Product strategy delivery

Abstract

Disclosed in the embodiments of the present description are a product strategy delivery method and apparatus, and a storage medium and a terminal. The method comprises: on the basis of a user action sequence, determining at least one target professional factor that meets a preset popularity condition, wherein the professional factor is a factor that provides a professional explanation for a product; determining at least one candidate product strategy corresponding to each target professional factor, wherein the candidate product strategy at least comprises a target professional factor and a strategy element corresponding to the target professional factor; and determining, from among all candidate product strategies, a candidate product strategy that meets a preset delivery condition as a target product strategy to be delivered. Since a professional factor can provide a professional explanation for a product strategy, a corresponding product strategy determined on the basis of a target professional factor can explicitly present an intrinsic professional concept of a product to a user.

Description

Product strategy launch Technical Field

[0001] The embodiments of this specification relate to the field of computer Internet technology, and in particular to a product strategy delivery method, device, storage medium, and terminal. Background Art

[0002] With the development of the internet industry, more and more users are conducting financial transactions through online trading platforms, such as purchasing daily necessities on online shopping platforms and buying and selling funds and stocks on financial institutions' platforms. Financial institutions, for example, often recommend financial products to users, providing professional and reliable purchasing guidance and improving user experience and satisfaction. Therefore, a product strategy delivery method is needed that can determine optimal recommendation strategies based on a professional perspective, helping users quickly obtain high-quality product information.

[0003] Summary of the Invention

[0004] The embodiments of this specification provide a product strategy delivery method, device, storage medium, and terminal, which can solve the technical problem in related technologies that users cannot obtain professional information about recommended products.

[0005] In a first aspect, an embodiment of the present specification provides a product strategy delivery method, the method comprising: based on a user behavior sequence of at least one user for a historical product strategy, determining at least one target professional factor that meets a preset popularity condition from at least one preset professional factor, the professional factor being a factor for professional interpretation of the product; respectively determining at least one candidate product strategy corresponding to each target professional factor, the candidate product strategy including at least a target professional factor and a strategy element corresponding to the target professional factor; and determining, from all candidate product strategies, a candidate product strategy that meets the preset delivery condition as the target product strategy to be delivered.

[0006] In a second aspect, an embodiment of the present specification provides a product strategy delivery device, which includes: a professional factor selection module, which is used to determine at least one target professional factor that meets preset popularity conditions from at least one preset professional factor based on the user behavior sequence of at least one user for historical product strategies, wherein the professional factor is a factor for professional interpretation of the product; a candidate strategy determination module, which is used to respectively determine at least one candidate product strategy corresponding to each target professional factor, wherein the candidate product strategy includes at least a target professional factor and a strategy element corresponding to the target professional factor; a product strategy delivery module, which is used to determine, from all candidate product strategies, a candidate product strategy that meets preset delivery conditions as the target product strategy to be delivered.

[0007] In a third aspect, an embodiment of this specification provides a computer program product comprising instructions, which, when executed on a computer or a processor, enables the computer or the processor to execute the steps of the above method.

[0008] In a fourth aspect, an embodiment of this specification provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the steps of the above method.

[0009] In a fifth aspect, an embodiment of this specification provides a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is suitable for being loaded by the processor and executing the steps of the above method.

[0010] The beneficial effects brought about by the technical solutions provided by some embodiments of this specification include at least the following: the embodiments of this specification provide a product strategy delivery method, based on the user behavior sequence of at least one user for a historical product strategy, at least one target professional factor that meets the preset popularity condition is determined from at least one preset professional factor, the professional factor being a factor for professionally explaining the product; at least one candidate product strategy corresponding to each target professional factor is determined respectively, the candidate product strategy at least including the target professional factor and the strategy element corresponding to the target professional factor; from all candidate product strategies, the candidate product strategy that meets the preset delivery condition is determined as the target product strategy to be delivered. Since the professional factor can provide a professional explanation for the product strategy, when the popular target professional factor determined according to the user behavior sequence is used as the core of the product strategy, the corresponding product strategy determined according to the target professional factor can explicitly display the inherent professional concept of the product to the user, so that the user can quickly obtain product information based on the product strategy, which can solve the user's urgent need for explainable product professional information. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0012] FIG1 is an exemplary system architecture diagram of a product strategy delivery method provided in an embodiment of this specification.

[0013] FIG2 is a flow chart of a product strategy delivery method provided in an embodiment of this specification.

[0014] FIG3 is a flow chart of a product strategy delivery method provided in an embodiment of this specification.

[0015] FIG4 is a schematic diagram of a tree structure of a policy element assembly rule provided in an embodiment of this specification.

[0016] FIG5 is a schematic diagram of a display interface for preselecting strategy specifications provided in an embodiment of this specification.

[0017] FIG6 is a logic flow diagram of a product strategy delivery method provided in an embodiment of this specification.

[0018] FIG7 is a diagram illustrating a behavior link model in a strategy delivery model provided in an embodiment of this specification.

[0019] FIG8 is a model structure diagram of a strategic delivery model provided in an embodiment of this specification.

[0020] FIG9 is a structural block diagram of a product strategy delivery device provided in an embodiment of this specification.

[0021] FIG10 is a schematic diagram of the structure of a terminal provided in an embodiment of this specification. DETAILED DESCRIPTION

[0022] To make the features and advantages of the embodiments of this specification more obvious and easy to understand, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the embodiments of this specification.

[0023] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this specification. Instead, they are merely examples of devices and methods consistent with certain aspects of the embodiments of this specification, as detailed in the appended claims.

[0024] With the development of the internet industry, more and more users are conducting financial transactions through online trading platforms, such as purchasing daily necessities on online shopping platforms and buying and selling funds and stocks on financial institutions' platforms. Financial institutions, for example, often recommend financial products to users to provide professional and reliable purchasing guidance, thereby improving user experience and satisfaction. A common method of recommendation guidance is to place configured product strategies on user browsing pages. Product strategies are instantiated forms of strategies composed of text, graphics, and action points used to introduce product information and information in marketing recommendation and advertising systems. In financial scenarios, product strategies are also called financial strategies, designed to attract users to click and subsequently purchase.

[0025] Currently, in some common product strategy delivery plans, manual configuration and input are usually performed by operations personnel during the strategy material configuration stage. During the manual configuration process, operations personnel manually select a combination of strategy elements such as text and charts to be displayed to users based on their own prior experience. When configuring the strategy, the main considerations are display effects, delivery locations, etc.

[0026] However, in some highly specialized scenarios, such as finance, most users who purchase financial fund products typically follow, click on, and purchase products based on basic knowledge, such as returns, risks, and recent trends. In other words, in these scenarios, users are more focused on the product's intrinsic attributes and professional analysis, and prefer to intuitively see the product's intrinsic professional attributes in the strategy. However, existing strategy deployment solutions do not explicitly display these intrinsic professional attributes that are strongly related to the product itself, resulting in them failing to meet users' urgent need for product explainability.

[0027] Therefore, an embodiment of this specification provides a product strategy delivery method, which determines at least one target professional factor that meets preset popularity conditions based on user behavior sequences, where the professional factor is a factor that provides professional interpretation of the product; determines at least one candidate product strategy corresponding to each target professional factor, where the candidate product strategy includes at least the target professional factor and the strategy element corresponding to the target professional factor; and determines from all candidate product strategies that the candidate product strategy that meets the preset delivery conditions is the target product strategy to be delivered, so as to solve the technical problem that the above-mentioned user cannot obtain the professional information of the recommended product.

[0028] Please refer to FIG1 , which is an exemplary system architecture diagram of a product strategy delivery method provided in an embodiment of this specification.

[0029] As shown in Figure 1, the system architecture may include a terminal 101, a network 102, and a server 103. The network 102 is used to provide a medium for a communication link between the terminal 101 and the server 103. The network 102 may include various types of wired communication links or wireless communication links, for example, a wired communication link including an optical fiber, a twisted pair, or a coaxial cable, and a wireless communication link including a Bluetooth communication link, a Wireless-Fidelity (Wi-Fi) communication link, or a microwave communication link.

[0030] The terminal 101 can interact with the server 103 through the network 102 to receive a message from the server 103 or send a message to the server 103, or the terminal 101 can interact with the server 103 through the network 102 to receive a message or data sent by other users to the server 103. The terminal 101 can be hardware or software. When the terminal 101 is hardware, it can be various electronic devices, including but not limited to smart watches, smart phones, tablet computers, laptop portable computers and desktop computers. When the terminal 101 is software, it can be installed in the electronic devices listed above, which can be implemented as multiple software or software modules (for example: for providing distributed services), or it can be implemented as a single software or software module, which is not specifically limited here.

[0031] In an embodiment of the present specification, the terminal 101 first determines at least one target professional factor that meets the preset popularity condition from at least one preset professional factor based on the user behavior sequence of at least one user for the historical product strategy, and the professional factor is a factor for professional interpretation of the product; then, the terminal 101 needs to determine at least one candidate product strategy corresponding to each target professional factor respectively, and the candidate product strategy includes at least the target professional factor and the strategy element corresponding to the target professional factor; finally, the terminal 101 determines the candidate product strategy that meets the preset delivery condition from all candidate product strategies as the target product strategy to be delivered.

[0032] The server 103 may be a business server that provides various services. It should be noted that the server 103 may be hardware or software. When the server 103 is hardware, it may be implemented as a distributed server cluster consisting of multiple servers, or it may be implemented as a single server. When the server 103 is software, it may be implemented as multiple software or software modules (for example, for providing distributed services), or it may be implemented as a single software or software module, which is not specifically limited herein.

[0033] Alternatively, the system architecture may not include the server 103. In other words, the server 103 may be an optional device in the embodiments of this specification, that is, the method provided in the embodiments of this specification may be applied to a system structure that only includes the terminal 101, and the embodiments of this specification do not limit this.

[0034] It should be understood that the number of terminals, networks, and servers in FIG1 is merely illustrative, and any number of terminals, networks, and servers may be used according to implementation requirements.

[0035] Please refer to Figure 2, which is a flowchart illustrating a product strategy delivery method provided in an embodiment of this specification. The execution subject of an embodiment of this specification can be a terminal executing product strategy delivery, a processor within a terminal executing the product strategy delivery method, or a product strategy delivery service within a terminal executing the product strategy delivery method. For ease of description, the specific execution process of the product strategy delivery method will be described below using the example of a processor within a terminal as the execution subject.

[0036] As shown in FIG2 , the product strategy launch method may include at least the following steps.

[0037] S202. Based on a user behavior sequence of at least one user for a historical product strategy, determine at least one target professional factor that meets a preset popularity condition from at least one preset professional factor, where the professional factor is a factor for professionally explaining the product.

[0038] Alternatively, facing users' demand for explainable product strategies in certain professional scenarios, conventional strategy deployment solutions that rely solely on expert experience and black-box strategy deployment models are gradually failing to meet users' deeper needs for product strategies. Therefore, to help users quickly and effectively select appropriate products and complete subsequent purchases, the implicit professional factor data in product strategies can be presented to users in a more trustworthy combination of text, charts, and action points. Professional factors are factors that provide professional interpretations of products and are the data representation of the product's objective definition from the perspective of relevant industry professionals. Different categories of factors explain products from different dimensions. For example, for a financial product, fundamental factors explain fund returns from multiple perspectives such as valuation, growth, and profitability, while quantity-price factors are constructed around technical indicators such as price and trading volume. Product strategies constructed using these professional factors allow users to quickly and visually understand the product's intrinsic benefits and risks through the combined, visualized product strategies.

[0039] Alternatively, to explainably and explicitly present the implicit professional factors of high interest to users within a product strategy, thereby increasing click-through rates, browsing rates, and subsequent purchase conversion rates, we can first identify target professional factors of interest to the user group within all product professional factors. This allows us to subsequently present preferred product strategies to users based on these highly-interested target professional factors. Prior to determining the target professional factors, we must first identify the pre-defined professional factors that will be analyzed for the product in the specific strategy recommendation scenario.

[0040] Furthermore, for a product, its professional factors encompass not only the objective dimensions of the product itself but also the subjective dimensions of user preferences. These pre-defined professional factors include, but are not limited to, product professional factors and user professional factors. Product professional factors are product-specific factors defined based on fundamental professional knowledge within the product's industry, while user professional factors are user-specific factors defined based on user behavioral data. For example, in a financial context, the product professional factors of a financial product can be broken down into yield, Sharpe ratio, BRAR (popularity readiness index), CR (capacity index), VR (volume ratio), MAR (potential growth and decline index), Mace line, VCI (variability index), and MAD (Williams bullish and bearish momentum indicator). These product professional factors are professionally defined parameters within the financial industry and are used to reflect the objective aspects of the financial product itself. For users, related user professional factors, such as the user's holding income amount and the user's holding yield, are also of great interest. These are user-specific factors of a product defined based on user behavioral data.

[0041] Specifically, the preset professional factors can be obtained by collecting basic data such as the product's trading data, market data, fundamental data and financial data. Each preset professional factor has its own special calculation formula. By calculating the basic data, the specific situation of each preset professional factor of each product can be determined. For example, through the Sharpe value calculation formula of financial products, the Sharpe value of this financial product can be calculated as a high Sharpe value or a low Sharpe value. Then, when a high Sharpe value is selected as the target professional factor that the user is concerned about, products with high Sharpe values ​​can be displayed to users in the product strategy.

[0042] Optionally, when determining the target professional factors that the user group is interested in among all the professional factors of the product, expert experience selection and algorithm automatic selection can be used, wherein expert experience selection is for the operation staff to manually select the target professional factors from the preset professional factors, specifically, based on the expert's prior experience, the preset professional factors that meet the preset popularity conditions are used as the target professional factors; algorithm automatic selection is for the algorithm to calculate based on the user behavior sequence of at least one user for the pushed historical product strategy collected in advance, and determine at least one target professional factor that meets the preset popularity conditions from at least one preset professional factor. The preset popularity conditions used in the judgment process are used to measure the degree of attention, that is, the popularity, of various professional factors among the user group. If the preset popularity conditions are met, it means that the user group is more interested in the professional factor, and the product strategy related to this type of factor will be able to obtain more user behaviors such as clicks and conversions.

[0043] The user behavior sequence is a sequence consisting of user behaviors and the corresponding professional factors obtained from user behavior data. For example, for financial product strategy A, which includes professional factors 1 and 2, and user X's behavior with respect to financial product strategy A is "click," the user behavior sequence for click behavior is [Product1[Professional Factor 1, Professional Factor 2]]. User Y's behavior with respect to financial product strategy A is "purchase conversion," and the user behavior sequence for conversion behavior is [Product2[Professional Factor 3, Professional Factor 4]]. This means that the user behavior sequence can reflect the correlation between a user's historical behavior with respect to a product strategy and the professional factors in that historical product strategy. Furthermore, combined with preset popularity conditions, at least one target professional factor that meets the preset popularity conditions can be determined from the preset professional factors for use in subsequent product strategy assembly.

[0044] S204. Determine at least one candidate product strategy corresponding to each target professional factor. The candidate product strategy at least includes the target professional factor and the strategy element corresponding to the target professional factor.

[0045] Optionally, after determining the target professional factors that meet the preset popularity conditions, the corresponding candidate product strategies can be further determined based on the target professional factors. The candidate product strategies include at least the target professional factors and the strategy elements corresponding to the target professional factors. That is, at this time, the target professional factors are mainly used as the core to determine the strategy elements that can be combined with the target professional factors to form product strategies, and then assembled into candidate product strategies that can be launched according to certain assembly rules. It should be noted that the product strategies in the embodiments of this specification are complete product strategies that can be directly launched, in which each strategy element and each target professional factor has a fixed display position and display attributes.

[0046] Furthermore, in general, considering that the meanings of some professional factors may be repetitive or contradictory, in order to avoid confusion and redundancy in the content of the product strategy, the candidate product strategy may only include one target professional factor. In this case, each target professional factor corresponds to at least one candidate product strategy. When it is necessary to increase the professional factor information in the candidate product strategy, the mutual influence relationship between the professional factors can be considered in advance, and professional factor coexistence rules can be established to stipulate which professional factors can appear at the same time and which professional factors do not need to appear at the same time. In this way, two or even more target professional factors can appear in a candidate product strategy. In this case, the content richness of a product strategy can be increased, and more target professional factor information can be displayed to users through a limited strategy space, thereby improving the efficiency of users in obtaining product intrinsic factor information.

[0047] S206: Determine, from all candidate product strategies, a candidate product strategy that meets preset launch conditions as the target product strategy to be launched.

[0048] Optionally, a target product strategy is ultimately determined from all candidate product strategies to be launched. The target product strategy needs to meet preset launch conditions. The preset launch conditions can be specific conditions determined based on user preferences, current market conditions, etc., and can change with scenario requirements. When selecting a target product strategy, it can be manually selected by operations staff based on expert experience, or it can be automatically calculated and decided with the help of algorithms, neural network models, etc., so as to quickly and efficiently determine the preferred target product strategy for launch. In this way, when the popular target professional factors are determined based on the user behavior sequence as the core of the product strategy, the final target product strategy can explicitly display the inherent professional concepts of the product to the user, allowing the user to quickly obtain product information based on the product strategy, which can solve the user's urgent need for explainable product professional information.

[0049] In an embodiment of the present specification, a product strategy launch method is provided. Based on the user behavior sequence of at least one user for a historical product strategy, at least one target professional factor that meets a preset popular condition is determined from at least one preset professional factor. The professional factor is a factor that provides a professional interpretation of the product. At least one candidate product strategy corresponding to each target professional factor is determined respectively. The candidate product strategy includes at least a target professional factor and a strategy element corresponding to the target professional factor. From all candidate product strategies, the candidate product strategy that meets the preset launch condition is determined as the target product strategy to be launched. Since professional factors can provide a professional interpretation of product strategies, when the popular target professional factor determined according to the user behavior sequence is used as the core of the product strategy, the corresponding product strategy determined according to the target professional factor can explicitly display the inherent professional concept of the product to the user, allowing the user to quickly obtain product information based on the product strategy, which can solve the user's urgent need for explainable product professional information.

[0050] Please refer to FIG3 , which is a flowchart of a product strategy delivery method provided in an embodiment of this specification.

[0051] As shown in FIG3 , the product strategy launch method may include at least the following steps.

[0052] S302: Based on a user behavior sequence of at least one user for a historical product strategy, determine at least one target user behavior corresponding to at least one preset professional factor and the number of user actions for each target user behavior.

[0053] Optionally, in order to determine the target professional factors that meet the preset popularity conditions, it is first necessary to accurately measure the popularity of each preset professional factor in the user group. From the introduction of the embodiment of the above specification, it can be understood that the user behavior sequence includes the behavior performed by the user and the preset professional factor corresponding to the behavior. The more behaviors a user performs on a preset professional factor, the higher the user's attention to the preset professional factor, which means that the preset professional factor is more popular. Then the attention of each preset professional factor can be judged by the number of user actions corresponding to important target user behaviors, that is, based on the user behavior sequence of at least one user for the historical product strategy, determine at least one target user behavior corresponding to at least one preset professional factor and the number of user actions for each target user behavior. The target user behavior is an important user behavior, such as clicking, browsing, collecting, following, purchasing, adding positions, etc. The number of user actions for each target user behavior is the total number of actions taken by the user group for the target user behavior in all the collected data.

[0054] S304: Determine, from at least one preset professional factor, a preset professional factor that meets a preset popularity condition as a target professional factor based on the number of user actions corresponding to each preset professional factor.

[0055] Optionally, the greater the total number of user actions of all target user behaviors corresponding to the preset professional factor, the more popular the preset professional factor is to a certain extent. That is, based on the number of user actions corresponding to each preset professional factor, it is possible to determine from at least one preset professional factor that the preset professional factor that meets the preset popularity condition is the target professional factor.

[0056] Furthermore, considering that different target user behaviors may have different levels of importance based on the purpose of recommending a product strategy, for example, when presenting a product strategy to a user, the primary goal may be to encourage the user to perform actions that are conducive to substantial conversions, such as clicks, purchases, and increased stock holdings, while browsing discussion forums and passively browsing the interface where the strategy resides are behaviors that cannot represent the user's actual attention. To more accurately calculate the popularity of the preset professional factors, a corresponding popularity weight can be assigned to each target user behavior based on its importance. For example, important click and purchase behaviors have higher weights, while unimportant browsing behaviors have lower weights. The popularity score of each preset professional factor can then be calculated based on the number of user actions corresponding to each preset professional factor and the popularity weight of each target user behavior. Specifically, "PopularityScore_factor n" is used to represent the popularity score of the preset professional factor n, "clk_uv" represents the user click rate, and its corresponding weight is w1, "trans_uv" represents the user conversion rate, and its corresponding weight is w2, and so on. Each target user behavior has a corresponding popularity weight, so the popularity score calculation expression of the preset professional factor n is: PopularityScore_factor n = w1×clk_uv+w2×trans_uv+….

[0057] After calculating the popularity scores of each pre-set professional factor, we can then identify those pre-set professional factors whose popularity scores meet the pre-set popularity criteria as target professional factors. In practical scenarios, we can set the pre-set popularity criteria to set the top K pre-set professional factors with the top K popularity scores as target professional factors. This controls the number of target professional factors and improves the efficiency of subsequent product strategy assembly.

[0058] S306: Input each target professional factor into the policy element extraction model, and determine at least one policy element corresponding to each target professional factor based on the policy element extraction model.

[0059] Optionally, after determining the target professional factor, the relevant strategy elements are determined based on the target professional factor, so that the subsequent product strategy composed of the target professional factor and the strategy elements corresponding to the target professional factor can explicitly display the target professional factor to the user, so as to meet the user's need for explainability of the product strategy. In the background of product strategy delivery, a large number of backup strategy elements will be prepared in advance, including at least text, images, charts, and action points, such as the text "Holding firmly is the easiest way to avoid actual losses..."; the image chart can be a product-related line chart or data chart; the action point is used to reflect the user behavior expected by the strategy. Then, when generating the strategy elements corresponding to the target professional factor, they can be extracted from the pre-prepared element library. The specific extraction process can use a strategy element extraction model. The strategy element extraction model is a trained and converged neural network model. Based on the strategy element extraction model, at least one strategy element corresponding to each target professional factor can be determined separately.

[0060] Optionally, the policy element extraction model f(text, image, chart, action) is trained based on at least one sample policy element and the standard professional factor label corresponding to each sample policy element. Specifically, when training the policy element extraction model, the sample policy elements are used as input, and each sample policy element is labeled with a standard professional factor label. The policy element extraction model outputs a predicted professional factor label for each sample policy element based on the sample policy elements. The model loss is calculated based on the standard professional factor label and the predicted professional factor label for each sample policy element. This model loss is used to train and adjust the parameters of the policy element extraction model, allowing the policy element extraction model to learn the correspondence between policy elements and professional factors. After the policy element extraction model converges, it receives the target professional factor as input and can extract at least one policy element corresponding to each target professional factor based on its own learned knowledge.

[0061] It should be noted that in actual scenarios, some text information, graphic resources, etc. with higher sensitivity may involve issues such as copyright and usage rights. After being extracted, these policy elements can undergo another layer of legal and applicable scenario review to avoid risks in some actual scenarios. The review rules are usually set based on expert experience, and the specific rule content needs to be set according to the real-time needs of the actual scenario. The embodiments of this specification do not limit this.

[0062] S308. Determine at least one candidate product strategy corresponding to each target professional factor based on each target professional factor and the strategy elements corresponding to each target professional factor.

[0063] Optionally, after determining the strategy elements corresponding to each target professional factor, it is necessary to assemble each target professional factor and the strategy elements into a product strategy, that is, to determine at least one candidate product strategy corresponding to each target professional factor based on each target professional factor and the strategy elements corresponding to each target professional factor. When assembling strategy elements, it is necessary to consider that the color attributes and style attributes of the elements may cause conflicts when some elements appear at the same time. For example, in a product strategy "Bad case" with a poor display effect, the combination of light gray background and white text makes the text content very unclear, while in a product strategy "Good case" with a better display effect, the combination of light gray background and black text makes the visual display effect of the product strategy better.

[0064] To assemble a product strategy with good display effects, please refer to FIG4 , which is a tree structure diagram of a strategy element assembly rule provided in an embodiment of this specification. As shown in FIG4 , the attribute type of each strategy element can be treated as a large node according to the attributes of the strategy element. For example, the strategy template attribute is treated as a large node A, the text font attribute is treated as a large node B, the text color attribute is treated as a large node C, and the background color attribute is treated as a large node D. Under the large node B of the text font attribute, there are five fonts, namely b1, b2, b3, b4, and b5; under the large node C of the text color attribute, there are three colors, namely c1, c2, and c3; under the large node D of the background color attribute, there are two background styles, namely d1 and d2. At this time, the connecting lines between each font, text color, and background style represent a parameter attribute combination of an element in the product strategy. There are a total of 5×3×2=30 possible permutations and combinations of product strategies. In the tree structure, "Good case" can be used as the preferred path, thereby solving the strategy element assembly problem.

[0065] Furthermore, the above tree structure can obtain element combination rules by learning positive sample product strategy cases (sample "Good case") and negative sample product strategy cases (sample "Bad case"), where these sample product strategy cases can be cases manually adjusted in the early strategy assembly process in actual applications. Finally, based on these case tree structures, visual prior experience is learned to narrow the search space for each parameter element combination, so that the element combination rules are applied when searching for the attribute combination path of the strategy elements. Based on the element combination rules, each target professional factor and the strategy elements corresponding to each target professional factor are combined respectively to determine at least one candidate product strategy corresponding to each target professional factor that meets the element combination rules. In this way, the candidate product strategy corresponding to each target professional factor is a candidate product strategy that is relevant to each target professional factor and shows good results.

[0066] Optionally, the tree structure can determine the combination of policy elements corresponding to each target professional factor. After that, it is necessary to specifically place these policy elements and each target professional factor in the pre-selected policy specifications determined in advance to complete the assembly of a complete candidate product strategy. The pre-selected policy specifications are product policy templates that need to be launched that are manually determined by the operation personnel in the product policy template library. The selection of pre-selected policy specifications is usually related to user needs, product information, and page functions. For example, please refer to Figure 5. Figure 5 is a schematic diagram of the display interface of a pre-selected policy specification provided in an embodiment of this specification. As shown in Figure 5, there is a product policy template library open to the operation staff in the terminal display interface. The operation staff can select L pre-selected policy specifications as needed as the target template for generating the product strategy. According to these pre-selected policy specifications, each target professional factor and the policy elements corresponding to each target professional factor are assembled by Cartesian product, thereby determining all candidate product strategies that can be used for launch generated by this product strategy.

[0067] Optionally, please refer to Figure 6, which is a logical flow chart of a product strategy launch method provided in an embodiment of this specification. As shown in Figure 6, when a product strategy needs to be launched, the starting process of the product strategy launch method is entered. First, the preset professional factors are determined based on basic data such as transaction data, market data, fundamental data, and financial data in the application scenario, and the product strategy template library is preset in advance; when generating the product strategy, the target professional factors are determined from the preset professional factors, and the pre-selected strategy specifications are selected from the product strategy template library; the strategy elements corresponding to each target professional factor are generated; and then, combined with the pre-selected strategy specifications, all candidate product strategies related to the target professional factors are assembled using Cartesian products.

[0068] S3010: Input each candidate product strategy into a strategy launch model, and determine, based on the strategy launch model, a candidate product strategy that meets preset launch conditions as the target product strategy to be launched.

[0069] Optionally, among all candidate product strategies, user behavior prediction can be further performed on each candidate product strategy to predict the user behavior that users will make when facing these candidate product strategies. Based on the user behavior prediction results, the candidate product strategy that meets the preset delivery conditions is selected as the target product strategy to be launched. The preset delivery conditions are specifically set according to the expected user behavior, and the launch value of each candidate product strategy can be measured.

[0070] Specifically, when determining the target product strategy, a strategy delivery model can be used to input each candidate product strategy into the strategy delivery model, and user behavior prediction can be performed based on the strategy delivery model to determine the candidate product strategy that meets the preset delivery conditions as the target product strategy to be delivered. Generally, there are multiple possibilities for user behavior with respect to a product strategy. For example, in the financial fund scenario, by analyzing the user's fund subscription behavior, after the user clicks, there are usually multiple behaviors that are highly correlated with delayed purchase behavior, such as self-selection, fixed investment, and subscription. Delayed purchase refers to the situation in which a purchase conversion occurs some time after the user behavior occurs, which is often the case in financial scenarios. Then, the PostClick behavior indicator can be divided into decisive behavior (DAction) and other behavior (OAction) according to whether it is related to delayed purchase behavior. Decisive behavior includes: self-selection behavior, fixed investment behavior, and subscription behavior, etc. Other behaviors include: discussion area browsing behavior, toolbar browsing behavior, and consultation behavior, etc. It can be understood that decisive behavior and other behaviors are divided according to the probability of association with delayed purchase conversion behavior.

[0071] Further, please refer to Figure 7, which is a behavioral link modeling in a strategic delivery model provided by an embodiment of this specification. As shown in Figure 7, in the strategic delivery model, user behavior can be established between clicks and delayed purchases, forming a behavioral link modeling of "delivery exposure → click → D(O)Action → delayed purchase → delayed GMV (total transaction amount)". On this basis, based on this behavioral link modeling, the strategic delivery model can be based on full-space multi-target modeling, making full use of the behavioral link of "delivery exposure → click → D(O)Action → delayed purchase → delayed GMV (total transaction amount)", and decomposing it into multiple target behavioral links, specifically "exposure → click", "click → DAction", "DAction → delayed purchase", "OAction → delayed purchase", "delayed purchase → delayed GMV", and each target behavior link corresponds to a prediction subnetwork.

[0072] Optionally, please refer to FIG8 , which is a model structure diagram of a strategic delivery model provided in an embodiment of this specification. As shown in Figure 8, the initial input of the model is the original one-hot encoded feature input. In the shared module, the one-hot feature encoding is embedded into a feature representation. The main structure of the strategic delivery model includes the following three modules: (1) Shared Embedding Module: SEM represents the embedding feature representation of sparse features shared by all prediction sub-networks. For example, the embedding feature representation of features such as user ID, habit preferences, and personality characteristics is shared by all prediction sub-networks. This can alleviate the data sparsity problem faced by a single behavior link to a certain extent; (2) Decomposed Prediction Module: DPM is composed of the above five prediction sub-networks. Each prediction sub-network estimates the user behavior estimated values ​​of the five target behavior links: "exposure→click", "click→DAction", "DAction→delayed purchase", "OAction→delayed purchase", and "delayed purchase→delayed GMV"; (3) Sequential Composition Module: SCM finally integrates four expected prediction values ​​based on the user behavior estimated values ​​of each prediction sub-network. They are:

[0073] The click-through rate of product strategy i in the "exposure→click" link This is determined by the "exposure→click" prediction subnetwork in the model, and its specific value is represented by Y1;

[0074] The decisive behavior rate of product strategy i in the "exposure→DAction" link Decisive behavior rate In the model, the estimated value Y1 of the "exposure→click" prediction sub-network and the estimated value Y2 of the "click→DAction" prediction sub-network are jointly determined, and the calculation formula is expressed as Y1Y2;

[0075] The transaction rate of product strategy i in the "exposure→delayed purchase" link Transaction rate In the model, the estimated value Y1 of the "exposure→click" prediction sub-network, the estimated value Y2 of the "click→DAction" prediction sub-network, the estimated value Y3 of the "DAction→delayed purchase" prediction sub-network, and the estimated value Y4 of the "OAction→delayed purchase" prediction sub-network are jointly determined. The calculation formula is: Y1[(1-Y2)Y4+Y2Y3];

[0076] And the predicted total transaction amount of product strategy i in "Exposure → Delayed GMV" It needs to be determined by the estimated values ​​Y1, Y2, Y3, Y4, and Y5 of the five prediction sub-networks, and the calculation formula is: Y1[(1-Y2)Y4+Y2Y3]Y5.

[0077] Optionally, when training the strategy delivery model, the prediction loss is calculated based on the predicted value output for at least one sample product strategy and the standard value corresponding to each sample product strategy. More specifically, taking the sample product strategy as product strategy i as an example, the click rate of sample product strategy i is Combined with the corresponding real click rate, we get sub-loss Loss1 and decisive behavior rate. Combined with the corresponding true decisive behavior rate, we can get the sub-loss Loss2 and the transaction rate. Combined with the corresponding actual transaction rate, we can get the sub-loss Loss3 and the total transaction amount prediction value. Combined with the corresponding actual transaction amount, the sub-loss Loss4 is obtained. Then, according to the loss weights corresponding to the four preset sub-losses, the final model's predicted Loss is calculated. 总 , using Loss 总 The model is trained iteratively once, and the strategic launch model that converges after multiple training sessions can be used in real-world scenarios to make product strategy launch decisions.

[0078] Refer to Figure 6. After determining the optimal target product strategy, the target product strategy is launched, completing the entire product strategy launch process. Furthermore, after the target product strategy launch, it can be monitored to collect user behavior feedback data. This collected user data can be used to iterate the algorithms and models involved in the product strategy launch plan, thereby continuously providing users with better product strategy plans and improving user experience.

[0079] In an embodiment of the present specification, a product strategy delivery method is provided. First, different popularity weights are assigned to target user behaviors of different importance, and the popularity score of the preset professional factor is calculated according to the user behavior sequence. The popularity of the preset professional factor in the user group is analyzed based on the popularity score, so as to accurately determine the important target professional factor; further, a strategy element extraction model is used to quickly and accurately extract the strategy elements related to the target professional factor, and a tree structure is used to restrict the combination of strategy elements, and finally a candidate product strategy with high correlation to the target professional factor and a preferred display effect is obtained, thereby realizing dynamic optimization selection of strategy element parameters; finally, a strategy delivery model is used to select the target product strategy to be delivered. The strategy delivery model is based on multiple user behavior link modeling and can simultaneously consider multiple target behavior links in the target scenario, and then output an accurate product strategy delivery decision after training convergence. Based on the above product strategy delivery scheme, when the popular target professional factor determined according to the user behavior sequence is used as the core of the product strategy, the corresponding product strategy determined according to the target professional factor can explicitly display the inherent professional concept of the product to the user, so that the user can quickly obtain product information according to the product strategy, which can solve the user's urgent need for explainable product professional information.

[0080] Please refer to Figure 9, which is a block diagram of a product strategy delivery device provided in an embodiment of this specification. As shown in Figure 9, the product strategy delivery device 900 includes: a professional factor selection module 910, which is used to determine at least one target professional factor that meets the preset popularity condition from at least one preset professional factor based on the user behavior sequence of at least one user for the historical product strategy, and the professional factor is a factor that professionally interprets the product; a candidate strategy determination module 920, which is used to respectively determine at least one candidate product strategy corresponding to each target professional factor, and the candidate product strategy includes at least the target professional factor and the strategy element corresponding to the target professional factor; a product strategy delivery module 930, which is used to determine, from all candidate product strategies, the candidate product strategy that meets the preset delivery condition as the target product strategy to be delivered.

[0081] Optionally, the professional factor selection module 910 is also used to determine at least one target user behavior corresponding to at least one preset professional factor and the number of user actions for each target user behavior based on the user behavior sequence of at least one user for the historical product strategy; and determine the preset professional factor that meets the preset popularity condition from at least one preset professional factor as the target professional factor according to the number of user actions corresponding to each preset professional factor.

[0082] Optionally, the professional factor selection module 910 is also used to calculate the popularity score of each preset professional factor based on the number of user actions corresponding to each preset professional factor and the popularity weight of each target user behavior, wherein the popularity weight is set according to the importance of the corresponding target user behavior; and determine the preset professional factor whose popularity score meets the preset popularity condition as the target professional factor.

[0083] Optionally, the candidate strategy determination module 920 is also used to input each target professional factor into a strategy element extraction model, and determine at least one strategy element corresponding to each target professional factor based on the strategy element extraction model; and determine at least one candidate product strategy corresponding to each target professional factor based on each target professional factor and the strategy elements corresponding to each target professional factor.

[0084] Optionally, the type of the policy element includes at least one of text, image, chart, and action point; the policy element extraction model is trained based on at least one sample policy element and a standard professional factor label corresponding to each sample policy element.

[0085] Optionally, the candidate strategy determination module 920 is also used to combine each target professional factor and the strategy elements corresponding to each target professional factor based on the element combination rules, and determine at least one candidate product strategy corresponding to each target professional factor that meets the element combination rules. The element combination rules are obtained by learning positive sample product strategy cases and negative sample product strategy cases.

[0086] Optionally, the candidate strategy determination module 920 is further configured to perform Cartesian product assembly on each target professional factor and the strategy elements corresponding to each target professional factor according to the preselected strategy specifications.

[0087] Optionally, the candidate strategy determination module 920 is further used to input each candidate product strategy into a strategy delivery model, and determine, based on the strategy delivery model, a candidate product strategy that meets preset delivery conditions as the target product strategy to be delivered; wherein, the strategy delivery model includes a prediction subnetwork of at least one target behavior link, the prediction subnetwork is used to predict the probability of a user group realizing a corresponding target behavior link under a given product strategy, and an embedded feature representation of sparse features shared by each prediction subnetwork.

[0088] Optionally, the strategy delivery model is trained based on a predicted value outputted for at least one sample product strategy and a predicted loss calculated from a standard value corresponding to each sample product strategy.

[0089] Optionally, the preset professional factors include but are not limited to product professional factors and user professional factors; wherein, the product professional factors are professional factors of the product dimension defined based on basic professional knowledge in the product industry field, and the user professional factors are professional factors of the user dimension defined based on user behavior data.

[0090] In an embodiment of the present specification, a product strategy delivery device is provided, wherein a professional factor selection module is used to determine at least one target professional factor that meets a preset popularity condition from at least one preset professional factor based on a user behavior sequence of at least one user with respect to a historical product strategy, wherein the professional factor is a factor that provides a professional interpretation of the product; a candidate strategy determination module is used to respectively determine at least one candidate product strategy corresponding to each target professional factor, wherein the candidate product strategy includes at least a target professional factor and a strategy element corresponding to the target professional factor; and a product strategy delivery module is used to determine, from all candidate product strategies, a candidate product strategy that meets the preset delivery condition as the target product strategy to be delivered. Since professional factors can provide a professional interpretation of product strategies, when the popular target professional factor determined according to the user behavior sequence is used as the core of the product strategy, the corresponding product strategy determined according to the target professional factor can explicitly display the inherent professional concept of the product to the user, allowing the user to quickly obtain product information based on the product strategy, thereby solving the user's urgent need for explainable product professional information.

[0091] The embodiments of this specification provide a computer program product including instructions. When the computer program product is run on a computer or a processor, the computer or the processor is caused to perform the steps of any one of the methods in the above embodiments.

[0092] The embodiments of this specification also provide a computer storage medium, which can store multiple instructions, and the instructions are suitable for being loaded by a processor and executing the steps of any method in the above embodiments.

[0093] Please refer to Figure 10, which is a schematic diagram of the structure of a terminal provided in an embodiment of this specification. As shown in Figure 10, the terminal 1000 may include: at least one terminal processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0094] The communication bus 1002 is used to implement the connection and communication between these components.

[0095] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0096] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0097] The terminal processor 1001 may include one or more processing cores. The terminal processor 1001 utilizes various interfaces and circuits to connect various components within the terminal 1000. It executes instructions, programs, code sets, or instruction sets stored in the memory 1005, and accesses data stored in the memory 1005 to perform various functions and process data for the terminal 1000. Optionally, the terminal processor 1001 may be implemented using at least one hardware form factor selected from the group consisting of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The terminal processor 1001 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the terminal processor 1001 and may be implemented as a separate chip.

[0098] The memory 1005 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may also optionally be at least one storage device located away from the aforementioned terminal processor 1001. As shown in Figure 10, the memory 1005, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a product strategy delivery program.

[0099] In the terminal 1000 shown in Figure 10, the user interface 1003 is mainly used to provide an input interface for the user and obtain the data input by the user; and the terminal processor 1001 can be used to call the product strategy delivery program stored in the memory 1005, and specifically perform the following operations: based on the user behavior sequence of at least one user for the historical product strategy, determine at least one target professional factor that meets the preset popularity condition from at least one preset professional factor, and the professional factor is a factor for professional interpretation of the product; determine at least one candidate product strategy corresponding to each target professional factor, and the candidate product strategy includes at least the target professional factor and the strategy element corresponding to the target professional factor; from all candidate product strategies, determine the candidate product strategy that meets the preset delivery condition as the target product strategy to be delivered.

[0100] In some embodiments, when the terminal processor 1001 executes a user behavior sequence based on at least one user for a historical product strategy and determines at least one target professional factor that meets a preset popularity condition from at least one preset professional factor, it specifically performs the following steps: based on the user behavior sequence of at least one user for a historical product strategy, determine at least one target user behavior corresponding to at least one preset professional factor and the number of user actions for each target user behavior; and determine, from at least one preset professional factor, a preset professional factor that meets the preset popularity condition as a target professional factor based on the number of user actions corresponding to each preset professional factor.

[0101] In some embodiments, when the terminal processor 1001 determines a preset professional factor that meets a preset popularity condition from at least one preset professional factor as a target professional factor based on the number of user actions corresponding to each preset professional factor, the terminal processor 1001 specifically performs the following steps: calculating the popularity score of each preset professional factor based on the number of user actions corresponding to each preset professional factor and the popularity weight of each target user behavior, wherein the popularity weight is set according to the importance of the corresponding target user behavior; determining the preset professional factor whose popularity score meets the preset popularity condition as the target professional factor.

[0102] In some embodiments, when the terminal processor 1001 determines at least one candidate product strategy corresponding to each target professional factor, it specifically performs the following steps: input each target professional factor into the policy element extraction model, and determine at least one policy element corresponding to each target professional factor based on the policy element extraction model; determine at least one candidate product strategy corresponding to each target professional factor based on each target professional factor and the policy element corresponding to each target professional factor.

[0103] In some embodiments, the type of policy elements includes at least one of text, image, chart, and action point; the policy element extraction model is trained based on at least one sample policy element and the standard professional factor labels corresponding to each sample policy element.

[0104] In some embodiments, when the terminal processor 1001 determines at least one candidate product strategy corresponding to each target professional factor based on each target professional factor and the policy elements corresponding to each target professional factor, it specifically performs the following steps: based on the element combination rule, each target professional factor and the policy elements corresponding to each target professional factor are respectively combined to determine at least one candidate product strategy corresponding to each target professional factor that meets the element combination rule, and the element combination rule is obtained by learning positive sample product strategy cases and negative sample product strategy cases.

[0105] In some embodiments, when the terminal processor 1001 executes the combination of each target professional factor and the policy elements corresponding to each target professional factor, it specifically performs the following steps: according to the preselected policy specifications, Cartesian product assembly is performed on each target professional factor and the policy elements corresponding to each target professional factor.

[0106] In some embodiments, when the terminal processor 1001 determines, from all candidate product strategies, a candidate product strategy that meets preset delivery conditions as the target product strategy to be launched, it specifically performs the following steps: inputting each candidate product strategy into a strategy launch model, and determining, based on the strategy launch model, a candidate product strategy that meets preset delivery conditions as the target product strategy to be launched; wherein, the strategy launch model includes a prediction subnetwork of at least one target behavior link, the prediction subnetwork is used to predict the probability of a user group realizing a corresponding target behavior link under a given product strategy, and an embedded feature representation of sparse features shared by each prediction subnetwork.

[0107] In some embodiments, the strategy delivery model is trained based on a predicted value outputted for at least one sample product strategy and a predicted loss calculated from a standard value corresponding to each sample product strategy.

[0108] In some embodiments, the preset professional factors include but are not limited to product professional factors and user professional factors; wherein, the product professional factors are professional factors of the product dimension defined based on basic professional knowledge in the product industry field, and the user professional factors are professional factors of the user dimension defined based on user behavior data.

[0109] In the several embodiments provided in this specification, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or modules, and can be electrical, mechanical or other forms.

[0110] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of these modules may be selected to achieve the purpose of this embodiment based on actual needs.

[0111] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The above-mentioned computer program product includes one or more computer instructions. When the above-mentioned computer program instructions are loaded and executed on a computer, the above-mentioned process or function according to the embodiment of this specification is generated in whole or in part. The above-mentioned computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The above-mentioned computer instructions can be stored in a computer-readable storage medium or transmitted by the above-mentioned computer-readable storage medium. The above-mentioned computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The above-mentioned computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available media may be magnetic media (eg, floppy disks, hard disks, tapes), optical media (eg, digital versatile discs (DVDs)), or semiconductor media (eg, solid state disks (SSDs)).

[0112] It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0113] In addition, it should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in the embodiments of this specification are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the user behavior sequence and number of user actions involved in this specification are all obtained with full authorization.

[0114] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0115] The above is a description of a product strategy delivery method, device, storage medium, and terminal provided in the embodiments of this specification. For those skilled in the art, based on the ideas of the embodiments of this specification, there may be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the embodiments of this specification.

Claims

1. A product strategy delivery method, the method comprising: Based on the user behavior sequence of at least one user for the historical product strategy, at least one target professional factor satisfying a preset popularity condition is determined from at least one preset professional factor, wherein the professional factor is a factor for professionally explaining the product; Determine at least one candidate product strategy corresponding to each target professional factor respectively, wherein the candidate product strategy includes at least the target professional factor and the strategy element corresponding to the target professional factor; From all candidate product strategies, determine the candidate product strategy that meets the preset launch conditions as the target product strategy to be launched.

2. The method according to claim 1, wherein the determining, based on the user behavior sequence of at least one user for the historical product strategy, at least one target professional factor satisfying a preset popularity condition from at least one preset professional factor comprises: Based on a user behavior sequence of at least one user for a historical product strategy, determining at least one target user behavior corresponding to at least one preset professional factor and the number of user actions for each target user behavior; According to the number of user actions corresponding to each preset professional factor, a preset professional factor satisfying a preset popularity condition is determined from at least one preset professional factor as a target professional factor.

3. The method according to claim 2, wherein the step of determining a preset professional factor that satisfies a preset popularity condition from at least one preset professional factor as a target professional factor according to the number of user actions corresponding to each preset professional factor, comprises: Calculate the popularity score of each preset professional factor according to the number of user actions corresponding to each preset professional factor and the popularity weight of each target user behavior, wherein the popularity weight is set according to the importance of the corresponding target user behavior; The preset professional factor whose popularity score meets the preset popularity condition is determined as the target professional factor.

4. The method according to claim 1, wherein the step of determining at least one candidate product strategy corresponding to each target professional factor comprises: Inputting each target professional factor into a policy element extraction model, and determining at least one policy element corresponding to each target professional factor based on the policy element extraction model; At least one candidate product strategy corresponding to each target professional factor is determined based on each target professional factor and the strategy elements corresponding to each target professional factor.

5. The method according to claim 4, wherein the type of the policy element comprises at least one of text, image, chart, and action point; The policy element extraction model is trained based on at least one sample policy element and standard professional factor labels corresponding to each sample policy element.

6. The method according to claim 4, wherein determining at least one candidate product strategy corresponding to each target professional factor based on each target professional factor and the strategy element corresponding to each target professional factor comprises: Based on the element combination rules, each target professional factor and the strategy elements corresponding to each target professional factor are combined respectively. At least one candidate product strategy corresponding to each target professional factor and satisfying the element combination rule is determined, and the element combination rule is obtained by learning positive sample product strategy cases and negative sample product strategy cases.

7. The method according to claim 6, wherein the step of respectively combining each target professional factor and the strategy element corresponding to each target professional factor comprises: According to the pre-selected strategy specifications, Cartesian product assembly is performed on each target professional factor and the strategy elements corresponding to each target professional factor.

8. The method according to claim 1, wherein determining, from all candidate product strategies, a candidate product strategy that meets a preset launch condition as a target product strategy to be launched comprises: Input each candidate product strategy into the strategy delivery model, and determine the candidate product strategy that meets the preset delivery conditions as the target product strategy to be delivered based on the strategy delivery model; Among them, the strategy delivery model includes at least one prediction subnetwork of a target behavior link, and the prediction subnetwork is used to predict the probability of a user group realizing a corresponding target behavior link under a given product strategy, as well as an embedded feature representation of sparse features shared by each prediction subnetwork.

9. According to the method of claim 8, the strategy delivery model is trained based on the predicted value output for at least one sample product strategy and the predicted loss calculated based on the standard value corresponding to each sample product strategy.

10. The method according to claim 1, wherein the preset professional factors include but are not limited to product professional factors and user professional factors; in, The product professional factor is a professional factor of the product dimension defined based on basic professional knowledge in the product industry field, and the user professional factor is a professional factor of the user dimension defined based on user behavior data.

11. A product strategy delivery device, comprising: A professional factor selection module, for determining at least one target professional factor that meets a preset popularity condition from at least one preset professional factor based on a user behavior sequence of at least one user for a historical product strategy, wherein the professional factor is a factor for professionally explaining a product; A candidate strategy determination module, used to determine at least one candidate product strategy corresponding to each target professional factor, wherein the candidate product strategy at least includes the target professional factor and a strategy element corresponding to the target professional factor; The product strategy delivery module is used to determine, from all candidate product strategies, a candidate product strategy that meets preset delivery conditions as the target product strategy to be delivered.

12. A computer program product comprising instructions, which, when executed on a computer or a processor, causes the computer or the processor to execute the steps of the method according to any one of claims 1 to 10.

13. A computer storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the steps of the method according to any one of claims 1 to 10.

14. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 10 when executing the program.

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