Personalized content push analysis system and method for mothers and infants based on AI scenarios

Abstract

The present invention relates to the technical field of maternal and infant content recommendation, and in particular to a personalized content push analysis system and method for maternal and infant groups based on AI scenarios. The steps of the method include: obtaining the historical conversation content and current conversation content of the user, and obtaining the historical purchase record of the user; estimating a first set of infant demand products based on the historical conversation content and the historical purchase record; extracting infant products in the current conversation content to form a second set of infant demand products; generating product recommendation content based on the first set of infant demand products and the second set of infant demand products. The present invention improves data utilization efficiency and product recommendation accuracy by correlating user conversation content with user purchase behavior in AI scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of maternal and infant content recommendation, and in particular to a system and method for pushing and analyzing personalized content for maternal and infant groups based on AI scenarios. Background Art

[0002] With the rapid development of the Internet and information technology, the application of artificial intelligence (AI) in the maternal and infant industry is gradually deepening, and the demand for personalized content services for mothers and infants is growing.

[0003] Traditional maternal and infant product recommendations often rely on static user portraits and historical purchasing behaviors, making it difficult to capture changes in user needs in real time. Personalized content push for maternal and infant groups based on AI scenarios can use natural language processing technology to analyze user conversation content in real time, tap into user potential needs, and provide users with accurate product recommendations and information services.

[0004] The content of users' conversations in AI scenarios reflects their immediate concerns, while purchasing behavior reflects their actual decisions and consumption habits. The existing personalized content push analysis methods for maternal and infant populations cannot correlate the content of users' conversations in AI scenarios with their purchasing behaviors, which in turn leads to information islands, low data utilization efficiency and low product recommendation accuracy. Summary of the invention

[0005] In order to overcome the defects and shortcomings of the prior art, the present invention provides a personalized content push analysis system and method for mothers and babies based on AI scenarios. By correlating and analyzing the user conversation content in the AI ​​scenario with the user purchasing behavior, the data utilization efficiency and product recommendation accuracy are improved.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for analyzing personalized content push for mothers and infants based on AI scenarios, comprising the following steps:

[0008] Obtain the user's historical conversation content and current conversation content, and obtain the user's historical purchase records;

[0009] estimating a first set of products in demand for infants and young children based on the historical conversation content and the historical purchase records;

[0010] Extracting infant products from the current conversation content to form a second infant demand product set;

[0011] Product recommendation content is generated based on the first set of products required by infants and young children and the second set of products required by infants and young children.

[0012] As a preferred technical solution, the estimated first set of infant demand products includes:

[0013] Obtaining the historical purchase records and the historical conversation content;

[0014] The periodic demand index of infant products is calculated by the historical purchase records, and the calculation formula of the periodic demand index is:

[0015] ;

[0016] In the formula Indicates The purchase time of the product corresponding to the purchase of the product. Indicates The purchase time of the product corresponding to the purchase of the product. Indicates the number of times a product is purchased. Indicates the average purchase frequency of infant and toddler products. Represents the cyclical demand index for infant and toddler products;

[0017] Calculating the demand relevance index of infant products based on the historical conversation content;

[0018] The infant product demand intensity index is calculated by using the periodic demand index and the demand correlation index. The calculation formula of the infant product demand intensity index is:

[0019] ;

[0020] In the formula represents the cyclical demand index, represents the demand correlation index, represents the first adjustment factor, It represents the demand intensity index of infant and toddler products;

[0021] Infant products whose infant product demand intensity index is greater than a preset demand intensity threshold are included in the first infant demand product set.

[0022] As a preferred technical solution, the calculation formula for the average purchase frequency is:

[0023] ;

[0024] In the formula Indicates The purchase time of the product corresponding to the purchase of the product. Indicates The purchase time of the product corresponding to the purchase of the product. Indicates the number of times a product is purchased. Indicates the average purchase frequency of baby products.

[0025] As a preferred technical solution, the calculation of the demand association index of infant products includes:

[0026] Extracting the number of appearances of infant products and the corresponding product appearance times in the historical conversation content by natural language processing technology;

[0027] The frequency correlation coefficient and time correlation coefficient of the infant product are calculated by the number of times the product appears and the time when the product appears. The calculation formula of the frequency correlation coefficient is:

[0028] ;

[0029] In the formula Indicates infant products The number of product appearances, Indicates infant products The number of product appearances, represents the frequency correlation coefficient;

[0030] The demand correlation index of infant products is calculated by the frequency correlation coefficient and the time correlation coefficient. The demand correlation index calculation formula is:

[0031] ;

[0032] In the formula represents the frequency correlation coefficient, represents the time correlation coefficient, represents the second adjustment factor, Represents the demand correlation index of infant and toddler products.

[0033] As a preferred technical solution, the calculation formula of the time correlation coefficient is:

[0034] ;

[0035] In the formula Indicates infant products The number of product appearances, Indicates infant products The number of product appearances, Indicates infant products In the The time of occurrence, Indicates infant products In the The time of occurrence, represents the time correlation coefficient.

[0036] As a preferred technical solution, the generating of product recommendation content includes:

[0037] Acquire the first set of products required by infants and young children and the second set of products required by infants and young children;

[0038] Performing an intersection operation on the first set of infant demand products and the second set of infant demand products to obtain a third set of infant demand products;

[0039] When the third infant demand product set is a non-empty set, the infant products in the third infant demand product set are used as product recommendation content;

[0040] When the third infant demand product set is an empty set, the infant products in the first infant demand product set and the second infant demand product set are used as product recommendation content.

[0041] It should be noted here that the first adjustment factor, the second adjustment factor and the preset demand intensity threshold are determined as follows: 5,000 sets of historical conversation content, current conversation content and historical purchase records are collected to distinguish whether the recommendation effect of infant products meets the recommendation requirements, and the historical conversation content, current conversation content and historical purchase records are substituted into the infant product demand intensity index calculation formula for calculation, and the calculated infant product demand intensity index and the distinction results are simultaneously imported into the fitting software, and the optimal first adjustment factor, the second adjustment factor and the preset demand intensity threshold that meet the distinction accuracy of the distinction results are output.

[0042] In a second aspect, an embodiment of the present invention provides a personalized content push analysis system for mothers and infants based on AI scenarios, including:

[0043] The information collection module is used to obtain the user's historical conversation content and current conversation content, and obtain the user's historical purchase records;

[0044] A first acquisition module, configured to estimate a first set of infant demand products based on the historical conversation content and the historical purchase records;

[0045] A second acquisition module, configured to extract infant products from the current conversation content to form a second infant demand product set;

[0046] The recommendation content generation module is used to generate product recommendation content according to the first set of products required by infants and young children and the second set of products required by infants and young children.

[0047] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a method for personalized content push analysis for mothers and babies based on AI scenarios by calling the computer program stored in the memory.

[0048] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute a method for analyzing personalized content push for mothers and infants based on AI scenarios.

[0049] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0050] The present invention estimates a first set of products needed for infants and young children through the user's historical conversation content and historical purchase records. At the same time, it constructs a second set of products needed for infants and young children by extracting infant products in the user's current conversation content. The first set of products needed for infants and young children and the second set of products needed for infants and young children are effectively combined to generate product recommendation content. While obtaining the user's actual decision-making and consumption habits, the present invention pays attention to the user's real-time demand changes, thereby effectively improving data utilization efficiency and product recommendation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 A schematic diagram of the overall process of the AI ​​scenario-based maternal and infant personalized content push analysis method provided by the present invention;

[0053] Figure 2 A schematic diagram of a process for estimating the first set of products required by infants and young children in the method for analyzing personalized content push for mothers and infants based on AI scenarios provided by the present invention;

[0054] Figure 3 A schematic diagram of the structure of the personalized content push analysis system for mothers and infants based on AI scenarios provided by the present invention;

[0055] Figure 4 This is a schematic structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0057] like Figure 1 As shown, an embodiment of the present invention provides a method for analyzing personalized content push for mothers and infants based on AI scenarios, which specifically includes the following steps:

[0058] S110: Obtain the user's historical conversation content and current conversation content, and obtain the user's historical purchase record.

[0059] S120: estimating a first set of products in demand for infants and young children based on the historical conversation content and the historical purchase records;

[0060] By calculating the cyclical demand index through historical purchase records, we can effectively identify the fluctuations in users’ demand for infant products in different time periods, and then reveal the user’s demand pattern for products. By calculating the demand association index through historical conversation content, we can dig out the demand relationship between users for different products in specific situations, and further enrich the background information for product recommendations. Figure 2 As shown, the specific steps for estimating the first infant demand product set include:

[0061] Obtaining the historical purchase records and the historical conversation content;

[0062] Calculating a periodic demand index for infant products based on the historical purchase records;

[0063] Calculating the demand relevance index of infant products based on the historical conversation content;

[0064] The infant product demand intensity index is calculated by using the periodic demand index and the demand correlation index. The calculation formula of the infant product demand intensity index is:

[0065] ;

[0066] In the formula represents the cyclical demand index, represents the demand correlation index, represents the first adjustment factor, It represents the demand intensity index of infant and toddler products;

[0067] Including the infant and young child product whose demand intensity index for infant and young child product is greater than a preset demand intensity threshold into a first set of infant and young child demand products;

[0068] The cyclical demand index of infant products refers to a quantitative indicator of the intensity and frequency of demand for infant products within a specific time period. It reflects the usage frequency and demand fluctuations of different products at different stages as infants grow. The significance of the cyclical demand index for product recommendations lies in that it provides data support for personalized recommendations. It can monitor cyclical demand in real time according to the growth stage of infants and improve the accuracy of product recommendations. The specific steps for calculating the cyclical demand index of infant products include:

[0069] Obtain the number of purchases of infant products and the corresponding product purchase time in the historical purchase records;

[0070] The average purchase frequency of infant products is calculated by the number of product purchases and the product purchase time. The calculation formula of the average purchase frequency is:

[0071] ;

[0072] In the formula Indicates The purchase time of the product corresponding to the purchase of the product. Indicates The purchase time of the product corresponding to the purchase of the product. Indicates the number of times a product is purchased. Indicates the average purchase frequency of infant and toddler products, e.g. hour, ,in, Indicates the purchase time of the product corresponding to the first purchase of the product. Indicates the purchase time of the product corresponding to the second purchase of the product. Indicates the purchase time of the product corresponding to the third purchase of the product. Indicates the purchase time of the product corresponding to the 4th purchase of the product. represents the purchase time of the product corresponding to the fifth purchase of the product. The average purchase frequency of infant products is obtained by calculating the average time interval between two consecutive purchases of the same infant product;

[0073] The periodic demand index of infant products is calculated by the average purchase frequency, and the calculation formula of the periodic demand index is:

[0074] ;

[0075] In the formula Indicates The purchase time of the product corresponding to the purchase of the product. Indicates The purchase time of the product corresponding to the purchase of the product. Indicates the number of times a product is purchased. Indicates the average purchase frequency of infant and toddler products. Represents the cyclical demand index for infant and toddler products;

[0076] The demand correlation index of infant products is a quantitative indicator that measures the demand relationship between different infant products. The demand correlation index reflects whether there is a correlation between the demands for certain infant products in a specific period of time, that is, when users pay attention to a certain product, whether they tend to pay attention to other specific products at the same time. For example, when a user mentions baby formula in a conversation, he or she may also mention complementary food or milk bottles. This correlation is reflected in the demand correlation index. Therefore, by accurately identifying the degree of demand correlation, it is possible to recommend products that better meet the user's needs during the user's purchase process, thereby enhancing the user experience. The specific steps for calculating the demand correlation index of infant products include:

[0077] Extracting the number of appearances of infant products and the corresponding product appearance times in the historical conversation content by natural language processing technology;

[0078] The frequency correlation coefficient and time correlation coefficient of the infant product are calculated by the number of times the product appears and the time when the product appears. The calculation formula of the frequency correlation coefficient is:

[0079] ;

[0080] In the formula Indicates infant products The number of product appearances, Indicates infant products The number of product appearances, represents the frequency correlation coefficient;

[0081] The demand correlation index of infant products is calculated by the frequency correlation coefficient and the time correlation coefficient. The demand correlation index calculation formula is:

[0082] ;

[0083] In the formula represents the frequency correlation coefficient, represents the time correlation coefficient, represents the second adjustment factor, It represents the demand correlation index of infant and toddler products;

[0084] The product correlation is determined by calculating the time interval between different products. If the time interval between two products mentioned in the conversation is small, it indicates that their demand correlation is strong. At the same time, it is necessary to consider whether the frequency of appearance of the products is consistent. If the frequency of mention of two products is close, it also indicates that the demand correlation between the two is strong. The calculation formula of the time correlation coefficient is:

[0085] ;

[0086] In the formula Indicates infant products The number of product appearances, Indicates infant products The number of product appearances, Indicates infant products In the The time of occurrence, Indicates infant products In the The time of occurrence, represents the time correlation coefficient.

[0087] S130: extracting infant products from the current conversation content to form a second set of infant demand products, wherein the method of extracting the infant products from the current conversation content is any one of natural language processing (NLP) and keyword matching. Natural language processing (NLP) performs text preprocessing, part-of-speech tagging (POSTagging) and named entity recognition (NER) on the current conversation content, and then extracts the infant products mentioned in the current conversation content. Keyword matching constructs a keyword library, uses a string matching algorithm, scans the user conversation content, and searches for infant products that match the entries in the keyword library. Natural language processing (NLP) can deeply understand the semantics and context of the user conversation, and keyword matching can quickly identify the infant products in the conversation content.

[0088] S140: generating product recommendation content according to the first infant demand product set and the second infant demand product set;

[0089] By performing an intersection operation on the first infant demand product set and the second infant demand product set, more accurate and personalized product recommendation content can be generated. The specific steps of generating the product recommendation content include:

[0090] Acquire the first set of products required by infants and young children and the second set of products required by infants and young children;

[0091] Performing an intersection operation on the first set of infant demand products and the second set of infant demand products to obtain a third set of infant demand products;

[0092] When the third infant demand product set is a non-empty set, the infant products in the third infant demand product set are used as product recommendation content;

[0093] When the third infant demand product set is an empty set, the infant products in the first infant demand product set and the second infant demand product set are used as product recommendation content.

[0094] like Figure 3 As shown, an embodiment of the present invention provides a personalized content push analysis system for mothers and infants based on AI scenarios, including:

[0095] Information collection module 210, used to obtain the user's historical conversation content and current conversation content, and obtain the user's historical purchase record;

[0096] A first acquisition module 220, configured to estimate a first set of infant demand products based on the historical conversation content and the historical purchase records;

[0097] A second acquisition module 230, configured to extract infant products from the current conversation content to form a second infant demand product set;

[0098] The recommendation content generating module 240 is used to generate product recommendation content according to the first infant demand product set and the second infant demand product set.

[0099] In an embodiment of the present invention, the first acquisition module 220 is used to estimate the first set of infant demand products according to the historical conversation content and the historical purchase record. The specific steps of estimating the first set of infant demand products include:

[0100] Obtaining the historical purchase records and the historical conversation content;

[0101] Calculating a periodic demand index for infant products based on the historical purchase records;

[0102] Calculating the demand relevance index of infant products based on the historical conversation content;

[0103] The infant product demand intensity index is calculated by using the periodic demand index and the demand correlation index. The calculation formula of the infant product demand intensity index is:

[0104] ;

[0105] In the formula represents the cyclical demand index, represents the demand correlation index, represents the first adjustment factor, It represents the demand intensity index of infant and toddler products;

[0106] Including the infant and young child product whose demand intensity index for infant and young child product is greater than a preset demand intensity threshold into a first set of infant and young child demand products;

[0107] The specific steps for calculating the cyclical demand index for infant and toddler products include:

[0108] Obtain the number of purchases of infant products and the corresponding product purchase time in the historical purchase records;

[0109] The average purchase frequency of infant products is calculated by the number of product purchases and the product purchase time. The calculation formula of the average purchase frequency is:

[0110] ;

[0111] In the formula Indicates The purchase time of the product corresponding to the purchase of the product. Indicates The purchase time of the product corresponding to the purchase of the product. Indicates the number of times a product is purchased. Indicates the average purchase frequency of infant and toddler products;

[0112] The periodic demand index of infant products is calculated by the average purchase frequency, and the calculation formula of the periodic demand index is:

[0113] ;

[0114] In the formula Indicates The purchase time of the product corresponding to the purchase of the product. Indicates The purchase time of the product corresponding to the purchase of the product. Indicates the number of times a product is purchased. Indicates the average purchase frequency of infant and toddler products. Represents the cyclical demand index for infant and toddler products;

[0115] The specific steps for calculating the demand relevance index for infant and toddler products include:

[0116] Extracting the number of appearances of infant products and the corresponding product appearance times in the historical conversation content by natural language processing technology;

[0117] The frequency correlation coefficient and time correlation coefficient of the infant product are calculated by the number of times the product appears and the time when the product appears. The calculation formula of the frequency correlation coefficient is:

[0118] ;

[0119] In the formula Indicates infant products The number of product appearances, Indicates infant products The number of product appearances, represents the frequency correlation coefficient;

[0120] The demand correlation index of infant products is calculated by the frequency correlation coefficient and the time correlation coefficient. The demand correlation index calculation formula is:

[0121] ;

[0122] In the formula represents the frequency correlation coefficient, represents the time correlation coefficient, represents the second adjustment factor, It represents the demand correlation index of infant and toddler products;

[0123] The calculation formula of the time correlation coefficient is:

[0124] ;

[0125] In the formula Indicates infant products The number of product appearances, Indicates infant products The number of product appearances, Indicates infant products In the The time of occurrence, Indicates infant products In the The time of occurrence, represents the time correlation coefficient.

[0126] In an embodiment of the present invention, the second acquisition module 230 is used to extract infant products in the current conversation content to constitute a second infant demand product set, wherein the method of extracting the infant products in the current conversation content is any one of natural language processing (NLP) and keyword matching. Natural language processing (NLP) performs text preprocessing, part-of-speech tagging (POS Tagging) and named entity recognition (NER) on the current conversation content, and then extracts the infant products mentioned in the current conversation content. Keyword matching constructs a keyword library, uses a string matching algorithm, scans the user conversation content, and searches for infant products that match the entries in the keyword library. Natural language processing (NLP) can deeply understand the semantics and context of the user conversation, and keyword matching can quickly identify the infant products in the conversation content.

[0127] In an embodiment of the present invention, the recommendation content generating module 240 is used to generate product recommendation content according to the first infant demand product set and the second infant demand product set. The specific steps of generating product recommendation content include:

[0128] Acquire the first set of products required by infants and young children and the second set of products required by infants and young children;

[0129] Performing an intersection operation on the first set of infant demand products and the second set of infant demand products to obtain a third set of infant demand products;

[0130] When the third infant demand product set is a non-empty set, the infant products in the third infant demand product set are used as product recommendation content;

[0131] When the third infant demand product set is an empty set, the infant products in the first infant demand product set and the second infant demand product set are used as product recommendation content.

[0132] The above-mentioned parameters and steps for each unit module to realize the corresponding functions in the personalized content push analysis system for mothers and babies based on AI scenarios of the present invention can be referred to the parameters and steps in the embodiment of the personalized content push analysis method for mothers and babies based on AI scenarios, and will not be repeated here.

[0133] like Figure 4 As shown, an embodiment of the present invention further provides an electronic device 300, including a memory 320 for storing a computer program 322; and a processor 310 for executing the computer program 322 to implement a method for analyzing personalized content push for mothers and babies based on AI scenarios as in any of the above embodiments.

[0134] It should be noted that Figure 4It is a structural diagram of an electronic device 300 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of application of the present invention.

[0135] Specifically, the electronic device 300 may include: at least one processor 310, at least one memory 320, a power supply 330, a communication interface 340, an input / output interface 350, and a communication bus 360. The memory 320 is used to store a computer program 322, which is loaded and executed by the processor 310 to implement the relevant steps in the method for analyzing personalized content push for mothers and infants based on AI scenarios disclosed in any of the aforementioned embodiments. In addition, the electronic device 300 in the embodiment of the present invention may be specifically an electronic computer.

[0136] In an embodiment of the present invention, a power supply 330 is used to provide a working voltage for each hardware device on the electronic device 300; the communication interface 340 can create a data transmission channel between the electronic device 300 and an external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present invention, and is not specifically limited here; the input and output interface 350 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0137] In addition, the memory 320, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 321, a computer program 322, etc., and the storage method can be temporary storage or permanent storage.

[0138] Among them, the operating system 321 is used to manage and control various hardware devices and computer programs on the electronic device 300, which can be Windows Server, NetWare, Unix, Linux, etc. In addition to including the computer program 322 that can be used to complete the AI ​​scene maternal and child group personalized content push analysis method performed by the electronic device 300 disclosed in any of the aforementioned embodiments, the computer program 322 can further include a computer program 322 that can be used to complete other specific tasks.

[0139] An embodiment of the present invention also provides a computer-readable storage medium for storing a computer program 322. When the computer program 322 is executed by the processor 310, it implements the personalized content push analysis method for mothers and babies based on AI scenarios as in any of the above embodiments.

[0140] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

Claims

1. A personalized content push analysis method for mothers and infants based on AI scenarios, characterized by: The steps include: Obtain the user's historical conversation content and current conversation content, and obtain the user's historical purchase records; estimating a first set of products in demand for infants and young children based on the historical conversation content and the historical purchase records; Extracting infant products from the current conversation content to form a second infant demand product set; generating product recommendation content according to the first set of infant demand products and the second set of infant demand products; The estimated first infant demand product set includes: Obtaining the historical purchase records and the historical conversation content; The periodic demand index of infant products is calculated by the historical purchase records, and the calculation formula of the periodic demand index is: ； In the formula Indicates The purchase time of the product corresponding to the purchase of the product. Indicates The purchase time of the product corresponding to the purchase of the product. Indicates the number of times a product is purchased. Indicates the average purchase frequency of infant and toddler products. Represents the cyclical demand index for infant and toddler products; Calculating the demand relevance index of infant products based on the historical conversation content; The infant product demand intensity index is calculated by using the periodic demand index and the demand correlation index. The calculation formula of the infant product demand intensity index is: ； In the formula represents the cyclical demand index, represents the demand correlation index, represents the first adjustment factor, It represents the demand intensity index of infant and toddler products; Including the infant and young child product whose demand intensity index for infant and young child product is greater than a preset demand intensity threshold into a first set of infant and young child demand products; The calculation of the demand relevance index for infant products includes: Extracting the number of appearances of infant products and the corresponding product appearance times in the historical conversation content by natural language processing technology; The frequency correlation coefficient and time correlation coefficient of the infant product are calculated by the number of times the product appears and the time when the product appears. The calculation formula of the frequency correlation coefficient is: ； In the formula Indicates infant products The number of product appearances, Indicates infant products The number of product appearances, represents the frequency correlation coefficient; The demand correlation index of infant products is calculated by the frequency correlation coefficient and the time correlation coefficient. The demand correlation index calculation formula is: ； In the formula represents the frequency correlation coefficient, represents the time correlation coefficient, represents the second adjustment factor, Represents the demand correlation index of infant and toddler products.

2. The method for analyzing personalized content push for mothers and infants based on AI scenarios according to claim 1 is characterized in that: The calculation formula of the time correlation coefficient is: ； In the formula Indicates infant products The number of product appearances, Indicates infant products The number of product appearances, Indicates infant products In the The time of occurrence, Indicates infant products In the The time of occurrence, represents the time correlation coefficient.

3. The method for analyzing personalized content push for mothers and infants based on AI scenarios according to claim 1 is characterized in that: The generating of product recommendation content includes: Acquire the first set of products required by infants and young children and the second set of products required by infants and young children; Performing an intersection operation on the first set of infant demand products and the second set of infant demand products to obtain a third set of infant demand products; When the third infant demand product set is a non-empty set, the infant products in the third infant demand product set are used as product recommendation content; When the third infant demand product set is an empty set, the infant products in the first infant demand product set and the second infant demand product set are used as product recommendation content.

4. A personalized content push analysis system for mothers and infants based on AI scenarios, which is implemented based on a personalized content push analysis method for mothers and infants based on AI scenarios as described in any one of claims 1-3, characterized in that: The system comprises: The information collection module is used to obtain the user's historical conversation content and current conversation content, and obtain the user's historical purchase records; A first acquisition module, configured to estimate a first set of infant demand products based on the historical conversation content and the historical purchase records; A second acquisition module, configured to extract infant products from the current conversation content to form a second infant demand product set; The recommendation content generation module is used to generate product recommendation content according to the first set of products required by infants and young children and the second set of products required by infants and young children.

5. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the AI ​​scenario-based mother-infant group personalized content push analysis method as described in any one of claims 1-3 by calling the computer program stored in the memory.

6. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the AI ​​scenario-based mother-infant group personalized content push analysis method as described in any one of claims 1-3.

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