CN117217810A - Business attention trend prediction method and system based on artificial intelligence - Google Patents
Business attention trend prediction method and system based on artificial intelligence Download PDFInfo
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- CN117217810A CN117217810A CN202311038173.8A CN202311038173A CN117217810A CN 117217810 A CN117217810 A CN 117217810A CN 202311038173 A CN202311038173 A CN 202311038173A CN 117217810 A CN117217810 A CN 117217810A
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Abstract
The application provides a business attention tendency prediction method and a system based on artificial intelligence, wherein the method comprises the following steps: acquiring resident information of a user in a current store; the residence information comprises residence time of a user on a commodity interface, conversation content of the user on the commodity interface, reciprocating times of the user on the commodity interface and interaction information between the user and a store; training a business concern prediction model based on the residence information; and inputting the resident information into a model, and predicting to obtain the current service attention tendency of the user. According to the application, the resident information of the user in the current store is acquired, and then the resident information is used as input to be input into the prediction model for training, so that the service attention tendency of the user can be predicted through the model, the randomness of manual prediction is further reduced, and the accuracy of the service attention tendency prediction of the user is improved.
Description
Technical Field
The application relates to the technical field of machine learning, in particular to a business attention tendency prediction method and system based on artificial intelligence. Background
With the development of the computing fields of the internet and the internet of things, customer service gradually becomes one of the key points of mining customers in the future, and precisely mining customer preferences is particularly important.
The customer preference refers to that the attention tendency of the customer to the commodity is predicted through the customer action so as to accurately predict the next action of the customer, the existing system needs to judge the next demand of the customer manually based on the customer action, and the judging mode is very low in efficiency and low in accuracy, and the purchasing action of the customer cannot form a complete closed loop.
Based on the above, the applicant provides a business attention tendency prediction method and system based on artificial intelligence, so as to solve the above technical problems.
Disclosure of Invention
The application provides a business attention tendency prediction method based on artificial intelligence, which is used for solving the problems in the background technology.
The application provides a business attention tendency prediction method based on artificial intelligence, which comprises the following steps:
acquiring resident information of a user in a current store; the residence information comprises residence time of a user on a commodity interface, conversation content of the user on the commodity interface, reciprocating times of the user on the commodity interface and interaction information between the user and a store;
training a business concern prediction model based on the residence information;
and inputting the resident information into a model, and predicting to obtain the current service attention tendency of the user.
According to the business concern trend prediction method based on artificial intelligence provided by the application, the method for acquiring the residence information of the user in the current store comprises the following steps:
and (3) uplink the resident information to realize information tracing.
According to the service attention tendency prediction method based on artificial intelligence provided by the application, the stay information is uplink, and the method comprises the following steps:
and carrying out offline and online chaining on the resident information.
According to the business concern trend prediction method based on artificial intelligence provided by the application, the resident information of the user in the current store is obtained, and the business concern trend prediction method further comprises the following steps:
and carrying out weight division on the resident information of different types.
According to the service attention tendency prediction method based on artificial intelligence provided by the application, the weight division is carried out on different kinds of resident information, and the method comprises the following steps:
the conversation content of the user on the commodity interface, the reciprocating times of the user on the commodity interface, the interaction information between the user and the store and the resident information of the user on the current store are successively decreased in weight.
According to the service attention trend prediction method based on artificial intelligence, the service attention prediction model is trained based on the resident information, and the method comprises the following steps:
and taking the resident information as input, inputting the resident information into a neural network for training to obtain the service attention prediction model.
According to the service attention tendency prediction method based on artificial intelligence provided by the application, after the prediction obtains the current service attention tendency of the user, the method further comprises the following steps:
and (5) the service attention tendency is uplink to improve the prediction accuracy.
The application also provides a service attention tendency prediction system based on artificial intelligence, which comprises the following steps:
the acquisition module is used for acquiring residence information of a user in a current store; the residence information comprises residence time of a user on a commodity interface, conversation content of the user on the commodity interface, reciprocating times of the user on the commodity interface and interaction information between the user and a store;
the training module is used for training the business attention prediction model based on the residence information;
and the prediction module is used for inputting the residence information into the model and predicting and obtaining the current service attention tendency of the user.
The application also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed implements the steps of the artificial intelligence based business concern trend prediction method as described above.
The present application also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the artificial intelligence based business concern trend prediction method as described above.
According to the business concern trend prediction method based on artificial intelligence, the resident information of the user in the current store is acquired, and then the resident information is used as input to be input into the prediction model for training, so that the business concern trend of the user can be predicted through the model, the randomness of the artificial prediction is further reduced, and the business concern trend prediction precision of the user is improved.
Drawings
In order to more clearly illustrate the application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an artificial intelligence based business concern trend prediction method provided by the application;
fig. 2 is a schematic structural diagram of an electronic device provided by the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, the application provides a service attention tendency prediction method based on artificial intelligence, which comprises the following steps:
s110, acquiring residence information of a user in a current store; the residence information comprises residence time of the user on the commodity interface, conversation content of the user on the commodity interface, reciprocating times of the user on the commodity interface and interaction information between the user and a store.
The attention tendency of the user can be further analyzed by extracting the stay time of the user on the commodity interface, the conversation content of the user on the commodity interface, the reciprocating times of the user on the commodity interface and the interaction information between the user and the store. The focus tendency is not limited to purchasing, recommending other people to purchase, and purchasing goals.
The longer the user stays at the merchandise interface, the higher the user's interest in the merchandise. The merchandise interface includes different categories, and the user can use the time data as one of the prediction criteria for the stay time of the different categories of merchandise. For example, the category of merchandise includes A, B, C, where the user's residence time is much longer at merchandise a than at merchandise B and merchandise C, and the user's propensity to purchase at merchandise a would be greater than at merchandise B and merchandise C.
The conversation content of the user on the commodity interface can be used for judging the attention tendency of the user by setting keywords, for example, sensitive words such as express, preferential, time and the like can be set and extracted as standards. The more sensitive words that appear in the conversation content, the higher the tendency to purchase is on behalf of the user.
The more the user reciprocates through the merchandise interface, the higher the interest level representing the user's analogy to the merchandise.
The interaction information between the user and the store includes: attention is paid to, and stores and the like are not limited to this.
Specifically, a blockchain can be set to link the stay time data of the user on the commodity interface, the session content data of the user on the commodity interface, the reciprocating times data of the user on the commodity interface and the interaction information between the user and the store, so that the information is traced, and the screening of the intention customers is facilitated. For example, user information that the number of times of reciprocation of the user on the commodity interface is greater than a threshold value can be extracted, and then operations such as return visit and the like are performed to improve the yield.
In one embodiment, the method for linking resident information comprises the steps of: the resident information is offline and online.
Therefore, the behavior characteristics of the user in the upper state and the lower state of the merchant can be both uplink, the integrity of data is ensured, and then the merchant can predict the next walking behavior of the user according to the behavior characteristics of the user, such as the attention tendency or the purchasing tendency of the user, and the like, so that the response rate of the merchant to the behavior of the user is improved.
Further, the weighting of different kinds of resident information includes: the conversation content of the user on the commodity interface, the reciprocating times of the user on the commodity interface, the interaction information between the user and the store and the resident information of the user on the current store are successively decreased in weight.
When prediction is performed, resident information is subjected to weight division, so that the data universality can be improved, and the prediction accuracy is not reduced.
Specifically, the weight of the prediction of the business attention trend of the user for the conversation content of the user and the commodity interface is higher than the reciprocating times of the user on the commodity interface. The more sensitive words in the conversation content, the higher the predictive weight for the user's propensity to focus. The weight of the reciprocation times of the user on the commodity interface is higher than the interaction information between the user and the store, and the more the reciprocation times of the user on the commodity interface are, the higher the interest degree of the user on the current commodity classification is represented. Similarly, the weight of the interaction information between the user and the store and the resident information of the user in the current store are sequentially decreased. The resident information is classified and weighted, so that the prediction accuracy of the attention tendency of the user can be improved.
S120, training the business attention prediction model based on the resident information.
Specifically, the resident information is used as input and is input into a neural network to be trained to obtain a business attention prediction model.
In the application, the LSTM neural network is selected, the resident information is divided into a plurality of units and is input into the neural network for training, and the trained service attention model is further combined with the weight proportion of the units to adjust the prediction trend so as to improve the prediction precision of the user on the service attention trend.
S130, inputting the resident information into the model, and predicting to obtain the current service attention tendency of the user.
And further, the method also comprises the step of uplink the service attention trend so as to improve the prediction accuracy. Because the actual behavior of the user can be compared with the predicted behavior after the uplink in the later stage, each user behavior forms a complete closed-loop chain.
For example, a month or half year after the residence information of the user occurs can be used as a deadline, the final behavior of the user is also uplink and compared with the predicted behavior before, and the data of the final behavior is further used as a reference to further adjust the prediction model so as to improve the prediction precision of the prediction model.
According to the business concern trend prediction method based on artificial intelligence, the resident information of the user in the current store is acquired, and then the resident information is used as input to be input into the prediction model for training, so that the business concern trend of the user can be predicted through the model, the randomness of the artificial prediction is further reduced, and the business concern trend prediction precision of the user is improved.
The application also provides a service attention tendency prediction system based on artificial intelligence, which comprises:
the acquisition module is used for acquiring residence information of a user in a current store; the residence information comprises residence time of a user on a commodity interface, conversation content of the user on the commodity interface, reciprocating times of the user on the commodity interface and interaction information between the user and a store;
the training module is used for training the business attention prediction model based on the resident information;
and the prediction module is used for inputting the resident information into the model and predicting and obtaining the current service attention tendency of the user.
According to the business concern trend prediction system based on artificial intelligence, the resident information of the user in the current store is acquired through the acquisition module, and then is input into the prediction model for training based on the resident information, so that the business concern trend of the user can be predicted through the model, the randomness of the artificial prediction is further reduced, and the business concern trend prediction precision of the user is improved.
Fig. 2 illustrates a physical schematic diagram of an electronic device, as shown in fig. 2, where the electronic device may include: processor 810, communication interface (Communications Interface) 820, memory 830, and communication bus 840, wherein processor 810, communication interface 820, memory 830 accomplish communication with each other through communication bus 840. The processor 810 may invoke logic instructions in the memory 830 to perform an artificial intelligence based business focus propensity prediction method comprising: acquiring resident information of a user in a current store; the residence information comprises residence time of a user on a commodity interface, conversation content of the user on the commodity interface, reciprocating times of the user on the commodity interface and interaction information between the user and a store;
training a business concern prediction model based on resident information;
and inputting the resident information into the model, and predicting to obtain the current service attention tendency of the user.
Further, the logic instructions in the memory 830 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present application also provides a computer program product, where the computer program product includes a computer program, where the computer program can be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, the computer is capable of executing the service attention tendency prediction method based on artificial intelligence provided by the above methods, and the method includes: acquiring resident information of a user in a current store; the residence information comprises residence time of a user on a commodity interface, conversation content of the user on the commodity interface, reciprocating times of the user on the commodity interface and interaction information between the user and a store;
training a business concern prediction model based on resident information;
and inputting the resident information into the model, and predicting to obtain the current service attention tendency of the user.
In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the artificial intelligence based traffic attention propensity prediction method provided by the above methods, the method comprising: acquiring resident information of a user in a current store; the residence information comprises residence time of a user on a commodity interface, conversation content of the user on the commodity interface, reciprocating times of the user on the commodity interface and interaction information between the user and a store;
training a business concern prediction model based on resident information;
and inputting the resident information into the model, and predicting to obtain the current service attention tendency of the user.
The apparatus embodiments described above are merely illustrative, wherein elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present application without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the various embodiments or methods of some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. The service attention tendency prediction method based on artificial intelligence is characterized by comprising the following steps of:
acquiring resident information of a user in a current store; the residence information comprises residence time of a user on a commodity interface, conversation content of the user on the commodity interface, reciprocating times of the user on the commodity interface and interaction information between the user and a store;
training a business concern prediction model based on the residence information;
and inputting the resident information into a model, and predicting to obtain the current service attention tendency of the user.
2. The business concern trend prediction method based on artificial intelligence according to claim 1, wherein the obtaining the resident information of the user in the current store comprises:
and (3) uplink the resident information to realize information tracing.
3. The artificial intelligence based traffic attention propensity prediction method according to claim 2, wherein the uplink of the resident information comprises:
and carrying out offline and online chaining on the resident information.
4. The business concern trend prediction method based on artificial intelligence according to claim 1, wherein the acquiring the resident information of the user in the current store further comprises:
and carrying out weight division on the resident information of different types.
5. The artificial intelligence based traffic attention propensity prediction method according to claim 4, wherein the weight partitioning of heterogeneous resident information comprises:
the conversation content of the user on the commodity interface, the reciprocating times of the user on the commodity interface, the interaction information between the user and the store and the resident information of the user on the current store are successively decreased in weight.
6. The artificial intelligence based business concern trend prediction method according to claim 1, wherein the training the business concern prediction model based on the resident information comprises:
and taking the resident information as input, inputting the resident information into a neural network for training to obtain the service attention prediction model.
7. The method for predicting service attention tendency based on artificial intelligence according to claim 1, wherein after predicting that the current service attention tendency of the user is obtained, further comprises:
and (5) the service attention tendency is uplink to improve the prediction accuracy.
8. An artificial intelligence based business concern trend prediction system, comprising:
the acquisition module is used for acquiring residence information of a user in a current store; the residence information comprises residence time of a user on a commodity interface, conversation content of the user on the commodity interface, reciprocating times of the user on the commodity interface and interaction information between the user and a store;
the training module is used for training the business attention prediction model based on the residence information;
and the prediction module is used for inputting the residence information into the model and predicting and obtaining the current service attention tendency of the user.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the artificial intelligence based business focus propensity prediction method according to any one of claims 1 to 7 when the program is executed.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the artificial intelligence based business focus propensity prediction method according to any one of claims 1 to 7.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311038173.8A CN117217810A (en) | 2023-08-17 | 2023-08-17 | Business attention trend prediction method and system based on artificial intelligence |
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| CN202311038173.8A CN117217810A (en) | 2023-08-17 | 2023-08-17 | Business attention trend prediction method and system based on artificial intelligence |
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| CN117217810A true CN117217810A (en) | 2023-12-12 |
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| CN202311038173.8A Pending CN117217810A (en) | 2023-08-17 | 2023-08-17 | Business attention trend prediction method and system based on artificial intelligence |
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