CN111833142A - Information push processing method, device, equipment and storage medium - Google Patents
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Abstract
The application provides an information push processing method, an information push processing device, information push processing equipment and a storage medium, wherein the method comprises the following steps: grouping a user set according to a preset user grouping feature set to obtain at least one user subset, wherein the user grouping feature set comprises at least one user grouping feature, and the user grouping feature comprises at least one grouping index and at least one grouping division point of each grouping index; for any user subset, determining the price sensitivity of the users in the user subset according to the user grouping characteristics corresponding to the user subset; and pushing the item information according to the price sensitivity of the users in at least one user subset. The method is beneficial to improving the rationality of pushing the article information, so that the transaction rate of the article is improved, the effectiveness of pushing the article information is ensured, the maximization of the user value is facilitated, and the article resource allocation effect is obvious.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to an information push processing method, an information push processing apparatus, an information push processing device, and a storage medium.
Background
With the development of the internet industry, the internet economy plays an increasingly important role in the life of people, and the competitive pressure between the internet economies is gradually obvious. The fine operation is a key factor for improving the economic competitiveness of the internet, wherein the accurate pushing of the article information to the user is an important content of the fine operation.
In the related art, when pushing the article information, the article information is pushed to all potential users, and the users decide whether to purchase the article information according to the requirements.
However, different users have different price sensitivities to the price of the article, so that the article information is pushed to all the users indiscriminately, and the article information received by part of the users may not be matched with the price sensitivities to the price of the article, so that the pushing of the article information is unreasonable, the distribution effect of the article resources is poor, and the utilization rate is low.
Disclosure of Invention
In order to solve the above problem, the present application provides an information push processing method, apparatus, device and storage medium.
In a first aspect, the present application provides an information push processing method, including:
grouping a user set according to a preset user grouping feature set to obtain at least one user subset, wherein the user grouping feature set comprises at least one user grouping feature, and the user grouping feature comprises at least one grouping index and at least one grouping division point of each grouping index;
for any user subset, determining the price sensitivity of the users in the user subset according to the user grouping characteristics corresponding to the user subset;
and pushing the item information according to the price sensitivity of the users in the at least one user subset.
Further, the determining, for any user subset, the price sensitivity of the users in the user subset according to the user grouping feature corresponding to the user subset includes:
aiming at any user subset, determining the price sensitivity corresponding to the user grouping feature according to the user grouping feature corresponding to the user subset; and
and the price sensitivity corresponding to the user grouping characteristics forms the price sensitivity of the users in the user subset.
Further, the price sensitivity corresponding to the user grouping feature is determined by:
grouping the sampling user set according to a preset user grouping feature set to obtain at least one sampling user subset;
aiming at any sampling user subset, determining transaction probability data of the sampling users according to the acquired historical behavior data of the sampling users;
determining the price sensitivity of the sampling user according to the transaction probability data of the sampling user; and
the price sensitivity of the sampling user constitutes a price sensitivity corresponding to the user grouping feature.
Further, the determining transaction probability data of the sampling user according to the acquired historical behavior data of the sampling user includes:
determining transaction probability data of the sampling user aiming at each item price data in at least one item price data according to the acquired historical behavior data of the sampling user;
the determining the price sensitivity of the sampling user according to the transaction probability data of the sampling user comprises the following steps:
determining price sensitivity of the sampling user for each item price according to the transaction probability data of the sampling user for each item price data;
the sampling of the price sensitivity of the user constitutes a price sensitivity corresponding to the user grouping feature, including:
and the price sensitivity of the user aiming at the price of any item is sampled to form the price sensitivity corresponding to the user grouping characteristic aiming at the price of the item.
Further, the determining the price sensitivity of the sampling user according to the transaction probability data of the sampling user further includes:
determining the normalized price sensitivity of the sampling user according to the transaction probability data of the sampling user aiming at each item price data;
the sampling of the price sensitivity of the user constitutes a price sensitivity corresponding to the user grouping feature, including:
and the normalized price sensitivity of the user is sampled to form the price sensitivity corresponding to the user grouping characteristic.
Further, when item information pushing is performed on a certain target item, the pushing of item information according to the price sensitivity of users in the at least one user subset includes:
determining the item price of the target item corresponding to each user subset according to the price sensitivity of the users in the at least one user subset;
pushing item information according to the item price of the target item corresponding to each user subset,
wherein the price sensitivity of any subset of users for the price of the item to which it corresponds is not below a first preset threshold.
Further, each user subset corresponds to an item price of the target item, and an average item price of all the user subsets corresponding to the target item is not lower than a second preset threshold.
In a second aspect, the present application provides an information push processing apparatus, including:
the system comprises a first processing unit, a second processing unit and a third processing unit, wherein the first processing unit is used for grouping a user set according to a preset user grouping feature set to obtain at least one user subset, the user grouping feature set comprises at least one user grouping feature, and the user grouping feature comprises at least one grouping index and at least one grouping division point of each grouping index;
the second processing unit is used for determining the price sensitivity of the users in the user subset according to the user grouping characteristics corresponding to the user subset aiming at any user subset;
and the third processing unit is used for pushing the item information according to the price sensitivity of the users in the at least one user subset.
Further, the second processing unit includes:
the first processing subunit is used for determining the price sensitivity corresponding to the user grouping feature according to the user grouping feature corresponding to any user subset;
and the second processing subunit is used for forming the price sensitivity of the users in the user subset by using the price sensitivity corresponding to the user grouping characteristics.
Further, the apparatus further comprises:
a fourth processing unit, configured to determine a price sensitivity corresponding to the user grouping feature;
the fourth processing unit includes:
the third processing subunit is used for carrying out grouping processing on the sampling user set according to a preset user grouping feature set to obtain at least one sampling user subset;
the fourth processing subunit is used for determining transaction probability data of the sampling users according to the acquired historical behavior data of the sampling users aiming at any sampling user subset;
and the fifth processing subunit is used for determining the price sensitivity of the sampling user according to the transaction probability data of the sampling user, wherein the price sensitivity of the sampling user forms the price sensitivity corresponding to the user grouping characteristic.
Further, the fourth processing subunit includes:
the first processing module is used for determining transaction probability data of the sampling user aiming at each item price data in at least one item price data according to the acquired historical behavior data of the sampling user;
the fifth processing subunit includes:
the second processing module is used for determining the price sensitivity of the sampling user for each item price according to the transaction probability data of the sampling user for each item price data;
the fifth processing subunit further includes:
and the third processing module is used for forming the price sensitivity of the sampling user for the price of any item, which corresponds to the user grouping characteristics and aims at the price of the item.
Further, the fifth processing subunit further includes:
the fourth processing module is used for determining the normalized price sensitivity of the sampling user according to the transaction probability data of the sampling user aiming at each item price data;
the fifth processing subunit further includes:
and the fifth processing module is used for forming the price sensitivity corresponding to the user grouping characteristic by using the normalized price sensitivity of the sampling user.
Further, the third processing unit includes:
a sixth processing subunit, configured to determine, according to price sensitivities of users in the at least one user subset, an item price of the target item corresponding to each user subset;
and the seventh processing subunit is configured to perform item information pushing according to the item price of the target item corresponding to each user subset, where a price sensitivity of any user subset to the item price corresponding thereto is not lower than a first preset threshold.
Further, each user subset corresponds to an item price of the target item, and an average item price of all the user subsets corresponding to the target item is not lower than a second preset threshold.
In a third aspect, the present application provides an information push processing apparatus, including: a processor, a memory, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program for execution by a processor to perform the method of any of the first aspects.
The application provides an information push processing method, an information push processing device and a storage medium, wherein at least one user subset is obtained by grouping a user set according to a preset user grouping feature set, wherein the user grouping feature set comprises at least one user grouping feature, and the user grouping feature comprises at least one grouping index and at least one grouping division point of each grouping index; for any user subset, determining the price sensitivity of the users in the user subset according to the user grouping characteristics corresponding to the user subset; and pushing the item information according to the price sensitivity of the users in at least one user subset. According to the user grouping feature set, the users are divided into different user subsets, the price sensitivity of the users in the user subsets is determined according to the user grouping features corresponding to the user subsets, then the item information is pushed according to the price sensitivity of the users in the user subsets, different item information pushing strategies are formulated for the different user subsets, and the item information matched with the price sensitivity of the users in the different user subsets is pushed for the users in the different user subsets.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a schematic flowchart of an information push processing method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of another information push processing method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an information push processing apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of another information push processing apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an information push processing device according to an embodiment of the present application.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The application has the specific application scenarios that: with the development of the internet industry, the internet economy plays an increasingly important role in the life of people, and the competitive pressure between the internet economies is gradually obvious. The fine operation is a key factor for improving the economic competitiveness of the internet, wherein the accurate pushing of the article information to the user is an important content of the fine operation. For example, coupon marketing is an effective transaction stimulation method, and in the coupon marketing process, how to accurately push coupon information to a user is achieved, so that subsidy cost is effectively controlled, and the user is stimulated to improve the transaction rate; for another example, the package card is an intelligent marketing means in the shared bicycle service, how to accurately push package card information to the user is realized, the package card purchase rate is effectively improved, the user stickiness degree is improved, the package card subsidy cost is controlled, and the user value maximization is realized.
In the related art, when pushing the article information, the article information is pushed to all potential users, and the users decide whether to purchase the article information according to the requirements. However, different users have different price sensitivities to the price of the article, so that the article information is pushed to all the users indiscriminately, and the article information received by part of the users may not be matched with the price sensitivities to the price of the article, so that the pushing of the article information is unreasonable, the distribution effect of the article resources is poor, and the utilization rate is low.
The application provides an information push processing method, an information push processing device, information push processing equipment and a storage medium, and aims to solve the technical problems in the prior art.
Fig. 1 is a schematic flow diagram of an information push processing method according to an embodiment of the present application, and as shown in fig. 1, the method includes:
In this embodiment, specifically, the execution main body of this embodiment is a terminal device, a server arranged on the terminal device, a controller, or other devices or devices that can execute this embodiment, and this embodiment is described by taking the execution main body as the server arranged on the terminal device as an example.
The method is mainly used for solving the problem of pushing the article information in the internet economy, and aims to push the article information matched with the price sensitivity of the user to different users so as to improve the effectiveness and reasonability of pushing the article information and reduce the cost of pushing the article information while improving the article transaction rate of the user. The item information in this embodiment includes coupon information, package card information, discount information, and the like of the item. The present embodiment will be explained with a month card coupon in the shared bicycle service as the article information.
The preset user grouping feature set is a basis for grouping the users, and may be an attribute feature set of the users themselves or a historical behavior feature set of the users. The user grouping feature set comprises at least one user grouping feature, the user grouping feature is a single element influencing user grouping and can be a certain historical behavior feature of the user for the target item, and for example, the user grouping feature can be the last consumption time, consumption frequency or consumption amount of the user for the target item. For example, for a month card in a shared bicycle service, the user grouping characteristic may be the time when the user purchased the month card last, the frequency of purchasing the month card, or the cumulative amount of consumption of purchasing the month card.
The user grouping characteristics comprise at least one grouping index and at least one grouping division point of each grouping index, wherein the grouping index is a name corresponding to the user grouping characteristics, the grouping division point is a value corresponding to the user grouping characteristics, and different grouping division points can correspond to the same or different user subsets.
According to a preset user grouping feature set, grouping the user set to obtain at least one user subset, comprising: and according to a preset user grouping feature set, grouping the user set by using a user behavior analysis model (RFM model). In the RFM model, the user grouping characteristics are a time interval (R value) of the last two consumptions of the user for the target item, a consumption frequency (F value) and a consumption amount (M value), wherein the time interval (R value) of the last two consumptions refers to the time interval of the last consumption and the last consumption of the user, the consumption frequency (F value) refers to the consumption times of the user in a fixed time, and the consumption amount (M value) refers to the consumption amount of the user in the fixed time. The user grouping feature setting method comprises the steps of setting different priorities for each user grouping feature in advance according to grouping division points corresponding to each user grouping feature in a user grouping feature set, wherein the different priorities correspond to different grouping division point ranges, when the user set is subjected to grouping processing, firstly determining the priorities of each user grouping feature corresponding to a user based on the grouping division points corresponding to each user grouping feature of the user, then overlapping the priorities of each user grouping feature to obtain a total priority corresponding to the user, and dividing the user into different user subsets according to the total priorities corresponding to the user, wherein the different user subsets correspond to different total priorities of the user.
The embodiment is described by taking the consumption behavior of the user on the shared bicycle service within 1 year as an example. For the shared bicycle service, the consumption behaviors of the user for the shared bicycle service include riding and purchasing a monthly card (hereinafter referred to as "card purchasing"), based on the RFM model, the user grouping features include a time interval (R value) between the last riding or card purchasing of the user and the last riding or card purchasing, the number of times (F value) that the user rides or purchases the card within 1 year, and the amount of money that the user rides or purchases the card within 1 year. According to the priority determination method shown in table 1, different priorities are set for the R value, the F value, and the M value, respectively:
TABLE 1
| Priority level | R value, day | F value of | M value, element |
| 0 | (-∞，3] | (-∞，150] | (-∞，130] |
| 1 | (3，9] | (150，172] | (130，3475] |
| 2 | (9，+∞) | (172，+∞) | (3475，+∞) |
The user A is a certain user in the user set, the R value corresponding to the user is assumed to be 1 day, the F value is 186 times, and the M value is 250 elements, grouping processing is performed on the user A, firstly, the R value priority corresponding to the user is determined to be 0, the F value priority is 2, and the M value priority is 1 based on the R value, the F value priority and the M value priority, then, the R value priority, the F value priority and the M value priority are overlapped to obtain the total priority of the user, and then, the user is divided into corresponding user subsets according to the total priority corresponding to the user. Here, "superposition" may mean direct addition or addition after weighting.
The method for performing grouping processing on the user set according to the preset user grouping feature set to obtain at least one user subset is only used for explaining the present embodiment and is not used for limiting the present application.
And 102, aiming at any user subset, determining the price sensitivity of the users in the user subset according to the user grouping characteristics corresponding to the user subset.
In this embodiment, specifically, all users in each user subset are taken as a user group, and the price sensitivity corresponding to the user group, that is, the price sensitivity of the users in the user subset, is determined according to the user grouping feature corresponding to the user group. The price sensitivity represents the change of the user to the purchase rate of the corresponding item after the price data of the item is changed, for example, for the monthly card in the shared bicycle service, the price sensitivity of the user to the price of the monthly card refers to the absolute value of the decrease (or increase) of the purchase rate of the monthly card of the user after the price of the monthly card increases (or decreases) each unit quantity. Thus, the price sensitivity of users in the user subset can characterize the purchase rate of the items by the users in the user subset according to the price data of the items.
The method for determining the price sensitivity of the users in the user subset according to the user grouping characteristics corresponding to the user subset comprises the following steps: and determining the item purchase rate of the user aiming at each item price data according to the user grouping characteristics corresponding to the user subset by utilizing an XGboost algorithm, and determining the price sensitivity of the user in the user subset according to the item purchase rate.
And 103, pushing the item information according to the price sensitivity of the users in at least one user subset.
In this embodiment, specifically, according to the price sensitivity of the users in each user subset, the item information is preferentially pushed to the users in the user subset with high price sensitivity. For the same item, the item transaction rate corresponding to the user with higher price sensitivity is higher, the item information is preferentially pushed to the users in the user subset with the price sensitivity higher than the preset threshold value, the transaction rate of the item is favorably improved, the effectiveness of pushing the item information is ensured, and the maximization of the user value is favorably realized.
In this embodiment, a user set is subjected to grouping processing according to a preset user grouping feature set to obtain at least one user subset, where the user grouping feature set includes at least one user grouping feature, and the user grouping feature includes at least one grouping index and at least one grouping division point of each grouping index; for any user subset, determining the price sensitivity of the users in the user subset according to the user grouping characteristics corresponding to the user subset; and pushing the item information according to the price sensitivity of the users in at least one user subset. According to the user grouping feature set, the users are divided into different user subsets, the price sensitivity of the users in the user subsets is determined according to the user grouping features corresponding to the user subsets, then the item information is pushed according to the price sensitivity of the users in the user subsets, different item information pushing strategies are formulated for the different user subsets, and the item information matched with the price sensitivity of the users in the different user subsets is pushed for the users in the different user subsets.
Fig. 2 is a schematic flow diagram of another information push processing method according to an embodiment of the present application, and based on fig. 1, as shown in fig. 2, the method includes:
The method and principle of step 201 are similar to or the same as those of step 101, and refer to the related description of step 101, which is not described herein again.
In this embodiment, specifically, the user referred to in this embodiment refers to a user who will push the item information, the subset of users is composed of the users who will push the item information, and the method of this embodiment is used to determine the price sensitivity of the user who will push the item information, so as to provide a basis for subsequent pushing of the item information.
And for any user subset, the corresponding relation between the user grouping characteristics corresponding to the user subset and the price sensitivity is predetermined and stored. When determining the price sensitivity of any user needing to push the item information, firstly determining a user subset to which the user belongs, and then determining the price sensitivity of the user according to the user grouping characteristics corresponding to the user subset to which the user belongs and the corresponding relationship between the user grouping characteristics corresponding to the user subset which is predetermined and stored and the price sensitivity; determining a user subset to which the user belongs, completing the determination in step 201, determining the price sensitivity of the user according to the user molecular characteristics corresponding to the user subset to which the user belongs, and the corresponding relationship between the user grouping characteristics corresponding to the user subset which is predetermined and stored and the price sensitivity, and completing the determination in step 202.
Optionally, the price sensitivity corresponding to the user grouping feature, which is predetermined and stored, is determined by the following method:
grouping the sampling user set according to a preset user grouping feature set to obtain at least one sampling user subset; aiming at any sampling user subset, determining transaction probability data of sampling users according to the acquired historical behavior data of the sampling users; and determining the price sensitivity of the sampling user according to the transaction probability data of the sampling user, wherein the price sensitivity of the sampling user forms the price sensitivity corresponding to the user grouping characteristics. The method and the principle for grouping the sampling user set according to the preset user grouping feature set to obtain at least one sampling user subset are similar or identical to the method and the principle recorded in step 101, see the related records of step 101, and are not described herein again.
The transaction probability data is used to represent the probability that the user purchases the corresponding item with the item price data, and for the same item price data, all users belonging to the same user subset have the same transaction probability data in this embodiment. For the monthly card in the shared bicycle service, the frequency period of the monthly card purchased by the user is longer, usually more than one month, so that the times of purchasing the monthly card by the user are generally less, and therefore, the purchase probability calculated by dividing the purchase times by the total browsing times in the related technology is not accurate enough; moreover, when the user purchases the monthly card for a plurality of times, the transaction price of each time is generally the same, which is not beneficial to directly calculating the purchase probability of the user aiming at the price data of each item. Therefore, in the present embodiment, the transaction probability data of the sampling user is determined according to the acquired historical behavior data of the sampling user.
For any sampling user subset, transaction probability data of the sampling user for each item price data in at least one item price data can be determined according to the acquired historical behavior data of the sampling user by utilizing an evaluation model obtained by pre-training, wherein the transaction probability data of the sampling user for any item price data form a transaction probability data set of the sampling user subset, and at least one item price data form an item price data set. The pre-trained evaluation model is used for representing the relevance between the user grouping characteristics corresponding to the sampling user subset and the transaction probability data corresponding to each item price data.
The evaluation model can be obtained by training the following method: establishing a model about sampling historical behavior data of a user; acquiring a historical behavior data set corresponding to a sampling user subset as a training sample, wherein the historical behavior data set corresponding to the sampling user subset comprises historical behavior data corresponding to at least one sampling user subset; and carrying out model training according to the training samples to obtain an evaluation model. Preferably, the evaluation model about the historical behavior data of the sampling user can be established and trained by using the XGBoost algorithm, and the method and principle for establishing and training the relevant model by using the XGBoost algorithm are conventional in the field, for example, a first evaluation model can be established and trained according to the training set and the price data of the first item, and the first evaluation model is used for evaluating the correlation between the historical behavior data of the sampling user and the transaction probability data corresponding to the price data of the first item; on the basis of the first evaluation model, a second evaluation model is established and trained according to the training set and the price data of the second article, and the second evaluation model is used for evaluating the relevance between the historical behavior data of the sampling user and the transaction probability data corresponding to the price data of the second article; and analogizing in turn, on the basis of the evaluation model obtained by training, gradually increasing and training the new evaluation model by using the training set and the new item price data until all the item price data are traversed to obtain the evaluation model for evaluating the relevance between the historical behavior data of the sampling user and the transaction probability data corresponding to all the item price data. And when determining the transaction probability data of the sampling user aiming at least one item price data according to the historical behavior data of the sampling user by using the evaluation model obtained by training, respectively determining the transaction probability data of the sampling user aiming at each item price data, and then adding all the transaction probability data to obtain the transaction probability data of the sampling user aiming at all the item price data.
Or, for any sampling user subset, the transaction probability data of the sampling user can be determined according to the historical behavior data corresponding to the sampling user subset by using a data statistics method. Specifically, the transaction probability data of the sampling user can be determined according to consumption frequency (R value) data corresponding to the sampling user subset by using a data statistical method. For example, for a monthly card in the shared bicycle service, the card purchase rate of the sampling user is the transaction probability, and according to the user subset dividing method described in step 101, it can be known that, in a certain time period, a certain sampling user subset corresponds to a certain number of riding times and card purchase times, and the proportion of the card purchase times to the total times of the riding times and the card purchase times can be used as the card purchase rate corresponding to the sampling user subset, that is, the transaction probability data of the sampling user.
The two methods for determining the transaction probability data of the sampling user described in this embodiment are only used for explaining this embodiment, and are not used to limit this application.
As a preferable solution of this embodiment, determining transaction probability data of the sampling user according to the acquired historical behavior data of the sampling user includes: and determining transaction probability data of the sampling user aiming at each item price data in the at least one item price data according to the acquired historical behavior data of the sampling user. Determining price sensitivity of the sampling user according to the transaction probability data of the sampling user, comprising: and determining the price sensitivity of the sampling user for each item price according to the transaction probability data of the sampling user for each item price data. Sampling the price sensitivity of the user to form the price sensitivity corresponding to the user grouping characteristics, wherein the price sensitivity comprises the following steps: and sampling the price sensitivity of the user for the price of any item, and forming the price sensitivity corresponding to the user grouping characteristics and aiming at the price of the item. In the preferred scheme, the price sensitivity of the sampling user for each item price is respectively determined, and the price data which is most matched with the users in the user subset can be determined according to the price sensitivity of the sampling user for each item price.
Wherein, according to the transaction probability data of the sampling user aiming at each item price data, determining the price sensitivity of the sampling user aiming at each item price comprises the following steps: and determining an incidence relation between the item price data set and the transaction probability data of the sampling user aiming at each item price data, and determining the price sensitivity of the sampling user aiming at each item price according to the incidence relation. Specifically, a correlation curve is generated through fitting according to the item price data set and the transaction probability data of the sampling user aiming at the at least one item price data, the slope of the correlation curve at the at least one item price data is determined, and the price sensitivity of the sampling user aiming at each item price data in the at least one item price data is obtained. More specifically, each item price data in the item price data set is used as an abscissa, transaction probability data of a sampling user aiming at the item price data is used as an ordinate, and a user transaction probability-item price data correlation curve graph is established; and determining the corresponding point of each item price data on the association curve, determining the slope of a straight line between the point corresponding to the current item price data and the point corresponding to the next item price data, and taking the reciprocal of the slope as the price sensitivity of the sampling user for the current item price.
As another preferable scheme of this embodiment, determining the transaction probability data of the sampling user according to the acquired price sensitivity of the sampling user includes: and determining transaction probability data of the sampling user aiming at each item price data in the at least one item price data according to the acquired price sensitivity of the sampling user. Determining price sensitivity of the sampling user according to the transaction probability data of the sampling user, comprising: and determining the normalized price sensitivity of the sampling user according to the transaction probability data of the sampling user aiming at the price data of each item. Sampling the price sensitivity of the user to form the price sensitivity corresponding to the user grouping characteristics, wherein the price sensitivity comprises the following steps: and sampling the normalized price sensitivity of the user to form the price sensitivity corresponding to the user grouping characteristics. In the preferred scheme, the price sensitivity of the sampling user for all item prices is determined, specifically, the sensitivity of the sampling user for item price data variation is determined, and a reasonable item information push strategy can be formulated for the users in the user subset according to the sensitivity of the sampling user for item price data variation.
Wherein, according to the transaction probability data of the sampling user aiming at each item price data, determining the normalized price sensitivity of the sampling user comprises: and establishing a user transaction probability-item price data association curve graph by taking the single item price data in the item price data set as an abscissa and the transaction probability data of the sampled user as an ordinate, determining corresponding points of each item price data on the association curve, and performing linear fitting on the points to obtain a normalized straight line of the association curve, wherein the slope of the normalized straight line is the normalized slope of the association curve, and the reciprocal of the normalized slope is taken as the normalized price sensitivity of the sampled user.
The method for determining the transaction probability data and the price sensitivity of the sampling user is only used for explaining the embodiment and is not used for limiting the application.
And step 203, pushing the item information according to the price sensitivity of the users in at least one user subset.
Optionally, in this embodiment, pushing the item information according to the price sensitivity of the users in at least one user subset includes: when item information is pushed for a certain target item, determining the item price of the target item corresponding to each user subset according to the price sensitivity of users in at least one user subset; and pushing the item information according to the item price of the target item corresponding to each user subset, wherein the price sensitivity of any user subset to the corresponding item price is not lower than a first preset threshold value. Each user subset corresponds to one item price of the target item, and the average item price of the target items corresponding to all the user subsets is not lower than a second preset threshold value.
The first preset threshold and the second preset threshold can be set according to actual needs. When the price sensitivity of the user subset to the item price corresponding to the user subset is not lower than the first preset threshold, it is indicated that the price sensitivity of the user in the user subset to the item price corresponding to the user is higher, so that the item transaction probability of the user in the user subset to the item price corresponding to the user is higher, item information is pushed according to the item price corresponding to each user subset, and the purchase rate of the user in the user subset to the item with the corresponding item price can be improved. The average item price of the target items corresponding to all the user subsets is not lower than a second preset threshold, which shows that the overall transaction probability of the target items by the users in all the user subsets of the pushed item information is higher, the item information of the target items is pushed to the users, and the purchase rate of the target items by the users can be further improved.
Illustratively, i is the user subset number, and i ∈ {1, 2, 3, ·, n }; j is the item price number of the target item, and j belongs to {1, 2, 3., m }; pijThe jth item price of the target item corresponding to the user subset i; xijIndicates whether the users in the user subset i purchase the target item at the jth item price, and XijE {0, 1}, if the users in the user subset i buy the target item at the jth item price, then XijIf the users in the user subset i do not purchase the target item at the jth item price, X is 1ij＝0；CVRijTransaction probabilities for the j item prices for the users in the user subset i; n is a radical ofiPotential trading population for i users of the subset;
and (4) price sensitivity of the users in the user subset i for the price of the jth item. Based on the above conditions, determining the item price of the target item corresponding to each user subset according to the price sensitivity of the users in at least one user subset, wherein the following conditions should be satisfied:
each user subset corresponds to an item price for the target item, namely:
the price sensitivity of any subset of users for the price of its corresponding item is not below a first preset threshold γ, i.e.
The average item price of the target items corresponding to all the user subsets is not lower than a second preset threshold k, namely:
the item price of the target item corresponding to each user subset is not lower than a third preset threshold value a and not higher than a fourth preset threshold value b, namely:
the sum of the product of the transaction probability of all the user subsets for the price of the respectively corresponding item and the respectively corresponding potential number of transacting persons is the largest, namely:
in this embodiment, a user set is subjected to grouping processing according to a preset user grouping feature set to obtain at least one user subset, where the user grouping feature set includes at least one user grouping feature, and the user grouping feature includes at least one grouping index and at least one grouping division point of each grouping index; aiming at any user subset, determining the price sensitivity corresponding to the user grouping feature according to the user grouping feature corresponding to the user subset; the price sensitivity corresponding to the user grouping characteristics forms the price sensitivity of the users in the user subset; and pushing the item information according to the price sensitivity of the users in at least one user subset. In the embodiment, the price sensitivity of the users in the user subset is formed by utilizing the price sensitivity corresponding to the user grouping characteristics, so that the price sensitivity corresponding to the user subset can represent the price sensitivity of each user in the user subset, therefore, when the price sensitivity of a certain user is determined, only the user subset to which the user belongs is determined, and the price sensitivity of the user is determined simply and quickly; meanwhile, in the embodiment, according to the price sensitivity corresponding to the user subset, the item information matched with the price sensitivity of the user in the user subset is pushed to the user in the user subset, so that the effectiveness of pushing the item information is effectively improved, the utilization rate of item resources is effectively improved, the transaction rate of the user is effectively stimulated, and the maximization of the user value is facilitated.
Fig. 3 is a schematic structural diagram of an information push processing apparatus according to an embodiment of the present application, and as shown in fig. 3, the apparatus includes:
the system comprises a first processing unit 1, a second processing unit and a third processing unit, wherein the first processing unit is used for grouping a user set according to a preset user grouping feature set to obtain at least one user subset, the user grouping feature set comprises at least one user grouping feature, and the user grouping feature comprises at least one grouping index and at least one grouping division point of each grouping index;
the second processing unit 2 is used for determining the price sensitivity of the users in the user subset according to the user grouping characteristics corresponding to the user subset aiming at any user subset;
and the third processing unit 3 is used for pushing the item information according to the price sensitivity of the users in at least one user subset.
In this embodiment, a user set is subjected to grouping processing according to a preset user grouping feature set to obtain at least one user subset, where the user grouping feature set includes at least one user grouping feature, and the user grouping feature includes at least one grouping index and at least one grouping division point of each grouping index; for any user subset, determining the price sensitivity of the users in the user subset according to the user grouping characteristics corresponding to the user subset; and pushing the item information according to the price sensitivity of the users in at least one user subset. According to the user grouping feature set, the users are divided into different user subsets, the price sensitivity of the users in the user subsets is determined according to the user grouping features corresponding to the user subsets, then the item information is pushed according to the price sensitivity of the users in the user subsets, different item information pushing strategies are formulated for the different user subsets, and the item information matched with the price sensitivity of the users in the different user subsets is pushed for the users in the different user subsets.
Fig. 4 is a schematic structural diagram of another information push processing apparatus according to an embodiment of the present application, and based on the embodiment shown in fig. 3, as shown in fig. 4,
a second processing unit 2 comprising:
the first processing subunit 21 is configured to determine, for any user subset, a price sensitivity corresponding to a user grouping feature according to the user grouping feature corresponding to the user subset;
and the second processing subunit 22 is used for forming the price sensitivity corresponding to the user grouping characteristics into the price sensitivity of the sampling user.
The device also includes:
a fourth processing unit 4, configured to determine a price sensitivity corresponding to the user grouping feature;
a fourth processing unit comprising:
a third processing subunit 41, configured to perform grouping processing on the sampling user set according to a preset user grouping feature set, so as to obtain at least one sampling user subset;
the fourth processing subunit 42 is configured to, for any sampling user subset, determine transaction probability data of the sampling user according to the acquired historical behavior data of the sampling user;
and the fifth processing subunit 43 is configured to determine, according to the transaction probability data of the sampling user, a price sensitivity of the sampling user, where the price sensitivity of the sampling user constitutes a price sensitivity corresponding to the user grouping feature.
A fourth processing subunit 42, comprising:
the first processing module 421, configured to determine, according to the obtained historical behavior data of the sampling user, transaction probability data of the sampling user for each item price data in the at least one item price data;
a fifth processing subunit 43, comprising:
a second processing module 431, configured to determine a price sensitivity of the sampling user for each item price according to the transaction probability data of the sampling user for at least one item price data;
the fifth processing subunit 43 further includes:
the third processing module 432 is configured to sample the price sensitivity of the user for any item price to form a price sensitivity corresponding to the user grouping feature for the item price.
The fifth processing subunit 43 further includes:
the fourth processing module 433 is configured to determine a normalized price sensitivity of the sampling user according to the transaction probability data of the sampling user for each item price data;
the fifth processing subunit 43 further includes:
and the fifth processing module 434 is configured to sample the normalized price sensitivity of the user to form a price sensitivity corresponding to the user grouping feature.
A third processing unit 3 comprising:
a sixth processing subunit 31, configured to determine, according to the price sensitivities of the users in the at least one user subset, an item price of the target item corresponding to each user subset;
the seventh processing subunit 32 is configured to perform item information pushing according to the item price of the target item corresponding to each user subset, where a price sensitivity of any user subset to the item price corresponding thereto is not lower than a first preset threshold.
And each user subset corresponds to one item price of the target item, and the average item price of the target item corresponding to all the user subsets is not lower than a second preset threshold.
In this embodiment, a user set is subjected to grouping processing according to a preset user grouping feature set to obtain at least one user subset, where the user grouping feature set includes at least one user grouping feature, and the user grouping feature includes at least one grouping index and at least one grouping division point of each grouping index; aiming at any user subset, determining the price sensitivity corresponding to the user grouping feature according to the user grouping feature corresponding to the user subset; the price sensitivity corresponding to the user grouping characteristics forms the price sensitivity of the users in the user subset; and pushing the item information according to the price sensitivity of the users in at least one user subset. In the embodiment, the price sensitivity of the users in the user subset is formed by utilizing the price sensitivity corresponding to the user grouping characteristics, so that the price sensitivity corresponding to the user subset can represent the price sensitivity of each user in the user subset, therefore, when the price sensitivity of a certain user is determined, only the user subset to which the user belongs is determined, and the price sensitivity of the user is determined simply and quickly; meanwhile, in the embodiment, according to the price sensitivity corresponding to the user subset, the item information matched with the price sensitivity of the user in the user subset is pushed to the user in the user subset, so that the effectiveness of pushing the item information is effectively improved, the utilization rate of item resources is effectively improved, the transaction rate of the user is effectively stimulated, and the maximization of the user value is facilitated.
Fig. 5 is a schematic structural diagram of an information push processing device according to an embodiment of the present application, and as shown in fig. 5, an embodiment of the present application provides an information push processing device, which may be used to execute actions or steps of the information push processing device in the embodiments shown in fig. 1 to fig. 2, and specifically includes: a processor 501, a memory 502 and a communication interface 503.
A memory 502 for storing a computer program.
The processor 501 is configured to execute the computer program stored in the memory 502 to implement the actions of the information pushing processing device in the embodiments shown in fig. 1-2, which are not described again.
Optionally, the information push processing device may further include a bus 504. The processor 501, the memory 502 and the communication interface 503 may be connected to each other through a bus 504; the bus 504 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus 504 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.
In the embodiments of the present application, the above embodiments may be referred to and referred to by each other, and the same or similar steps and terms are not repeated.
Alternatively, part or all of the above modules may be implemented by being embedded in a chip of the information push processing device in the form of an integrated circuit. And they may be implemented separately or integrated together. That is, the above modules may be configured as one or more integrated circuits implementing the above methods, for example: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs)
A computer-readable storage medium on which a computer program is stored, the computer program being executed by a processor to implement the processing method described above.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the embodiments of the present application are all or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, information push processing device, or data center to another website, computer, information push processing device, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. Computer-readable storage media can be any available media that can be accessed by a computer or data storage device, including one or more available media integrated information push processing devices, data centers, and the like. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), among others.
Those skilled in the art will recognize that, in one or more of the examples described above, the functions described in the embodiments of the present application may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. An information push processing method, comprising:
grouping a user set according to a preset user grouping feature set to obtain at least one user subset, wherein the user grouping feature set comprises at least one user grouping feature, and the user grouping feature comprises at least one grouping index and at least one grouping division point of each grouping index;
for any user subset, determining the price sensitivity of the users in the user subset according to the user grouping characteristics corresponding to the user subset;
and pushing the item information according to the price sensitivity of the users in the at least one user subset.
2. The method according to claim 1, wherein the determining, for any one of the subsets of users, the price sensitivity of the users in the subset of users according to the user grouping feature corresponding to the subset of users comprises:
aiming at any user subset, determining the price sensitivity corresponding to the user grouping feature according to the user grouping feature corresponding to the user subset; and
and the price sensitivity corresponding to the user grouping characteristics forms the price sensitivity of the users in the user subset.
3. The method of claim 2, wherein the price sensitivity corresponding to the user grouping feature is determined by:
grouping the sampling user set according to a preset user grouping feature set to obtain at least one sampling user subset;
aiming at any sampling user subset, determining transaction probability data of the sampling users according to the acquired historical behavior data of the sampling users;
determining the price sensitivity of the sampling user according to the transaction probability data of the sampling user; and
the price sensitivity of the sampling user constitutes a price sensitivity corresponding to the user grouping feature.
4. The method of claim 3, wherein determining transaction probability data of the sampling user according to the acquired historical behavior data of the sampling user comprises:
determining transaction probability data of the sampling user aiming at each item price data in at least one item price data according to the acquired historical behavior data of the sampling user;
the determining the price sensitivity of the sampling user according to the transaction probability data of the sampling user comprises the following steps:
determining price sensitivity of the sampling user for each item price according to the transaction probability data of the sampling user for each item price data;
the sampling of the price sensitivity of the user constitutes a price sensitivity corresponding to the user grouping feature, including:
and the price sensitivity of the user aiming at the price of any item is sampled to form the price sensitivity corresponding to the user grouping characteristic aiming at the price of the item.
5. The method of claim 4, wherein determining the price sensitivity of the sampling user based on the transaction probability data of the sampling user further comprises:
determining the normalized price sensitivity of the sampling user according to the transaction probability data of the sampling user aiming at each item price data;
the sampling of the price sensitivity of the user constitutes a price sensitivity corresponding to the user grouping feature, including:
and the normalized price sensitivity of the user is sampled to form the price sensitivity corresponding to the user grouping characteristic.
6. The method according to any one of claims 1 to 5, wherein, when pushing item information for a target item, the pushing item information according to the price sensitivity of users in the at least one user subset comprises:
determining the item price of the target item corresponding to each user subset according to the price sensitivity of the users in the at least one user subset;
pushing item information according to the item price of the target item corresponding to each user subset,
wherein the price sensitivity of any subset of users for the price of the item to which it corresponds is not below a first preset threshold.
7. The method according to claim 6, wherein each user subset corresponds to an item price of the target item, and an average item price of all user subsets corresponding to the target item is not lower than a second preset threshold.
8. An information push processing device, comprising:
the system comprises a first processing unit, a second processing unit and a third processing unit, wherein the first processing unit is used for grouping a user set according to a preset user grouping feature set to obtain at least one user subset, the user grouping feature set comprises at least one user grouping feature, and the user grouping feature comprises at least one grouping index and at least one grouping division point of each grouping index;
the second processing unit is used for determining the price sensitivity of the users in the user subset according to the user grouping characteristics corresponding to the user subset aiming at any user subset;
and the third processing unit is used for pushing the item information according to the price sensitivity of the users in the at least one user subset.
9. An information push processing device, comprising:
a processor, a memory, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any of claims 1-7.
10. A computer-readable storage medium having stored thereon a computer program for execution by a processor to perform the method of any one of claims 1 to 7.
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