CN116069604A - User behavior prediction method, device, equipment and storage medium - Google Patents

User behavior prediction method, device, equipment and storage medium Download PDF

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CN116069604A
CN116069604A CN202211649979.6A CN202211649979A CN116069604A CN 116069604 A CN116069604 A CN 116069604A CN 202211649979 A CN202211649979 A CN 202211649979A CN 116069604 A CN116069604 A CN 116069604A
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user
control
clicked
identification information
menu
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赵盼
鲜雨宏
周贵龙
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Shanghai Pudong Development Bank Co Ltd
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Abstract

The invention discloses a user behavior prediction method, a device, equipment and a storage medium. The method comprises the following steps: acquiring identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical click frequency corresponding to each control clicked by the user; predicting the user behavior according to the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical clicking frequency corresponding to each control clicked by the user, and obtaining the target control. According to the technical scheme, the operation guide under different scenes can be intelligently recommended to the user, the learning cost is reduced, the interaction with the user is more convenient and quicker, and the user experience is improved.

Description

User behavior prediction method, device, equipment and storage medium
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a user behavior prediction method, a device, equipment and a storage medium.
Background
The DevOps system is a full-flow research and development system integrating requirements carding, research and development, operation and maintenance, testing and the like, and is huge, complex in operation and quick in version updating, and needs a certain learning cost. In the using process, the user can only be familiar with specific operations by searching handbooks, clicking for many times, asking for customer service and the like, and the user experience is poor. Some current systems provide operation guidance for users when the users are in new functions, but the systems are directly oriented to all clients, the individuation degree of recommended information is low, and the time and the energy of the users are easily wasted.
Disclosure of Invention
The embodiment of the invention provides a user behavior prediction method, device, equipment and storage medium, which solve the problems of high learning cost caused by complex system operation and poor user experience caused by low individuation degree of system recommendation information and waste of time and energy of a user.
According to an aspect of the present invention, there is provided a user behavior prediction method, including:
acquiring identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical click frequency corresponding to each control clicked by the user;
predicting the user behavior according to the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical clicking frequency corresponding to each control clicked by the user, and obtaining the target control.
According to another aspect of the present invention, there is provided a user behavior prediction apparatus including:
the first acquisition module is used for acquiring the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical click frequency corresponding to each control clicked by the user;
The first obtaining module is used for predicting the user behavior according to the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical click frequency corresponding to each control clicked by the user, so as to obtain the target control.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the user behavior prediction method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a user behavior prediction method according to any one of the embodiments of the present invention.
According to the embodiment of the invention, the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical click frequency corresponding to each control clicked by the user are obtained; the method comprises the steps of predicting user behaviors according to identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical clicking frequency corresponding to each control clicked by the user, so that a target control is obtained, the problems that learning cost is high due to complex system operation and user experience is poor and energy is wasted due to low individuation degree of system recommendation information are solved, operation guidelines in different scenes can be intelligently recommended for the user, learning cost is reduced, interaction with the user is more convenient and quick, and user experience is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for predicting user behavior in accordance with a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a device for predicting user behavior according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It will be appreciated that prior to using the technical solutions disclosed in the embodiments of the present disclosure, the user should be informed and authorized of the type, usage range, usage scenario, etc. of the personal information related to the present disclosure in an appropriate manner according to the relevant legal regulations.
Example 1
Fig. 1 is a flowchart of a user behavior prediction method in a first embodiment of the present invention, where the embodiment is applicable to predicting user behavior and performing accurate guidance on a user, the method may be performed by a user behavior prediction device in the embodiment of the present invention, and the device may be implemented in software and/or hardware, as shown in fig. 1, and the method specifically includes the following steps:
S110, acquiring identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical click frequency corresponding to each control clicked by the user.
The identification information of the user may be a login name and a login password of the user, or may be ID information of the user. And determining that the user clicks the control according to the identification information of each control clicked by the user. The page browsing time corresponding to each control clicked by the user is the residence time of the corresponding displayed page after the user clicks the control. The historical click frequency corresponding to each control clicked by the user is the historical click frequency of each control clicked by the user.
Specifically, the method for obtaining the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user, and the historical click frequency corresponding to each control clicked by the user may be: the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical click frequency corresponding to each control clicked by the user in a specified period can be collected through a DevOps system. It should be noted that, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user, and the historical click frequency corresponding to each control clicked by the user may be updated in real time according to a specified period in statistics.
S120, predicting the user behavior according to the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical click frequency corresponding to each control clicked by the user, and obtaining the target control.
The target control may be a control to be clicked next by the user obtained by predicting the user behavior according to the trained LSTM model.
Specifically, the method for obtaining the target control by predicting the user behavior according to the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical click frequency corresponding to each control clicked by the user may be as follows: inputting the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical click frequency corresponding to each control clicked by the user into a trained LSTM model, and predicting the behavior of the user to obtain a target control.
Optionally, predicting the user behavior according to the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user, and the historical click frequency corresponding to each control clicked by the user, to obtain the target control, including:
Inputting identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical click frequency corresponding to each control clicked by the user into a target model to obtain a predicted control, wherein the target model is obtained by iteratively training an LSTM model through a target sample set, and the target sample set comprises: the method comprises the steps of identifying information of a sample user, identifying information of each control clicked by the sample user, page browsing time corresponding to each control clicked by the sample user, historical click frequency corresponding to each control clicked by the sample user, and a control corresponding to the next operation of the sample user.
Among them, LSTM (Long Short Term Memory, long-short-term memory network) is a time recurrent neural network that can be used to predict the user's intention to go next. The prediction control is a control which predicts that the user needs to click next.
Specifically, the method for obtaining the predicted control by inputting the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical click frequency corresponding to each control clicked by the user into the target model may be as follows: the identification information of the sample user, the identification information of each control clicked by the sample user, the page browsing time corresponding to each control clicked by the sample user, the historical click frequency corresponding to each control clicked by the sample user and the control corresponding to the next operation of the sample user are taken as parameters, the LSTM model is adopted for training to obtain a target model, the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical click frequency corresponding to each control clicked by the user are taken as input parameters, and the target model is input to output a prediction control.
It should be noted that, the traditional front-end embedded point manner can be used for data collection, a user is marked with a label, and content of interest is recommended to the user according to the label, but the technology can derive a plurality of redundant codes along with the continuous progress of the data collection process, and the redundant codes are scattered at all positions of the source codes to occupy resources, so that the original business logic is disturbed, the collected data is inaccurate, and the accuracy of the subsequent user behavior collection is reduced. And when the control is predicted, monitoring the user behavior to acquire the single target content of the user. The user may have multiple roles in the system, and the user is operated according to multiple planning contents in the system, so that the user cannot be guided accurately only by classifying or labeling the user according to the roles. The LSTM model is iteratively trained by utilizing the target sample set, the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical click frequency corresponding to each control clicked by the user are used as input parameters, and the LSTM model which is iteratively trained by adopting the target sample set can accurately predict the next intention of the user in real time to obtain a predicted control.
Optionally, iteratively training the LSTM model by the set of target samples includes:
inputting the identification information of the sample user in the target sample set, the identification information of each control clicked by the sample user, the page browsing time corresponding to each control clicked by the sample user and the historical click frequency corresponding to each control clicked by the sample user into an LSTM model to obtain a prediction control;
training parameters of the LSTM model according to an objective function formed by a prediction control and a control corresponding to the next operation of a sample user;
and the return execution inputs the identification information of the sample user in the target sample set, the identification information of each control clicked by the sample user, the page browsing time corresponding to each control clicked by the sample user and the historical click frequency corresponding to each control clicked by the sample user into the LSTM model to obtain the operation of the prediction control until the target model is obtained.
The objective function is a function formed by the prediction control and a control corresponding to the next operation of the sample user, and is mainly used for training parameters of the LSTM model. Parameters of the LSTM model may include: the method comprises the steps of identifying information of a user, identifying information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical click frequency corresponding to each control clicked by the user.
Optionally, after predicting the user behavior according to the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user, and the historical click frequency corresponding to each control clicked by the user, obtaining the predicted control, the method further includes:
generating a first menu stream according to each control clicked by the user and the prediction control;
obtaining the similarity of each menu stream in the first menu stream and the menu stream set;
determining a menu stream with the similarity with the first menu stream being larger than a similarity threshold value in the menu stream set as a target menu stream;
and recommending the control according to the target menu flow.
The first menu stream is generated according to each control clicked by the user and a predicted control obtained according to the parameters of the input target model.
The menu flow set may be a set of historical menu flows pre-established according to different roles or custom disagreement graphs of users for the next operation. For example, the menu stream may be: if a user 'newly-built work item' in the DevOps system is used as a first step of operation and is used as a developer, the newly-built work item is used for managing development data, a [ newly-built work item, a selection start time, a selection end time, a related father work item and a generation child work item … ] menu flow are formed; as a tester, a new work item is formed for counting defect details, a [ new work item, a defect responsible person, a defect state, a defect grade and a deadline … ] menu stream is formed; as a pipeline manager, a new work item creates a [ new work item, associated with an existing branch/new branch, run pipeline … ] menu flow in order to create a branch management pipeline. The menu flow set may be a menu flow set formed by at least one menu flow formed according to different intentions of users of different roles or habits for a next operation.
The similarity is the characteristic association degree of each menu stream in the first menu stream and the menu stream set. The similarity threshold value can be set according to the actual situation. The target menu stream is a menu stream predicted from the menu stream set and the first menu stream for the next operation of the user.
Specifically, the manner of generating the first menu stream according to each control clicked by the user and the prediction control may be: and after the prediction control is obtained, forming a first menu stream according to each control clicked by the user and the prediction control according to the clicking sequence.
Specifically, the method for obtaining the similarity of each menu stream in the first menu stream and the menu stream set may be: and associating the first menu stream with each menu stream in the menu stream set, and determining the similarity of the first menu stream and each menu stream in the menu stream set by adopting a clustering method.
Specifically, the method for determining, as the target menu stream, the menu stream in the menu stream set having the similarity with the first menu stream greater than the similarity threshold may be: and setting a similarity threshold, comparing the similarity of each menu stream in the menu stream set and the first menu stream with the similarity threshold, and determining a menu stream with similarity larger than the similarity threshold as a target menu stream.
Specifically, the manner of performing control recommendation according to the target menu flow may be: and after the target menu stream is determined, performing operation recommendation on the user according to the control sequence in the target menu stream. For example, if the first menu flow generated according to the control "new work item" clicked by the user and the predictive control "select defect responsible person" is [ new work item, select defect responsible person ], the target menu flow with the similarity of each menu flow in the first menu flow and the menu flow set greater than the similarity threshold is [ new work item, select defect responsible person, select defect state, select defect level, select expiration date … ], the next clicked control can be recommended to the user as "select defect state" according to the control sequence in the target menu flow.
Optionally, the method further comprises:
and if the similarity of the first menu flow and each menu flow in the menu flow set is smaller than a similarity threshold value, performing control recommendation according to the target control.
Specifically, if the similarity of the first menu flow and each menu flow in the menu flow set is smaller than a similarity threshold, the control recommendation method according to the target control may be: if the similarity of each menu flow in the first menu flow and the menu flow set is smaller than the similarity threshold value, the target control predicted according to the target model is directly recommended to the user.
Optionally, after determining, as the target menu stream, a menu stream in the menu stream set having a similarity to the first menu stream greater than the similarity threshold, further comprising:
acquiring identification information of a first control clicked by a user;
if the target menu stream does not have the control which is the same as the identification information of the first control, generating a second menu stream according to the first menu stream and the first control;
determining a menu stream with the similarity with the second menu stream being larger than a similarity threshold value in the menu stream set as a third menu stream;
and recommending the control according to the third menu flow.
The first control may be a next control that the user may click on after clicking on the predictive control. The second menu stream is a menu stream regenerated from the first menu stream and the first control. The third menu stream is a menu stream predicted by the user to be intended next by the user after clicking the first control.
Specifically, the method for obtaining the identification information of the first control clicked by the user may be: identification information of the first control clicked by the user can be obtained through a DevOps system.
Specifically, if the target menu flow does not have a control with the same identification information as the first control, the method for generating the second menu flow according to the first menu flow and the first control may be: and acquiring a target menu stream, if the target menu stream does not have a control which is the same as the identification information of the first control, and indicating that the target menu stream is not consistent with the intention of the user in the next step, regenerating the menu stream according to the first menu stream and the first control, and determining the regenerated menu stream as a second menu stream.
Specifically, the manner of determining, as the third menu flow, the menu flow in the menu flow set having the similarity with the second menu flow greater than the similarity threshold may be: and determining the similarity of the second menu stream and each menu stream in the menu stream set by adopting a clustering method, and determining the menu stream with the similarity larger than a similarity threshold value as a third menu stream.
Specifically, the manner of performing control recommendation according to the third menu flow may be: and recommending the user in the next step according to the control in the third menu stream. For example, if the first menu flow is [ new work item, selection defect responsible person ], the obtained target menu flow is [ new work item, selection defect responsible person, selection defect state, selection defect level, selection expiration date … ], the next control clicked for the user should be "selection defect state", but the user does not click "selection defect state", the first control clicked by the user is "associated with existing branch/new branch", "associated with existing branch/new branch" is not in the target menu flow, then the second menu flow is generated according to the first menu flow and the first control [ new work item, selection defect responsible person, associated with existing branch/new branch ], the similarity of each menu flow in the second menu flow and the menu flow set is compared according to the clustering method, the menu flow with the similarity greater than the similarity threshold is determined to be the third menu flow, the third menu flow may be [ work item, selection defect responsible person, associated with existing branch/new branch, operation … ], and the next control in the third menu flow is clicked on the new control according to the third menu flow.
Optionally, the method further comprises:
if the similarity of each menu stream in the first menu stream and the menu stream set is smaller than a similarity threshold value, and the page browsing time corresponding to the clicked control of the user is larger than a time threshold value, displaying a prompt interface;
responding to the detection of the editing operation of a user for the prompt interface, and acquiring target information input by the user;
determining a second control according to the target information and the menu flow set;
and recommending the control according to the second control.
The time threshold can be set according to actual needs. The prompt interface may be an intelligent question interface, for example, may be a question: the user, your good-! Please ask what questions you have?
The target information may be key information input by the user and capable of performing the next operation. The second control is a control which is obtained after analyzing the target information input by the user and is clicked next by the user, and can also be a control which is obtained after analyzing the target information input by the user and is clicked next by the user according to the menu flow set.
Specifically, if the similarity of the first menu flow and each menu flow in the menu flow set is smaller than a similarity threshold, and the page browsing time corresponding to the clicked control of the user is greater than a time threshold, the manner of displaying the prompt interface may be: if the similarity of each menu flow in the first menu flow and the menu flow set is smaller than a similarity threshold value and the page browsing time corresponding to the clicked control of the user is larger than a time threshold value, a prompt interface is displayed by a popup window, and an intelligent question-answering robot built in the system is introduced to question the current situation of the user.
Specifically, in response to detecting an editing operation of the user on the prompt interface, a manner of acquiring target information input by the user may be: the user inputs key information about the next intention to the editing position of the prompt interface according to the question in the prompt interface.
Specifically, the manner of determining the second control according to the target information and the menu flow set may be: and identifying according to the target information to obtain keywords corresponding to the target information, matching according to the control clicked by the user, the keywords corresponding to the target information and each menu flow in the menu flow set, determining the corresponding menu flow according to the matching result, and further determining the second control. The method for determining the second control according to the target information and the menu flow set may further be: and directly acquiring a second control corresponding to the target information by identifying the target information.
Specifically, the manner of performing control recommendation according to the second control may be: and recommending the user in the next step according to the second control.
According to the technical scheme, identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical click frequency corresponding to each control clicked by the user are obtained; the method comprises the steps of predicting user behaviors according to identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical clicking frequency corresponding to each control clicked by the user, so that a target control is obtained, the problems that learning cost is high due to complex system operation and user experience is poor and energy is wasted due to low individuation degree of system recommendation information are solved, operation guidelines in different scenes can be intelligently recommended for the user, learning cost is reduced, interaction with the user is more convenient and quick, and user experience is improved.
Example two
Fig. 2 is a schematic structural diagram of a user behavior prediction apparatus according to a second embodiment of the present invention. The embodiment may be suitable for predicting user behavior and performing accurate guidance on a user, where the apparatus may be implemented in software and/or hardware, and the apparatus may be integrated in any device that provides a function of user behavior prediction, as shown in fig. 2, where the user behavior prediction apparatus specifically includes: a first acquisition module 210 and a first acquisition module 220.
The first obtaining module 210 is configured to obtain identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user, and historical click frequency corresponding to each control clicked by the user;
the first obtaining module 220 is configured to predict a user behavior according to the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user, and the historical click frequency corresponding to each control clicked by the user, so as to obtain a target control.
Optionally, the method further comprises:
the first generation module is used for generating a first menu stream according to each control clicked by the user and the prediction control;
A second obtaining module, configured to obtain a similarity of each menu flow in the first menu flow and the menu flow set;
the first determining module is used for determining a menu stream, of which the similarity with the first menu stream is larger than a similarity threshold value, in the menu stream set as a target menu stream;
and the first recommendation module is used for recommending the control according to the target menu flow.
Optionally, the method further comprises:
and the second recommendation module is used for recommending the control according to the target control if the similarity of each menu flow in the first menu flow and the menu flow set is smaller than a similarity threshold value.
Optionally, the method further comprises:
the third acquisition module is used for acquiring the identification information of the first control clicked by the user;
the second generation module is used for generating a second menu stream according to the first menu stream and the first control if the control which is the same as the identification information of the first control does not exist in the target menu stream;
a second determining module, configured to determine, as a third menu flow, a menu flow in the menu flow set having a similarity with the second menu flow greater than a similarity threshold;
and the third recommending module is used for recommending the control according to the third menu flow.
Optionally, the method further comprises:
the first display module is used for displaying a prompt interface if the similarity of each menu flow in the first menu flow and the menu flow set is smaller than a similarity threshold and the page browsing time corresponding to the clicked control of the user is larger than a time threshold;
the fourth acquisition module is used for responding to the detection of the editing operation of the user on the prompt interface and acquiring target information input by the user;
a third determining module, configured to determine a second control according to the target information and the menu flow set;
and the fourth recommendation module is used for recommending the control according to the second control.
Optionally, the first obtaining module is specifically configured to:
inputting identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical click frequency corresponding to each control clicked by the user into a target model to obtain a predicted control, wherein the target model is obtained by iteratively training an LSTM model through a target sample set, and the target sample set comprises: the method comprises the steps of identifying information of a sample user, identifying information of each control clicked by the sample user, page browsing time corresponding to each control clicked by the sample user, historical click frequency corresponding to each control clicked by the sample user, and a control corresponding to the next operation of the sample user.
Optionally, the first obtaining module is specifically configured to:
inputting the identification information of the sample user in the target sample set, the identification information of each control clicked by the sample user, the page browsing time corresponding to each control clicked by the sample user and the historical click frequency corresponding to each control clicked by the sample user into an LSTM model to obtain a prediction control;
training parameters of the LSTM model according to an objective function formed by a prediction control and a control corresponding to the next operation of a sample user;
and the return execution inputs the identification information of the sample user in the target sample set, the identification information of each control clicked by the sample user, the page browsing time corresponding to each control clicked by the sample user and the historical click frequency corresponding to each control clicked by the sample user into the LSTM model to obtain the operation of the prediction control until the target model is obtained.
The product can execute the method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
According to the technical scheme, identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical click frequency corresponding to each control clicked by the user are obtained; the method comprises the steps of predicting user behaviors according to identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical clicking frequency corresponding to each control clicked by the user, so that a target control is obtained, the problems that learning cost is high due to complex system operation and user experience is poor and energy is wasted due to low individuation degree of system recommendation information are solved, operation guidelines in different scenes can be intelligently recommended for the user, learning cost is reduced, interaction with the user is more convenient and quick, and user experience is improved.
Example III
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 3, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM12 and the RAM13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the user behavior prediction method.
In some embodiments, the user behavior prediction method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM12 and/or the communication unit 19. When the computer program is loaded into RAM13 and executed by processor 11, one or more steps of the user behavior prediction method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the user behavior prediction method in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program 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 the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage 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. Alternatively, the computer readable storage medium may be a machine readable signal medium. 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 an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; as well as a keyboard and pointing device (e.g., a mouse or trackball), the user may provide input to the electronic device through the keyboard and the pointing device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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 computing system may include a client and a server. The client and server are typically 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 hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims (10)

1. A method for predicting user behavior, comprising:
acquiring identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical click frequency corresponding to each control clicked by the user;
predicting the user behavior according to the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical clicking frequency corresponding to each control clicked by the user, and obtaining the target control.
2. The method of claim 1, wherein predicting the user behavior based on the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user, and the historical click frequency corresponding to each control clicked by the user, after obtaining the predicted control, further comprises:
generating a first menu stream according to each control clicked by the user and the prediction control;
obtaining the similarity of each menu stream in the first menu stream and the menu stream set;
determining a menu stream with the similarity with the first menu stream being larger than a similarity threshold value in the menu stream set as a target menu stream;
and recommending the control according to the target menu flow.
3. The method as recited in claim 2, further comprising:
and if the similarity of the first menu flow and each menu flow in the menu flow set is smaller than a similarity threshold value, performing control recommendation according to the target control.
4. The method of claim 2, further comprising, after determining a menu stream in the set of menu streams having a similarity to the first menu stream greater than a similarity threshold as the target menu stream:
Acquiring identification information of a first control clicked by a user;
if the target menu stream does not have the control which is the same as the identification information of the first control, generating a second menu stream according to the first menu stream and the first control;
determining a menu stream with the similarity with the second menu stream being larger than a similarity threshold value in the menu stream set as a third menu stream;
and recommending the control according to the third menu flow.
5. The method as recited in claim 2, further comprising:
if the similarity of each menu stream in the first menu stream and the menu stream set is smaller than a similarity threshold value, and the page browsing time corresponding to the clicked control of the user is larger than a time threshold value, displaying a prompt interface;
responding to the detection of the editing operation of a user for the prompt interface, and acquiring target information input by the user;
determining a second control according to the target information and the menu flow set;
and recommending the control according to the second control.
6. The method of claim 1, wherein predicting the user behavior based on the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user, and the historical click frequency corresponding to each control clicked by the user, to obtain the target control comprises:
Inputting identification information of a user, identification information of each control clicked by the user, page browsing time corresponding to each control clicked by the user and historical click frequency corresponding to each control clicked by the user into a target model to obtain a predicted control, wherein the target model is obtained by iteratively training an LSTM model through a target sample set, and the target sample set comprises: the method comprises the steps of identifying information of a sample user, identifying information of each control clicked by the sample user, page browsing time corresponding to each control clicked by the sample user, historical click frequency corresponding to each control clicked by the sample user, and a control corresponding to the next operation of the sample user.
7. The method of claim 6, wherein iteratively training the LSTM model by the set of target samples comprises:
inputting the identification information of the sample user in the target sample set, the identification information of each control clicked by the sample user, the page browsing time corresponding to each control clicked by the sample user and the historical click frequency corresponding to each control clicked by the sample user into an LSTM model to obtain a prediction control;
training parameters of the LSTM model according to an objective function formed by a prediction control and a control corresponding to the next operation of a sample user;
And the return execution inputs the identification information of the sample user in the target sample set, the identification information of each control clicked by the sample user, the page browsing time corresponding to each control clicked by the sample user and the historical click frequency corresponding to each control clicked by the sample user into the LSTM model to obtain the operation of the prediction control until the target model is obtained.
8. A user behavior prediction apparatus, comprising:
the first acquisition module is used for acquiring the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical click frequency corresponding to each control clicked by the user;
the first obtaining module is used for predicting the user behavior according to the identification information of the user, the identification information of each control clicked by the user, the page browsing time corresponding to each control clicked by the user and the historical click frequency corresponding to each control clicked by the user, so as to obtain the target control.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the user behavior prediction method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the user behavior prediction method of any one of claims 1-7 when executed.
CN202211649979.6A 2022-12-21 2022-12-21 User behavior prediction method, device, equipment and storage medium Pending CN116069604A (en)

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