology includes:
in turn will be
Figure 649493DEST_PATH_IMAGE002
The edge relation data quantity of the level topology and the level 1 topology is normalized to obtain the level one
Figure 762943DEST_PATH_IMAGE003
The social affinity of the hierarchical topology and the 1 st hierarchical topology is calculated according to the formula:
Figure 970064DEST_PATH_IMAGE004
in the formula (I), the compound is shown in the specification,
Figure 459951DEST_PATH_IMAGE005
characterized by a social affinity of the jth hierarchical topology to the level 1 topology,
Figure 318186DEST_PATH_IMAGE006
the number of edge relationships represented by the jth level topology and the level 1 topology.
As a preferred aspect of the present invention, the marking of the multiple levels of topology as the incremental delivery level with the delivery priority includes:
in turn will be
Figure 133695DEST_PATH_IMAGE007
The releasing priority of the hierarchical topology is set to be k level, the hierarchical topology with the k level releasing priority is marked as the kth releasing level with the k level releasing priority, and all releasing levels are arranged according to the priority to form the incremental releasing level.
As a preferred scheme of the present invention, the similarity matching between the consumption attributes of the target users and the product attributes of the short video advertisements to be delivered in sequence includes:
quantizing the consumption attribute of the target user from a semantic form into a vector form by using an NLP tool word2vec to obtain a consumption attribute vector, and quantizing the product attribute of the short video advertisement to be delivered from the semantic form into the vector form by using the NLP tool word2vec to obtain a product attribute vector;
establishing a similarity matching formula of the consumption attribute of the target user and the product attribute of the short video advertisement to be launched based on the Euclidean distance of the consumption attribute vector and the product attribute vector, wherein the similarity matching formula is as follows:
Figure 811801DEST_PATH_IMAGE008
in the formula (I), the compound is shown in the specification,
Figure 354647DEST_PATH_IMAGE009
characterized by the similarity of the product attribute of the r-th short video advertisement to be delivered and the consumption attribute of the target user,
Figure 801809DEST_PATH_IMAGE010
Figure 522640DEST_PATH_IMAGE011
respectively representing the product attribute vector and the consumption attribute vector, wherein r is a metering constant;
setting a similarity matching threshold, and comparing the similarity of the product attribute of the short video advertisement to be launched and the consumption attribute of the target user with the similarity matching threshold in sequence, wherein,
if the similarity is
Figure 953621DEST_PATH_IMAGE009
If the similarity matching threshold is exceeded, the similarity is compared
Figure 50890DEST_PATH_IMAGE009
The corresponding r-th short video advertisement to be delivered is taken as a standard for delivering the short video advertisement;
if the similarity is
Figure 352559DEST_PATH_IMAGE009
If the similarity matching threshold is not exceeded, the similarity is compared
Figure 260603DEST_PATH_IMAGE009
And the corresponding r-th short video advertisement to be delivered is not taken as the short video advertisement to be delivered.
As a preferred scheme of the present invention, counting the advertisement conversion rate of each delivery level in incremental delivery levels according to the delivery priority includes:
counting the ratio of the number of users purchasing products in the r-th quasi-delivery short video advertisement in the k-th delivery layer of the incremental delivery layer to all the users in the k-th delivery layer in turn as the advertisement conversion rate of the r-th quasi-delivery short video advertisement in the k-th delivery layer, wherein the calculation formula of the advertisement conversion rate is as follows:
Figure 913301DEST_PATH_IMAGE012
in the formula (I), the compound is shown in the specification,
Figure 283103DEST_PATH_IMAGE013
characterized by the ad conversion rate of the r-th short video advertisement to be delivered in the k-th delivery level,
Figure 970436DEST_PATH_IMAGE014
characterized by the number of users in the kth placement level who purchased the product in the r-th quasi-placement short video advertisement,
Figure 33070DEST_PATH_IMAGE015
the characteristics are all the users in the k-th release level.
As a preferred aspect of the present invention, the constructing a delivery increment association between delivery levels with a high delivery priority and a low delivery priority based on an advertisement conversion rate of a delivery level with a high delivery priority includes:
obtaining the advertisement conversion rate of the delivery layer with high delivery priority, setting an attenuation coefficient for the advertisement conversion rate of the delivery layer with high delivery priority, attenuating the advertisement conversion rate based on the attenuation coefficient to obtain a correlation coefficient representing the relevance of the delivery increment, wherein,
the attenuation coefficient is obtained by constructing social affinity corresponding to the release level so as to realize attenuation according to the social affinity and improve attenuation scientificity, and the calculation formula of the attenuation coefficient is as follows:
Figure 428191DEST_PATH_IMAGE016
in the formula (I), the compound is shown in the specification,
Figure 867263DEST_PATH_IMAGE017
characterized by attenuation coefficients from the k-th level of delivery to the k + 1-th level of delivery,
Figure 409103DEST_PATH_IMAGE018
the social affinity is characterized by the social affinity of the k +1 th release level and the 1 st release level;
the calculation formula of the correlation coefficient is as follows:
Figure 908217DEST_PATH_IMAGE019
in the formula (I), the compound is shown in the specification,
Figure 269928DEST_PATH_IMAGE020
the characteristic is that the r-th short video advertisement to be delivered is delivered with the association coefficient of the increment in the k-th delivery level and the k + 1-th delivery level;
the release increment is characterized by the increasing rate of the release amount of the k + 1-level release level on the basis of the release amount of the k-level release level.
As a preferred aspect of the present invention, the adjusting the placement amount of the placement level having the low placement priority based on the relevance includes:
according to the correlation coefficient, the first
Figure 997844DEST_PATH_IMAGE021
The amount of the level of putting, the second
Figure 394190DEST_PATH_IMAGE022
The calculation formula of the putting amount of the level putting level is as follows:
Figure 64206DEST_PATH_IMAGE023
in the formula (I), the compound is shown in the specification,
Figure 913213DEST_PATH_IMAGE024
Figure 694088DEST_PATH_IMAGE025
respectively representing the putting quantities of the r-th quasi-put short video advertisement in the k +1 th and k-th putting levels;
setting the throwing amount of the 1 st level throwing level according to the order
Figure 459787DEST_PATH_IMAGE026
And calculating the putting amount of the r-th quasi-delivery short video advertisement in the 1 st to m-th delivery levels by using a calculation formula of the putting amount of the level delivery levels.
As a preferred scheme of the present invention, the placement amount of the level 1 placement layer is in a direct proportion relation with the similarity of the target user according to the r-th targeted short video advertisement.
As a preferred aspect of the present invention, the present invention provides an optimization evaluation system according to the short video advertisement placement optimization evaluation method, including:
the hierarchy determining unit is used for acquiring a social hierarchy network representing the hierarchy social relationship of the target user according to the social communication data, clustering and quantizing the social hierarchy network into a plurality of hierarchy topologies, and marking the plurality of hierarchy topologies as incremental release hierarchies with release priorities;
the advertisement matching unit is used for carrying out similarity matching on the consumption attributes of the target users and the product attributes of the short video advertisements to be launched in sequence, and taking the short video advertisements to be launched with high similarity matching degree as the quasi-launched short video advertisements which are launched to the increment launching level of the target users in increments according to the launching priority;
and the delivery volume determining unit is used for counting the advertisement conversion rate of each delivery level in the incremental delivery levels according to the delivery priority, constructing the delivery incremental relevance between the delivery levels with the high delivery priority and the low delivery priority based on the advertisement conversion rate of the delivery level with the high delivery priority, and then adjusting the delivery volume of the delivery level with the low delivery priority based on the relevance until the delivery of the quasi-delivery short video advertisement in all the delivery levels is completed according to the delivery volume.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, a social hierarchy network representing the hierarchy social relationship of a target user and an incremental launching hierarchy of launching priorities are obtained according to social communication data, launching incremental relevance between the launching hierarchies with high launching priorities and low launching priorities is constructed based on the advertisement conversion rate of the launching hierarchy with high launching priorities, and then the launching amount of the launching hierarchy with low launching priorities is adjusted based on the relevance, so that the incremental adjustment of the short-video advertisement launching amount is realized to achieve the optimization of advertisement conversion rate and launching expenditure.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a flowchart of a short video advertisement delivery optimization evaluation method according to an embodiment of the present invention;
fig. 2 is a block diagram of an evaluation system according to an embodiment of the present invention.
The reference numerals in the drawings denote the following, respectively:
1-a hierarchy determination unit; 2-an advertisement matching unit; and 3, a putting amount determining unit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present invention provides a short video advertisement placement optimization evaluation method, which includes the following steps:
step S1, acquiring a social network representing the hierarchical social relationship of the target user according to the social communication data, clustering and quantizing the social network into a plurality of hierarchical topologies, and marking the plurality of hierarchical topologies as incremental release levels with release priorities;
the social network of the target user represents the extraction of the social circle of the target user, and because the social circle of the target user is usually formed by person objects with similar interest characteristics to a certain extent, the product in which the target user is interested is also the product in which the person in the social network of the target user is interested to a large extent, and the mapping to the real scene is as follows: the interest characteristics of the target user are given to friends of the target user, products (namely, the commodities interested by the target user) corresponding to the interest characteristics of the target user are recommended to friends of the target user when commodity recommendation is carried out, the generalization force of product recommendation is improved, namely, the generalization force of advertisement putting is optimized, the volume of transaction is promoted, the interest degree is quantized into putting levels, and putting priorities are given to the putting levels, wherein the higher the interest degree of the putting priority is, the earlier the putting is, the lower the interest degree of the putting priority is, the later the putting is, the specific steps are as follows:
the method for acquiring the social network representing the target user hierarchical social relationship according to the social communication data comprises the following steps:
setting a communication data volume threshold value representing the normalized social relationship, extracting a communication object of which the social communication data volume with the target user exceeds the communication data volume threshold value from the social communication data, and constructing the communication object as a 1 st level topological node representing the 1 st level social relationship of the target user;
extracting communication objects with the social communication data quantity of the 1 st to 5 th level topology nodes exceeding a communication data quantity threshold value from the social communication data in sequence, and respectively constructing the communication objects as 2 nd to 6 th level topology nodes representing the 2 nd to 6 th level social relations of the target user;
and quantizing the target user into 0 th-level topological nodes, sequentially connecting the 0 th-6 th-level topological nodes to obtain a social hierarchical network, and taking the connection relation of the topological nodes as the edge relation of the social hierarchical network.
Because the social relationship can be fully covered by social personnel under the condition of 6 layers, 6 layers are selected when the social hierarchy network is constructed, and the social relationship can be increased or decreased when the social hierarchy network is used, so that the practical standard is met.
Clustering and quantizing the social hierarchical network into a plurality of hierarchical topologies, wherein the hierarchical topologies comprise the following steps:
clustering operation is carried out on the social hierarchical network based on modularity to obtain a plurality of topological communities, the topological communities where target users are located in the topological communities are used as the level 1 topology, and the rest topological communities are arranged in a reverse order according to the degree of a margin coefficient of the level 1 topology to be used as the level 1 topology
Figure 35125DEST_PATH_IMAGE001
The hierarchical topology, wherein m is characterized as the total number of topological communities, and j is a metering constant;
preferably, the second step is sequentially calculated based on the amount of the side relation coefficient
Figure 371428DEST_PATH_IMAGE002
The social affinity of the hierarchical topology to the level 1 topology includes:
in turn will be
Figure 955993DEST_PATH_IMAGE002
The edge relation data quantity of the level topology and the level 1 topology is normalized to obtain the level one
Figure 61353DEST_PATH_IMAGE027
The social intimacy between the hierarchical topology and the 1 st hierarchical topology is calculated according to the following formula:
Figure 73171DEST_PATH_IMAGE028
in the formula (I), the compound is shown in the specification,
Figure 381924DEST_PATH_IMAGE029
characterized by a social affinity of the jth hierarchical topology to the level 1 topology,
Figure 770180DEST_PATH_IMAGE030
the number of edge relationships represented by the jth level topology and the level 1 topology.
Marking a plurality of tier topologies as incremental drop tiers having drop priorities, comprising:
in turn will be
Figure 995625DEST_PATH_IMAGE031
The releasing priority of the hierarchical topology is set to be k level, the hierarchical topology with the k level releasing priority is marked as the kth releasing level with the k level releasing priority, and all releasing levels are arranged according to the priority to form the incremental releasing level.
Step S2, similarity matching is carried out on the consumption attributes of the target users and the product attributes of the short video advertisements to be launched in sequence, and the short video advertisements to be launched with high similarity matching degree are used as quasi-launched short video advertisements which are launched to the increment launching level of the target users in an increment launching mode according to launching priorities;
and performing similarity matching on the consumption attributes of the target users and the product attributes of the short video advertisements to be launched in sequence, wherein the similarity matching comprises the following steps:
quantizing the consumption attribute of the target user from a semantic form into a vector form by using an NLP tool word2vec to obtain a consumption attribute vector, and quantizing the product attribute of the short video advertisement to be delivered from the semantic form into the vector form by using the NLP tool word2vec to obtain a product attribute vector;
establishing a similarity matching formula of the consumption attribute of the target user and the product attribute of the short video advertisement to be launched based on the Euclidean distance of the consumption attribute vector and the product attribute vector, wherein the similarity matching formula is as follows:
Figure 912765DEST_PATH_IMAGE008
in the formula (I), the compound is shown in the specification,
Figure 489240DEST_PATH_IMAGE032
characterized by the similarity of the product attribute of the r-th short video advertisement to be delivered and the consumption attribute of the target user,
Figure 399296DEST_PATH_IMAGE033
Figure 213668DEST_PATH_IMAGE011
respectively representing the product attribute vector and the consumption attribute vector, wherein r is a metering constant;
setting a similarity matching threshold, and comparing the similarity of the product attribute of the short video advertisement to be delivered and the consumption attribute of the target user with the similarity matching threshold in sequence, wherein,
if the similarity is similar
Figure 832868DEST_PATH_IMAGE032
If the similarity matching threshold is exceeded, the similarity is calculated
Figure 631060DEST_PATH_IMAGE032
The corresponding r-th short video advertisement to be delivered is taken as a standard for delivering the short video advertisement;
if the similarity is similar
Figure 829960DEST_PATH_IMAGE032
If the similarity matching threshold is not exceeded, the similarity is compared
Figure 764418DEST_PATH_IMAGE032
And the corresponding r-th short video advertisement to be delivered is not taken as the short video advertisement to be delivered.
And performing accurate advertisement putting matching according to the similarity, wherein the higher the similarity is, the more the product attribute of the short video advertisement to be put conforms to the consumption attribute of the target user, the higher the product conversion possibility is, the more the product is suitable for being pushed to the target user, and the lower the similarity is, the less the product attribute of the short video advertisement to be put conforms to the consumption attribute of the target user, the lower the product conversion possibility is, the less the meaning of the product pushed to the target user is, and the watching experience of the target user is reduced.
Step S3, counting the advertisement conversion rate of each delivery level in the incremental delivery levels according to the delivery priority, constructing the delivery incremental relevance between the delivery levels with high delivery priority and low delivery priority based on the advertisement conversion rate of the delivery level with high delivery priority, and adjusting the delivery amount of the delivery level with low delivery priority based on the relevance until the delivery of the quasi-delivery short video advertisements in all the delivery levels is completed according to the delivery amount, so as to realize the incremental adjustment of the delivery amount of the short video advertisements to achieve the optimization of the advertisement conversion rate and the delivery expenditure.
Counting the advertisement conversion rate of each delivery layer in the incremental delivery layers according to the delivery priority, wherein the counting comprises the following steps:
the method comprises the following steps of counting the ratio of the number of users purchasing products in the r-th quasi-delivery short video advertisement in the k-th delivery layer of the incremental delivery layer to all the users in the k-th delivery layer in sequence to serve as the advertisement conversion rate of the r-th quasi-delivery short video advertisement in the k-th delivery layer, wherein the calculation formula of the advertisement conversion rate is as follows:
Figure 305252DEST_PATH_IMAGE034
in the formula (I), the compound is shown in the specification,
Figure 325161DEST_PATH_IMAGE013
characterized by the ad conversion rate of the r-th short video advertisement to be delivered in the k-th delivery level,
Figure 593331DEST_PATH_IMAGE014
characterized by the number of users in the kth placement level who purchased the product in the r-th quasi-placement short video advertisement,
Figure 382296DEST_PATH_IMAGE035
the characteristics are all the users in the k-th release level.
Constructing a delivery increment relevance between delivery levels with high delivery priority and low delivery priority based on the advertisement conversion rate of the delivery level with high delivery priority, comprising:
obtaining the advertisement conversion rate of the delivery layer with high delivery priority, setting an attenuation coefficient for the advertisement conversion rate of the delivery layer with high delivery priority, attenuating the advertisement conversion rate based on the attenuation coefficient to obtain a correlation coefficient representing the correlation of delivery increment, wherein,
the attenuation coefficient is obtained by constructing social affinity corresponding to the release level so as to realize attenuation according to the social affinity and improve the attenuation scientificity, and the calculation formula of the attenuation coefficient is as follows:
Figure 77719DEST_PATH_IMAGE036
in the formula (I), the compound is shown in the specification,
Figure 834191DEST_PATH_IMAGE017
characterized by attenuation coefficients from the k-th level of delivery to the k + 1-th level of delivery,
Figure 906053DEST_PATH_IMAGE018
the social affinity is characterized by the social affinity of the k +1 th release level and the 1 st release level;
the correlation coefficient is calculated by the formula:
Figure 549524DEST_PATH_IMAGE037
in the formula (I), the compound is shown in the specification,
Figure 681428DEST_PATH_IMAGE020
the characteristic is that the r-th short video advertisement to be delivered is delivered with the association coefficient of the increment in the k-th delivery level and the k + 1-th delivery level;
the release increment is characterized by the increasing rate of the release amount of the k +1 level release level on the basis of the release amount of the k level release level.
Adjusting a placement amount for a placement tier having a low placement priority based on relevance, comprising:
according to the correlation coefficient, obtain
Figure 410349DEST_PATH_IMAGE031
The amount of the level of delivery, the second
Figure 36634DEST_PATH_IMAGE031
The calculation formula of the putting amount of the level putting level is as follows:
Figure 800190DEST_PATH_IMAGE023
in the formula (I), the compound is shown in the specification,
Figure 571837DEST_PATH_IMAGE024
Figure 319213DEST_PATH_IMAGE025
respectively representing the putting quantities of the r-th quasi-put short video advertisement in the k +1 th and k-th putting levels;
setting the throwing amount of the 1 st level throwing level according to the order
Figure 467298DEST_PATH_IMAGE026
And calculating the putting amount of the r-th quasi-delivery short video advertisement in the 1 st to m-th delivery levels by using a calculation formula of the putting amount of the level delivery levels.
The lower the level of placement priority, the lower the level of interest to those in the placement hierarchy for placing short video advertisements, the advertisement conversion rate will be lower, so directly delivering the placement volume in the placement level with high placement priority to the placement level with low placement priority will cause the redundancy of placement volume, the placement volume of a drop tier with a low drop priority is thus based on the decay of the placement volume of a drop tier with a high drop priority, and the construction of the attenuation coefficient is established on the advertisement conversion rate of the delivery level with high delivery priority, the delivery amount can be reduced under the condition of ensuring that the advertisement conversion rate is not influenced, the method reduces the cost of the delivery expenditure, ensures the advertisement conversion rate, and realizes the increment adjustment of the short video advertisement delivery amount to achieve the optimization of the advertisement conversion rate and the delivery expenditure.
The putting quantity of the level 1 putting level is in a direct proportion relation with the similarity of the target user according to the r-th quasi-putting short video advertisement.
As shown in fig. 2, based on the short video advertisement delivery optimization evaluation method, the present invention provides an optimization evaluation system, which includes:
the hierarchy determining unit 1 is used for acquiring a social hierarchy network representing a hierarchy social relationship of a target user according to social communication data, clustering and quantizing the social hierarchy network into a plurality of hierarchy topologies, and marking the plurality of hierarchy topologies as incremental release hierarchies with release priorities;
the advertisement matching unit 2 is used for carrying out similarity matching on the consumption attributes of the target users and the product attributes of the short video advertisements to be launched in sequence, and taking the short video advertisements to be launched with high similarity matching degree as the quasi-launched short video advertisements which are launched to the increment launching level of the target users in increments according to the launching priority;
and the putting quantity determining unit 3 is used for counting the advertisement conversion rate of each putting layer in the incremental putting layers according to the putting priority, constructing the putting increment relevance between the putting layers with the high putting priority and the putting layers with the low putting priority based on the advertisement conversion rate of the putting layer with the high putting priority, and then adjusting the putting quantity of the putting layer with the low putting priority based on the relevance until the putting of the quasi-put short video advertisement in all the putting layers is finished according to the putting quantity.
According to the method, a social hierarchy network representing the target user hierarchy social relationship and an incremental delivery hierarchy of delivery priorities are obtained according to social communication data, delivery incremental relevance between the delivery hierarchies with high delivery priorities and low delivery priorities is constructed based on the advertisement conversion rate of the delivery hierarchy with high delivery priorities, and the delivery amount of the delivery hierarchy with low delivery priorities is adjusted based on the relevance, so that the incremental adjustment of the short-video advertisement delivery amount is realized to achieve the optimization of advertisement conversion rate and delivery expenditure.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.

Claims (10)

1. A short video advertisement putting optimization evaluation method is characterized by comprising the following steps:
step S1, acquiring a social network representing the hierarchical social relationship of the target user according to the social communication data, clustering and quantizing the social network into a plurality of hierarchical topologies, and marking the plurality of hierarchical topologies as incremental release levels with release priorities;
step S2, similarity matching is carried out on the consumption attributes of the target users and the product attributes of the short video advertisements to be launched in sequence, and the short video advertisements to be launched with high similarity matching degree are used as quasi-launched short video advertisements which are launched to the increment launching level of the target users in an increment launching mode according to launching priorities;
step S3, counting the advertisement conversion rate of each delivery level in the incremental delivery levels according to the delivery priority, constructing the delivery incremental relevance between the delivery levels with high delivery priority and low delivery priority based on the advertisement conversion rate of the delivery level with high delivery priority, and adjusting the delivery amount of the delivery level with low delivery priority based on the relevance until the delivery of the quasi-delivery short video advertisement in all the delivery levels is completed according to the delivery amount, so as to realize the incremental adjustment of the delivery amount of the short video advertisement to achieve the optimization of the advertisement conversion rate and the delivery expenditure.
2. The method of claim 1, wherein the method comprises: the method for acquiring the social network representing the target user hierarchical social relationship according to the social communication data comprises the following steps:
setting a communication data volume threshold value representing a normalized social relationship, extracting a communication object of which the social communication data volume with a target user exceeds the communication data volume threshold value from the social communication data, and constructing the communication object as a level 1 topological node representing the level 1 social relationship of the target user;
extracting communication objects with the social communication data quantity of the 1 st to 5 th level topology nodes exceeding a communication data quantity threshold value from the social communication data in sequence, and respectively constructing the communication objects as 2 nd to 6 th level topology nodes representing the 2 nd to 6 th level social relations of the target user;
quantizing the target user into 0 th-level topological nodes, sequentially connecting 0 th-6 th-level topological nodes to obtain the social network, and taking the connection relation of the topological nodes as the edge relation of the social network.
3. The method of claim 2, wherein the method comprises: clustering quantization is performed on the social hierarchical network into a plurality of hierarchical topologies, and the clustering quantization comprises the following steps:
clustering operation is carried out on the social hierarchical network based on modularity to obtain a plurality of topological communities, the topological communities where target users are located in the topological communities are used as the level 1 topology, and the rest topological communities are arranged in a reverse order according to the degree of a margin coefficient of the level 1 topology to be used as the level 1 topology
Figure 824603DEST_PATH_IMAGE001
The hierarchical topology, wherein m is characterized as the total number of topological communities, and j is a metering constant;
preferably, the second step is sequentially calculated based on the amount of the side relation coefficient
Figure 199084DEST_PATH_IMAGE002
The social affinity of the hierarchical topology to the level 1 topology includes:
in turn will be
Figure 29637DEST_PATH_IMAGE003
The edge relation data quantity of the level topology and the level 1 topology is normalized to obtain the level one
Figure 272399DEST_PATH_IMAGE004
The social affinity of the hierarchical topology and the 1 st hierarchical topology is calculated according to the formula:
Figure 104964DEST_PATH_IMAGE005
in the formula (I), the compound is shown in the specification,
Figure 650346DEST_PATH_IMAGE006
characterized by a social affinity of the jth hierarchical topology to the level 1 topology,
Figure 499353DEST_PATH_IMAGE007
the number of edge relationships represented by the jth level topology and the level 1 topology.
4. The method of claim 3, wherein the method comprises: the marking of the multiple tier topology as an incremental drop tier with drop priority comprises:
in turn will be
Figure 749069DEST_PATH_IMAGE008
The releasing priority of the hierarchical topology is set to be k level, the hierarchical topology with the k level releasing priority is marked as the kth releasing level with the k level releasing priority, and all releasing levels are arranged according to the priority to form the incremental releasing level.
5. The method of claim 4, wherein the method comprises: the similarity matching of the consumption attributes of the target users and the product attributes of the short video advertisements to be launched in sequence comprises the following steps:
quantizing the consumption attribute of the target user from a semantic form into a vector form by using an NLP tool word2vec to obtain a consumption attribute vector, and quantizing the product attribute of the short video advertisement to be delivered from the semantic form into the vector form by using the NLP tool word2vec to obtain a product attribute vector;
establishing a similarity matching formula of the consumption attribute of the target user and the product attribute of the short video advertisement to be launched based on the Euclidean distance of the consumption attribute vector and the product attribute vector, wherein the similarity matching formula is as follows:
Figure 140867DEST_PATH_IMAGE009
in the formula (I), the compound is shown in the specification,
Figure 247363DEST_PATH_IMAGE010
characterized by the similarity of the product attribute of the r-th short video advertisement to be delivered and the consumption attribute of the target user,
Figure 459033DEST_PATH_IMAGE011
Figure 43598DEST_PATH_IMAGE012
respectively representing the product attribute vector and the consumption attribute vector, wherein r is a metering constant;
setting a similarity matching threshold, and comparing the similarity of the product attribute of the short video advertisement to be launched and the consumption attribute of the target user with the similarity matching threshold in sequence, wherein,
if the similarity is
Figure 617799DEST_PATH_IMAGE010
If the similarity matching threshold is exceeded, the similarity is compared
Figure 269098DEST_PATH_IMAGE010
The corresponding r-th short video advertisement to be delivered is taken as a standard for delivering the short video advertisement;
if the similarity is
Figure 358276DEST_PATH_IMAGE010
If the similarity matching threshold is not exceeded, the similarity is compared
Figure 684216DEST_PATH_IMAGE010
And the corresponding r-th short video advertisement to be delivered is not taken as the short video advertisement to be delivered.
6. The method of claim 5, wherein the method comprises: counting the advertisement conversion rate of each delivery layer in the incremental delivery layers according to the delivery priority, wherein the counting comprises the following steps:
counting the ratio of the number of users purchasing products in the r-th quasi-delivery short video advertisement in the k-th delivery layer of the incremental delivery layer to all the users in the k-th delivery layer in turn as the advertisement conversion rate of the r-th quasi-delivery short video advertisement in the k-th delivery layer, wherein the calculation formula of the advertisement conversion rate is as follows:
Figure 316185DEST_PATH_IMAGE013
in the formula (I), the compound is shown in the specification,
Figure 233326DEST_PATH_IMAGE014
characterized by the ad conversion rate of the r-th short video advertisement to be delivered in the k-th delivery level,
Figure 685167DEST_PATH_IMAGE015
characterized by the number of users in the kth placement level who purchased the product in the r-th quasi-placement short video advertisement,
Figure 611534DEST_PATH_IMAGE016
the characteristics are all the users in the k-th release level.
7. The method of claim 6, wherein the method comprises: constructing a delivery increment relevance between delivery levels with high delivery priority and low delivery priority based on the advertisement conversion rate of the delivery level with high delivery priority comprises the following steps:
obtaining the advertisement conversion rate of the delivery layer with high delivery priority, setting an attenuation coefficient for the advertisement conversion rate of the delivery layer with high delivery priority, attenuating the advertisement conversion rate based on the attenuation coefficient to obtain a correlation coefficient representing the relevance of the delivery increment, wherein,
the attenuation coefficient is obtained by constructing social affinity corresponding to the release level so as to realize attenuation according to the social affinity and improve attenuation scientificity, and the calculation formula of the attenuation coefficient is as follows:
Figure 160327DEST_PATH_IMAGE017
in the formula (I), the compound is shown in the specification,
Figure 654894DEST_PATH_IMAGE018
characterized by attenuation coefficients from the k-th level of delivery to the k + 1-th level of delivery,
Figure 453085DEST_PATH_IMAGE019
the social affinity is characterized by the social affinity of the k +1 th release level and the 1 st release level;
the calculation formula of the correlation coefficient is as follows:
Figure 386406DEST_PATH_IMAGE020
in the formula (I), the compound is shown in the specification,
Figure 954486DEST_PATH_IMAGE021
the characteristic is that the r-th short video advertisement to be delivered is delivered with the association coefficient of the increment in the k-th delivery level and the k + 1-th delivery level;
the release increment is characterized by the increasing rate of the release amount of the k + 1-level release level on the basis of the release amount of the k-level release level.
8. The method of claim 7, wherein the adjusting the placement amount for the placement level with low placement priority based on the relevance comprises:
according to the correlation coefficient, the first
Figure 479008DEST_PATH_IMAGE022
The amount of the level of putting, the second
Figure 905441DEST_PATH_IMAGE023
The calculation formula of the putting amount of the level putting level is as follows:
Figure 642453DEST_PATH_IMAGE024
in the formula (I), the compound is shown in the specification,
Figure 696997DEST_PATH_IMAGE025
Figure 267786DEST_PATH_IMAGE026
respectively representing the putting quantities of the r-th quasi-put short video advertisement in the k +1 th and k-th putting levels;
setting the throwing amount of the 1 st level throwing level according to the order
Figure 306150DEST_PATH_IMAGE023
And calculating the putting amount of the r-th quasi-delivery short video advertisement in the 1 st to m-th delivery levels by using a calculation formula of the putting amount of the level delivery levels.
9. The method of claim 8, wherein the placement amount of the level 1 placement level is proportional to the similarity between the tth targeted short video advertisement and the target user.
10. An optimization evaluation system of the short video advertisement placement optimization evaluation method according to any one of claims 1 to 9, comprising:
the hierarchy determining unit (1) is used for acquiring a social hierarchy network representing the hierarchy social relationship of a target user according to social communication data, clustering and quantizing the social hierarchy network into a plurality of hierarchy topologies, and marking the plurality of hierarchy topologies as incremental release hierarchies with release priorities;
the advertisement matching unit (2) is used for carrying out similarity matching on the consumption attributes of the target users and the product attributes of the short video advertisements to be launched in sequence, and taking the short video advertisements to be launched with high similarity matching degree as the quasi-launched short video advertisements which are launched in increments according to launching priorities to the increment launching level of the target users;
and the delivery volume determining unit (3) is used for counting the advertisement conversion rate of each delivery level in the incremental delivery levels according to the delivery priority, constructing the delivery incremental relevance between the delivery levels with the high delivery priority and the low delivery priority based on the advertisement conversion rate of the delivery level with the high delivery priority, and then adjusting the delivery volume of the delivery level with the low delivery priority based on the relevance until the delivery of the quasi-delivery short video advertisement in all the delivery levels is completed according to the delivery volume.
CN202210203897.2A 2022-03-03 2022-03-03 Short video advertisement putting optimization evaluation method and system Active CN114266606B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210203897.2A CN114266606B (en) 2022-03-03 2022-03-03 Short video advertisement putting optimization evaluation method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210203897.2A CN114266606B (en) 2022-03-03 2022-03-03 Short video advertisement putting optimization evaluation method and system

Publications (2)

Publication Number Publication Date
CN114266606A true CN114266606A (en) 2022-04-01
CN114266606B CN114266606B (en) 2022-05-17

Family

ID=80834014

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210203897.2A Active CN114266606B (en) 2022-03-03 2022-03-03 Short video advertisement putting optimization evaluation method and system

Country Status (1)

Country Link
CN (1) CN114266606B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110264522A1 (en) * 2010-04-26 2011-10-27 Webjuice, LLC Direct targeting of advertisements to social connections in a social network environment
US20140150016A1 (en) * 2012-11-29 2014-05-29 At&T Intellectual Property I, Lp Method and apparatus for managing advertisements using social media data
US20160180375A1 (en) * 2014-12-22 2016-06-23 Lochlan H. Rose System And Method To Estimate The Incrementality Delivered By Online Campaigns Based On Measuring Impression-Level Digital Display Ad Viewability
CN109711885A (en) * 2018-12-27 2019-05-03 上海旺翔文化传媒股份有限公司 Motivate video ads intelligence put-on method
CN111598633A (en) * 2020-07-24 2020-08-28 北京淇瑀信息科技有限公司 Online advertisement putting method and device based on incremental learning and electronic equipment
CN112435067A (en) * 2020-11-30 2021-03-02 翼果(深圳)科技有限公司 Intelligent advertisement putting method and system for cross-e-commerce platform and social platform

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110264522A1 (en) * 2010-04-26 2011-10-27 Webjuice, LLC Direct targeting of advertisements to social connections in a social network environment
US20140150016A1 (en) * 2012-11-29 2014-05-29 At&T Intellectual Property I, Lp Method and apparatus for managing advertisements using social media data
US20160180375A1 (en) * 2014-12-22 2016-06-23 Lochlan H. Rose System And Method To Estimate The Incrementality Delivered By Online Campaigns Based On Measuring Impression-Level Digital Display Ad Viewability
CN109711885A (en) * 2018-12-27 2019-05-03 上海旺翔文化传媒股份有限公司 Motivate video ads intelligence put-on method
CN111598633A (en) * 2020-07-24 2020-08-28 北京淇瑀信息科技有限公司 Online advertisement putting method and device based on incremental learning and electronic equipment
CN112435067A (en) * 2020-11-30 2021-03-02 翼果(深圳)科技有限公司 Intelligent advertisement putting method and system for cross-e-commerce platform and social platform

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
D. LUGOVOY等: "Advertising in Popular Instant Messengers", 《2018 IEEE COMMUNICATION STRATEGIES IN DIGITAL SOCIETY WORKSHOP (COMSDS)》 *
李娜: "基于增量学习的精准广告投放系统研究", 《硕士电子期刊》 *

Also Published As

Publication number Publication date
CN114266606B (en) 2022-05-17

Similar Documents

Publication Publication Date Title
US20190279230A1 (en) Online Content Delivery Based on Information from Social Networks
US9727927B2 (en) Prediction of user response to invitations in a social networking system based on keywords in the user's profile
CN104090967B (en) Application program recommends method and recommendation apparatus
CN104915392B (en) A kind of microblogging forwarding behavior prediction method and device
CN107222566A (en) Information-pushing method, device and server
CN108304435A (en) Information recommendation method, device, computer equipment and storage medium
CN102365649A (en) Leveraging information in a social network for inferential targeting of advertisements
WO2014193700A1 (en) Social media pricing engine
CN106504098A (en) A kind of capitalized method of the financial investment system based on big data technology
CN108777701A (en) A kind of method and device of determining receiver
CN110097395A (en) Directional advertisement release method, device and computer readable storage medium
CN106776873A (en) A kind of recommendation results generation method and device
CN109697627A (en) System and method for using deep layer nerve language model to bid automatically
US20200098015A1 (en) Methods for determining targeting parameters and bids for online ad distribution
CN107545444A (en) A kind of card data recommendation method and device
CN114565407A (en) Advertisement delivery data analysis method and system
CN114266606B (en) Short video advertisement putting optimization evaluation method and system
CN107507023B (en) Information delivery method and device
CN106204163B (en) Method and device for determining user attribute characteristics
CN105208033B (en) A kind of colony's auxiliary based on intelligent terminal scene recommends method and system
CN107463853A (en) The method and system of audient's label analysis
CN114565408B (en) Bidding prediction method and system for advertisement putting
CN109191159B (en) Data orientation method and device, computer equipment and computer readable storage medium
CN112541010A (en) User gender prediction method based on logistic regression
CN116226537A (en) Layout and display method, device, equipment and medium of page modules in page

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