CN114862479A - Information pushing method and device, electronic equipment and medium - Google Patents
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Abstract
The disclosure provides an information pushing method, an information pushing device, electronic equipment and a medium, relates to the technical field of computers, and particularly relates to the technical field of information pushing, information flow, finance and cloud service. The specific implementation scheme is as follows: acquiring at least one item of candidate behavior data associated with a candidate object, and determining the number of target objects associated with the candidate behavior data; wherein the target object represents a candidate object for performing an access operation on the historical push information; and determining an object to be pushed from the candidate objects according to the number of the target objects, and pushing information to the object to be pushed. The effect of improving the information pushing precision is achieved, the labor cost required by information pushing is reduced, and the efficiency of information pushing is improved.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to the field of information push, information flow, finance, and cloud service technologies, and in particular, to an information push method, an information push apparatus, an electronic device, and a medium.
Background
With the development of internet technology, internet advertisements have gradually replaced traditional advertisements, becoming an important advertisement form, and when a user browses information on an internet platform, the internet platform can push advertisements to the user. Compared with the traditional advertisement, the internet advertisement can be pushed according to different people.
The current internet advertisement pushing generally depends on a manual mode to screen out potential users and push advertisements to the potential users.
Disclosure of Invention
The disclosure provides a method, a device, an electronic device and a medium for improving information pushing precision.
According to an aspect of the present disclosure, there is provided an information pushing method, including:
acquiring at least one item of candidate behavior data associated with a candidate object, and determining the number of target objects associated with the candidate behavior data; wherein the target object represents a candidate object for performing an access operation on the historical push information;
and determining an object to be pushed from the candidate objects according to the number of the target objects, and pushing information to the object to be pushed.
According to another aspect of the present disclosure, there is provided an information pushing apparatus including:
the behavior data acquisition module is used for acquiring at least one item of candidate behavior data associated with the candidate object and determining the target object number of the target object associated with the candidate behavior data; wherein the target object represents a candidate object for performing an access operation on the historical push information;
and the information pushing module is used for determining an object to be pushed from the candidate objects according to the number of the target objects and pushing information to the object to be pushed.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of any one of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of the present disclosure.
According to another aspect of the disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the method of any one of the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a flow chart of some information push methods disclosed according to embodiments of the present disclosure;
fig. 2 is a flow chart of another information push method disclosed according to an embodiment of the present disclosure;
FIG. 3 is a schematic structural diagram of some information pushing devices disclosed according to an embodiment of the present disclosure;
fig. 4 is a block diagram of an electronic device for implementing the information pushing method disclosed in the embodiment of the disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Currently, internet advertisement pushing generally relies on a manual method to screen out potential users, that is, relevant personnel accumulate experience based on relevant fields, and analyze business data of internet platform candidate users to screen out potential users, for example, in the financial field, relevant personnel analyze financial data of candidate users to screen out potential users, so as to push financial advertisements to potential users.
However, this method undoubtedly brings high labor cost and results in low efficiency of advertisement push. In the method, related personnel can only screen out potential users according to the analysis result of the business data, but cannot screen out potential users by analyzing the internet behavior data, because the internet behavior data has the characteristics of timeliness, large data volume, high dimensionality and the like compared with the business data. Since the advertisement pushing cannot be performed with reference to the internet behavior data, the advertisement pushing accuracy is undoubtedly low.
Fig. 1 is a flowchart of some information pushing methods disclosed in the embodiments of the present disclosure, and the embodiments may be applied to a case where an object to be pushed is determined and information is pushed to the object to be pushed. The method of the embodiment may be executed by an information pushing apparatus disclosed in the embodiment of the present disclosure, and the apparatus may be implemented by software and/or hardware, and may be integrated on any electronic device with computing capability.
As shown in fig. 1, the information pushing method disclosed in this embodiment may include:
s101, acquiring at least one item of candidate behavior data associated with the candidate object, and determining the number of target objects of the target object associated with the candidate behavior data; wherein the target object represents a candidate object for performing an access operation on the history push information.
The candidate object represents a user who performs access operation on the internet platform, and the candidate object can be a user who performs access operation on the internet platform at the current moment, or a user who performs access operation on the internet platform at a historical moment; the candidate may be a registered user registered on the internet platform, or a guest user not registered on the internet platform. The number of candidates may be one or more. Internet platforms include, but are not limited to, network databases, websites, applications or applets, and the like.
The candidate behavior data represents internet behavior data possessed by the candidate object, and includes, but is not limited to, search word information input when the internet platform performs information search, application information installed in a client held by the candidate object, and image information of the candidate object. In this embodiment, the data type of the candidate behavior data may be one type, for example, only search word information is used as the candidate behavior data, or may be multiple types, for example, at least two types of search word information, application information, and image information are used as the candidate behavior data. The data amount of the candidate behavior data is at least one item, for example, ten pieces of input search word information are used as the candidate behavior data, and for example, five pieces of installed application information are used as the candidate behavior data.
The historical push information represents information that the internet platform has pushed once at a historical moment. The access operation represents a control operation performed by the target object for browsing the history push information, and includes, but is not limited to, a click access operation, a voice access operation, a gesture access operation, and the like. For example, assuming that the candidate object a performs a click access operation on the history push information a at the history time, the candidate object a is taken as a target object.
It is understood that, before the technical solutions disclosed in the embodiments of the present disclosure are used, the types, the use ranges, the use scenes, and the like of the candidate behavior data related to the present disclosure should be informed in a proper manner according to the relevant laws and regulations, and the authorization of the candidate object should be obtained.
In one embodiment, the internet platform generates a data acquisition request, wherein the data acquisition request contains the type, the use range, the use scene and the like of candidate behavior data required by the internet platform. The internet platform sends the data acquisition request to the client side held by the candidate object, and under the condition that the candidate object authorizes the data acquisition request, the internet platform acquires at least one item of candidate behavior data related to the candidate object from the client side, such as search word information, application program information, portrait information and the like.
In another embodiment, if the candidate object has set the candidate behavior data in the client to be in the public state, the internet platform may directly obtain at least one item of candidate behavior data associated with the candidate object from the client.
After at least one item of candidate behavior data associated with the candidate object is acquired, the internet platform determines whether each candidate object performs an access operation on any history push information, and takes the candidate object performing the access operation on any history push information as a target object. And counting whether the candidate object associated with each candidate behavior data is the target object or not, and determining the number of the target objects of the target object associated with each candidate behavior data according to the counting result.
Illustratively, it is assumed that the candidates include a candidate 1, a candidate 2, a candidate 3, and a candidate 4, the candidate 1 is associated with a candidate behavior data a, a candidate behavior data B, and a candidate behavior data C, the candidate 2 is associated with a candidate behavior data a and a candidate behavior data C, the candidate 3 is associated with a candidate behavior data a, a candidate behavior data C, and a candidate behavior data D, and the candidate 4 is associated with a candidate behavior data a and a candidate behavior data D. Assuming that the candidate object 1 and the candidate object 2 are target objects, it is determined that the number of target objects of the target objects associated with the candidate behavior data a is "2", the number of target objects of the target objects associated with the candidate behavior data B is "1", the number of target objects of the target objects associated with the candidate behavior data C is "2", and the number of target objects of the target objects associated with the candidate behavior data D is "0".
By acquiring at least one item of candidate behavior data associated with the candidate object and determining the target object quantity of the target object associated with the candidate behavior data, a data base is laid for subsequently determining the object to be pushed according to the target object quantity.
S102, determining an object to be pushed from the candidate objects according to the number of the target objects, and pushing information to the object to be pushed.
Wherein the pushed information includes, but is not limited to, advertisement information, news information,
In one implementation mode, the candidate behavior data are sorted according to the target object number of the target object associated with the candidate behavior data, the candidate behavior data with the sorting order being before the preset order are used as the target behavior data, the candidate object associated with the target behavior data is used as the object to be pushed, and information pushing is performed on the object to be pushed.
Illustratively, it is assumed that the candidates include a candidate 1, a candidate 2, a candidate 3, and a candidate 4, the candidate 1 is associated with a candidate behavior data a and a candidate behavior data B, the candidate 2 is associated with a candidate behavior data B and a candidate behavior data C, the candidate 3 is associated with a candidate behavior data a, a candidate behavior data C, and a candidate behavior data D, and the candidate 4 is associated with a candidate behavior data B and a candidate behavior data D. Assuming that the candidate object 1 and the candidate object 2 are target objects, it is determined that the number of target objects of the target objects associated with the candidate behavior data a is "1", the number of target objects of the target objects associated with the candidate behavior data B is "2", the number of target objects of the target objects associated with the candidate behavior data C is "1", and the number of target objects of the target objects associated with the candidate behavior data D is "0". The result of ranking each candidate behavior data according to the number of the target objects is as follows: and assuming that the preset sequence is 3, the candidate behavior data B, the candidate behavior data A/the candidate behavior data C and the candidate behavior data D are used as target behavior data, and further the candidate object 1, the candidate object 2 and the candidate object 3 are used as objects to be pushed.
In another embodiment, a weighted calculation method, such as a TF-IDF (word frequency-inverse text frequency index) method, is adopted according to the number of target objects of the target object associated with each candidate behavior data to calculate the importance score of each candidate behavior data, and each candidate behavior data is ranked according to the importance score, and the candidate behavior data with the ranking order before the preset order is used as the target behavior data, so that the candidate object associated with the target behavior data is used as the object to be pushed, and information is pushed to the object to be pushed.
The method comprises the steps of obtaining at least one item of candidate behavior data associated with candidate objects, and determining the number of target objects of the target objects associated with the candidate behavior data; the target object represents candidate objects for performing access operation on historical pushing information, the objects to be pushed are determined from the candidate objects according to the number of the target objects, and information is pushed to the objects to be pushed, so that candidate behavior data associated with the objects to be pushed can be overlapped with the candidate behavior data of a large number of target objects, and the target objects perform access operation on the historical pushing information and are similar to the candidate behavior data of the target objects, so that the probability of performing access operation on the pushing information by the objects to be pushed is high, and the effect of improving the information pushing precision is achieved; in addition, the object to be pushed does not need to be determined manually, so that the labor cost required by information pushing is reduced, and the efficiency of information pushing is improved.
Fig. 2 is a flowchart of another information pushing method disclosed according to an embodiment of the present disclosure, which is further optimized and expanded based on the foregoing technical solution, and may be combined with each of the foregoing optional embodiments.
As shown in fig. 2, the information pushing method disclosed in this embodiment may include:
s201, acquiring at least one item of candidate behavior data associated with the candidate object, and determining the number of target objects of the target object associated with the candidate behavior data; the target object represents a candidate object for performing an access operation on the historical push information.
S202, determining target behavior data from the candidate behavior data according to the number of the target objects.
In one embodiment, the candidate behavior data are sorted according to the target object number of the target object associated with the candidate behavior data, and the candidate behavior data with the sorting order before the preset order is used as the target behavior data.
In another embodiment, a weighted calculation method, such as a TF-IDF (word frequency-inverse text frequency index) method, is used to calculate an importance score of each candidate behavior data according to the number of target objects of the target object associated with each candidate behavior data, and each candidate behavior data is ranked according to the importance score, and the candidate behavior data with the ranking order before the preset order is used as the target behavior data.
Optionally, S202 includes the following steps a and B:
A. and determining the total number of the candidate objects, the total number of the target objects and the number of the candidate objects of the candidate behavior data.
The total number of candidates represents the sum of all the candidates. The total number of target objects represents the sum of the number of all target objects in the candidate object. The number of candidates indicates the number of candidates associated with any one of the candidate behavior data.
For example, assuming that the candidates include candidate 1, candidate 2, candidate 3, and candidate 4, and candidate 1 and candidate 2 are target objects, the total number of candidates is "4" and the total number of target objects is "2".
Candidate 1 is associated with candidate behavior data a, candidate behavior data B, and candidate behavior data C, candidate 2 is associated with candidate behavior data a and candidate behavior data C, candidate 3 is associated with candidate behavior data a, candidate behavior data C, and candidate behavior data D, and candidate 4 is associated with candidate behavior data a and candidate behavior data D. The number of candidates of the candidate object associated with candidate behavior data a is "4", the number of candidates of the candidate object associated with candidate behavior data B is "1", the number of candidates of the candidate object associated with candidate behavior data C is "3", and the number of candidates of the candidate object associated with candidate behavior data D is "2".
B. And determining target behavior data from the candidate behavior data according to the number of the target objects, the total number of the candidate objects, the total number of the target objects and the number of the candidate objects.
In one embodiment, the first parameter value of each candidate behavior data is determined according to the number of target objects and the total number of target objects. And determining a second parameter value of each candidate behavior data according to the total number of the candidate objects and the number of the candidate objects. And determining target behavior data from the candidate behavior data according to the product of the first parameter value and the second parameter value.
The total number of the candidate objects, the total number of the target objects and the number of the candidate objects of the candidate behavior data are determined, and the target behavior data are determined from the candidate behavior data according to the number of the target objects, the total number of the candidate objects, the total number of the target objects and the number of the candidate objects, so that the target behavior data are determined based on multiple data dimensions, the problem that the accuracy is low when the target behavior data are determined only according to a single data dimension, for example, only according to the number of the target objects, is solved, and the effect of improving the accuracy of determining the target behavior data is achieved.
Optionally, step B includes the following steps B1, B2, and B3:
b1, determining a first parameter value of the candidate behavior data according to the ratio between the number of the target objects and the total number of the target objects, and determining a second parameter value of the candidate behavior data according to the ratio between the total number of the candidate objects and the number of the candidate objects.
In one embodiment, the Term Frequency (TF) of any candidate behavior data is determined as the first parameter value of the candidate behavior data according to the ratio between the number of target objects corresponding to the candidate behavior data and the total number of target objects. And determining an Inverse text Frequency Index (IDF) of the candidate behavior data as a second parameter value of the candidate behavior data according to a ratio between the total number of the candidate objects and the number of the candidate objects corresponding to the candidate behavior data.
Optionally, the following formula is adopted to determine the first parameter value of the candidate behavior data:
Y1＝A/B
where a represents the number of target objects associated with any one candidate behavior data, B represents the total number of target objects, and Y1 represents the first parameter value of the candidate behavior data.
Optionally, the following formula is adopted to determine the second parameter value of the candidate behavior data:
Y2＝log[C/(D+1)]
where C denotes the total number of candidate objects, D denotes the number of candidate objects associated with any one of the candidate behavior data, and Y2 denotes the second parameter value of the candidate behavior data.
For example, assuming that the number of target objects of the target object associated with the candidate behavior data a is 100, and the total number of target objects is 1000, the first parameter value of the candidate behavior data a is 100/1000 — 0.1. Assuming that the number of candidate objects associated with the candidate behavior data a is 199 and the total number of candidate objects is 2000, the second parameter value of the candidate behavior data a is log [2000/(199+1) ] -log 10.
Determining a second parameter value of each candidate behavior data by setting the following formula: and Y2 is log [ C/(D +1) ], so that the problem of low scoring reliability caused by determining the scoring of each candidate behavior data only according to the first parameter value is avoided. And because the number 1 is set in the denominator of the formula, the situation that the number of the candidate objects is abnormally counted to be zero and the calculation of the second parameter value fails is avoided.
And B2, determining the grade of the candidate behavior data according to the product of the first parameter value and the second parameter value.
In one embodiment, the product of the first parameter value and the second parameter value of any candidate behavior data is used as the score of the candidate behavior data. The score represents the importance degree of the candidate behavior data, the higher the score is, the higher the importance degree is, and the lower the score is, the lower the importance degree is.
And B3, determining target behavior data from the candidate behavior data according to the scores.
In one embodiment, the candidate behavior data are sorted according to the scores of the candidate behavior data, and the candidate behavior data with the sorting order before the preset order is used as the target behavior data. The preset order may be a specific order, such as the fifth bit, or a percentage order, such as 20%.
Optionally, if the data types of the candidate behavior data are multiple, the target behavior data is determined from the various candidate behavior data according to the scores of the various candidate behavior data. For example, assuming that the candidate behavior data includes three types of search term information, application information, and portrait information, and the preset order is set to 20%, the search term information with a score of the top 20% is taken as the target behavior data, the application information with a score of the top 20% is taken as the target behavior data, and the portrait information with a score of the top 20% is taken as the target behavior data.
The method comprises the steps of determining a first parameter value of each candidate behavior data according to the ratio between the number of the target objects and the total number of the target objects, determining a second parameter value of each candidate behavior data according to the ratio between the total number of the candidate objects and the number of the candidate objects, determining the score of each candidate behavior data according to the product of the first parameter value and the second parameter value, and further determining the target behavior data from each candidate behavior data according to the score.
S203, determining an object to be pushed from the candidate objects according to the target behavior data and the incidence relation between the candidate behavior data and the candidate objects.
In one embodiment, candidate behavior data matched with the target behavior data is used as auxiliary candidate behavior data, and a candidate object associated with the auxiliary candidate behavior data is used as an object to be pushed.
For example, assuming that the target behavior data includes "candidate data a" and "candidate data B", assuming that the candidate associated with "candidate data a" includes "candidate 1" and "candidate 2", and the candidate associated with "candidate data B" includes "candidate 2" and "candidate 3", then "candidate 1", "candidate 2", and "candidate 3" are taken as the objects to be pushed.
The target behavior data are determined from the candidate behavior data according to the number of the target objects, the object to be pushed is determined from the candidate objects according to the target behavior data and the incidence relation between the candidate behavior data and the candidate objects, so that the candidate behavior data related to the object to be pushed can be overlapped with the candidate behavior data of a large number of target objects, and the probability of the object to be pushed performing the access operation on the pushing information is high because the target objects perform the access operation on the historical pushing information and the candidate behavior data of the object to be pushed are similar to the candidate behavior data of the target objects.
S204, determining the object type of the object to be recommended according to the online time of the object to be pushed; wherein the object types include active objects and inactive objects.
The online duration represents the duration of the object to be pushed accessing the internet platform.
In one embodiment, the online time of an object to be pushed in a historical time period, for example, the online time of approximately ten days, is determined, the online time is compared with a preset time threshold, if the online time is greater than or equal to the time threshold, the object to be recommended is determined to be an active object, and if the online time is less than the time threshold, the object to be recommended is determined to be an inactive object. The time length threshold value can be set and adjusted according to actual service requirements.
S205, determining a pushing time period of the object to be recommended according to the object type, and pushing information to the object to be recommended within the pushing time period.
In one embodiment, the active time period of the object to be recommended is determined according to the type of the object, and the active time period is used as a pushing time period, so that the internet platform pushes information to the object to be pushed in the pushing time period.
Determining the object type of the object to be recommended according to the online time length of the object to be pushed; the object types comprise active objects and inactive objects, a pushing time period of the object to be recommended is determined according to the object types, and information is pushed to the object to be pushed in the pushing time period, so that the time period to be pushed can be adapted to the object to be pushed of the corresponding object type, and the probability of the object to be pushed browsing and pushing information is improved.
Optionally, S205 includes the following two cases C and D:
C. and under the condition that the object type is an active object, determining a pushing time period from the candidate time periods according to the average online time length of the object to be pushed in at least two candidate time periods.
The candidate time period can be set and adjusted according to actual service requirements. Preferably 24 hours out of 1 day are taken as 24 candidate time periods.
In one embodiment, in the case that the object type is an active object, determining an average online time length of the object to be recommended in at least two candidate time periods of the historical time, and taking a preset number of candidate time periods with the longest average online time length as the pushing time period.
Illustratively, the average online time of 24 candidate time periods per day in the last 10 days of the object to be recommended is determined, and the 3 candidate time periods with the longest average online time are taken as the pushing time periods. For example, 9: the average online time lengths of 00-10:00, 14:00-15:00, and 19:00-20:00 are longest, then 9: 00-10:00, 14:00-15:00, and 19:00-20:00 as push time periods.
D. And under the condition that the object type is an inactive object, determining a pushing time period from the candidate time periods according to the average online time length of the candidate object in at least two candidate time periods.
In one embodiment, in the case that the object type is an inactive object, an average online time length of all candidate objects in at least two candidate time periods of the historical time is determined, and a preset number of candidate time periods with the longest average online time length are used as the push time periods.
Illustratively, the average online time length of 24 candidate time periods per day in the last 10 days of all the candidate objects is determined, and the longest 3 candidate time periods with the average online time length are taken as the pushing time period.
The method comprises the steps that when the object type is an active object, the pushing time period is determined from the candidate time periods according to the average online time length of the object to be pushed in the at least two candidate time periods, and when the object type is an inactive object, the pushing time period is determined from the candidate time periods according to the average online time length of the candidate object in the at least two candidate time periods, so that when the object to be pushed is the active object, the recommended information can be browsed in the specific active time period of the object to be pushed, and when the object to be pushed is the inactive object, the recommended information can be browsed in the general active time period of the public, and therefore the probability that the object to be pushed browses the pushed information is improved.
Fig. 3 is a schematic structural diagram of some information pushing apparatuses disclosed according to the embodiment of the present disclosure, which may be applied to a case where an object to be pushed is determined and information is pushed to the object to be pushed. The device of the embodiment can be implemented by software and/or hardware, and can be integrated on any electronic equipment with computing capability.
As shown in fig. 3, the information pushing apparatus 30 disclosed in this embodiment may include a behavior data acquiring module 31 and an information pushing module 32, where:
a behavior data obtaining module 31, configured to obtain at least one item of candidate behavior data associated with the candidate object, and determine the number of target objects of the target object associated with the candidate behavior data; the target object represents a candidate object for performing an access operation on the historical push information;
and the information pushing module 32 is configured to determine an object to be pushed from the candidate objects according to the number of the target objects, and push information to the object to be pushed.
Optionally, the information pushing module 32 is specifically configured to:
determining target behavior data from the candidate behavior data according to the number of the target objects;
and determining an object to be pushed from the candidate objects according to the target behavior data and the incidence relation between the candidate behavior data and the candidate objects.
Optionally, the information pushing module 32 is further specifically configured to:
determining the total number of the candidate objects, the total number of the target objects and the number of the candidate objects of the candidate behavior data;
and determining target behavior data from the candidate behavior data according to the number of the target objects, the total number of the candidate objects, the total number of the target objects and the number of the candidate objects.
Optionally, the information pushing module 32 is further specifically configured to:
determining a first parameter value of the candidate behavior data according to the ratio between the number of the target objects and the total number of the target objects, and determining a second parameter value of the candidate behavior data according to the ratio between the total number of the candidate objects and the number of the candidate objects;
determining a score of the candidate behavior data according to a product between the first parameter value and the second parameter value;
and determining target behavior data from the candidate behavior data according to the scores.
Optionally, the information pushing module 32 is further specifically configured to:
determining a second parameter value of the candidate behavior data using the following formula:
Y2＝log[C/(D+1)]
where C denotes the total number of candidate objects, D denotes the number of candidate objects associated with any one of the candidate behavior data, and Y2 denotes the second parameter value of the candidate behavior data. A
Optionally, the information pushing module 32 is further specifically configured to:
determining the object type of the object to be recommended according to the online time length of the object to be pushed; wherein the object types include active objects and inactive objects;
and determining a pushing time period of the object to be recommended according to the object type, and pushing information to the object to be pushed in the pushing time period.
Optionally, the information pushing module 32 is further specifically configured to:
determining a pushing time period from the candidate time periods according to the average online time lengths of the object to be pushed in the at least two candidate time periods under the condition that the object type is the active object;
and under the condition that the object type is an inactive object, determining a pushing time period from the candidate time periods according to the average online time length of the candidate object in at least two candidate time periods.
The information pushing apparatus 30 disclosed in the embodiment of the present disclosure can execute the information pushing method disclosed in the embodiment of the present disclosure, and has functional modules and beneficial effects corresponding to the execution method. Reference may be made to the description in the method embodiments of the present disclosure for details that are not explicitly described in this embodiment.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
As shown in fig. 4, the apparatus 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
A number of components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (17)
1. An information push method, comprising:
acquiring at least one item of candidate behavior data associated with a candidate object, and determining the number of target objects associated with the candidate behavior data; wherein the target object represents a candidate object for performing an access operation on the historical push information;
and determining an object to be pushed from the candidate objects according to the number of the target objects, and pushing information to the object to be pushed.
2. The method of claim 1, wherein the determining the object to be pushed from the candidate objects according to the target object number comprises:
determining target behavior data from the candidate behavior data according to the number of the target objects;
and determining an object to be pushed from the candidate objects according to the target behavior data and the incidence relation between the candidate behavior data and the candidate objects.
3. The method of claim 2, wherein the determining target behavior data from the candidate behavior data as a function of the number of target objects comprises:
determining the total number of the candidate objects, the total number of the target objects and the number of the candidate objects related to the candidate behavior data;
and determining target behavior data from the candidate behavior data according to the number of the target objects, the total number of the candidate objects, the total number of the target objects and the number of the candidate objects.
4. The method of claim 3, wherein the determining target behavior data from the candidate behavior data as a function of the number of target objects, the total number of candidate objects, the total number of target objects, and the number of candidate objects comprises:
determining a first parameter value of the candidate behavior data according to the ratio between the number of the target objects and the total number of the target objects, and determining a second parameter value of the candidate behavior data according to the ratio between the total number of the candidate objects and the number of the candidate objects;
determining a score for the candidate behavior data based on a product between the first parameter value and the second parameter value;
and determining target behavior data from the candidate behavior data according to the scores.
5. The method of claim 4, wherein the determining a second parameter value of the candidate behavior data as a function of a ratio between the total number of candidate objects and the number of candidate objects comprises:
determining a second parameter value of the candidate behavior data using the following formula:
Y2＝log[C/(D+1)]
where C denotes the total number of candidate objects, D denotes the number of candidate objects associated with any one of the candidate behavior data, and Y2 denotes the second parameter value of the candidate behavior data.
6. The method of claim 1, wherein the pushing information to the object to be pushed comprises:
determining the object type of the object to be recommended according to the online time length of the object to be pushed; wherein the object types include active objects and inactive objects;
and determining a pushing time period of the object to be recommended according to the object type, and pushing information to the object to be recommended within the pushing time period.
7. The method of claim 6, wherein the determining the pushing time period of the object to be recommended according to the object type comprises:
determining a pushing time period from at least two candidate time periods according to the average online time length of the object to be pushed in the candidate time periods under the condition that the object type is an active object;
and under the condition that the object type is an inactive object, determining a pushing time period from the candidate time periods according to the average online time length of the candidate object in at least two candidate time periods.
8. An information pushing apparatus comprising:
the behavior data acquisition module is used for acquiring at least one item of candidate behavior data associated with the candidate object and determining the target object number of the target object associated with the candidate behavior data; wherein the target object represents a candidate object for performing an access operation on the historical push information;
and the information pushing module is used for determining an object to be pushed from the candidate objects according to the number of the target objects and pushing information to the object to be pushed.
9. The apparatus according to claim 8, wherein the information pushing module is specifically configured to:
determining target behavior data from the candidate behavior data according to the number of the target objects;
and determining an object to be pushed from the candidate objects according to the target behavior data and the incidence relation between the candidate behavior data and the candidate objects.
10. The apparatus according to claim 9, wherein the information pushing module is further specifically configured to:
determining the total number of the candidate objects, the total number of the target objects and the number of the candidate objects related to the candidate behavior data;
and determining target behavior data from the candidate behavior data according to the number of the target objects, the total number of the candidate objects, the total number of the target objects and the number of the candidate objects.
11. The apparatus according to claim 10, wherein the information pushing module is further specifically configured to:
determining a first parameter value of the candidate behavior data according to the ratio between the number of the target objects and the total number of the target objects, and determining a second parameter value of the candidate behavior data according to the ratio between the total number of the candidate objects and the number of the candidate objects;
determining a score for the candidate behavior data based on a product between the first parameter value and the second parameter value;
and determining target behavior data from the candidate behavior data according to the scores.
12. The apparatus according to claim 11, wherein the information pushing module is further specifically configured to:
determining a second parameter value of the candidate behavior data using the following formula:
Y2＝log[C/(D+1)]
where C denotes the total number of candidate objects, D denotes the number of candidate objects associated with any one of the candidate behavior data, and Y2 denotes the second parameter value of the candidate behavior data.
13. The apparatus according to claim 8, wherein the information pushing module is further specifically configured to:
determining the object type of the object to be recommended according to the online time length of the object to be pushed; wherein the object types include active objects and inactive objects;
and determining a pushing time period of the object to be recommended according to the object type, and pushing information to the object to be recommended within the pushing time period.
14. The apparatus according to claim 13, wherein the information pushing module is further specifically configured to:
determining a pushing time period from at least two candidate time periods according to the average online time length of the object to be pushed in the candidate time periods under the condition that the object type is an active object;
and under the condition that the object type is an inactive object, determining a pushing time period from the candidate time periods according to the average online time length of the candidate object in at least two candidate time periods.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method according to any one of claims 1-7.
17. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 7.
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| CN116957649A (en) * | 2023-07-26 | 2023-10-27 | 广东企企通科技有限公司 | Customer screening method, device, equipment and medium |
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