Commodity promotion object determination method, computing platform and computer storage medium

Abstract

The embodiment of the invention provides a commodity promotion object determination method, a computing platform and a computer storage medium. The determination method comprises the steps of obtaining object information corresponding to at least one commodity object in a preset shop; based on the object information corresponding to the at least one commodity object, a recommended traffic list matched with a preset shop is determined, and the recommended traffic list comprises multiple traffic keywords matched with the preset shop and sorting information of the multiple traffic keywords; and determining a promoted commodity object in the at least one commodity object based on the recommended flow list. In the embodiment of the invention, the promotion commodity object is determined based on the recommendation flow list matched with the preset shop, and the promotion commodity objects determined by different preset shops can be different, so that the determination operation of the personalized promotion commodity object in the shop dimension is realized to a certain extent; and then popularization operation is performed based on the commodity popularization object, so that the quality and effect of the popularization operation can be ensured.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular, to a method for determining a promoted commodity object, a computing platform, and a computer storage medium. Background Art

[0002] With the rapid development of e-commerce technology, the application of e-commerce platforms has become more and more extensive, so that more and more merchants have settled in e-commerce platforms. For each merchant, effective promotion operations of commodity objects can not only attract new customers, but also enhance the purchase intention of existing customers. Therefore, the promotion operation of commodity objects is an important means to improve the transaction rate of commodity objects and maintain a loyal customer group.

[0003] Currently, merchants often only perform advertising promotion operations on newly launched commodity objects and main promoted commodity objects in the merchant's store. However, since the commodity objects participating in the promotion operation of commodity objects are relatively fixed and cannot adapt to the changing market demands, the quality and efficiency of the promotion operation of commodity objects are reduced. Summary of the Invention

[0004] Multiple aspects of the present application provide a method for determining a promoted commodity object, a computing platform, and a computer storage medium, which can improve the quality and efficiency of the promotion operation of commodity objects to a certain extent.

[0005] An embodiment of the present invention provides a method for determining a promoted commodity object, including:

[0006] Obtaining object information corresponding to at least one commodity object in a preset store;

[0007] Based on the object information corresponding to the at least one commodity object, determining a recommended traffic list that matches the preset store, where the recommended traffic list includes a plurality of traffic keywords adapted to the preset store and sorting information of the plurality of traffic keywords;

[0008] Based on the recommended traffic list, determining a promoted commodity object among the at least one commodity object.

[0009] An embodiment of the present invention provides a device for determining a promoted commodity object, including:

[0010] A first obtaining module, configured to obtain object information corresponding to at least one commodity object in a preset store;

[0011] A first determining module, configured to determine a recommended traffic list based on the object information corresponding to the at least one commodity object, where the recommended traffic list includes a plurality of traffic keywords adapted to the preset store and sorting information of the plurality of traffic keywords;

[0012] The first processing module is used to determine a promoted product object among the at least one product object based on the recommended traffic list.

[0013] An embodiment of the present invention provides a computing platform, including: a memory and a processor; wherein, the memory is used to store one or more computer instructions, and when the one or more computer instructions are executed by the processor, the method for determining a promoted product object in the first aspect above is implemented.

[0014] An embodiment of the present invention provides a computer storage medium for storing a computer program, and when the computer program is executed by a computer, the method for determining a promoted product object in the first aspect above is implemented.

[0015] An embodiment of the present invention provides a computer program product, including: a computer-readable storage medium storing computer instructions, and when the computer instructions are executed by one or more processors, the steps in the method for determining a promoted product object in the first aspect above are caused to be executed by the one or more processors.

[0016] The method, device, computing platform and computer storage medium for determining a promoted product object provided in this embodiment determine a recommended traffic list matching a preset store through the object information corresponding to each of at least one product object in the preset store, and then can determine a promoted product object among the at least one product object based on the recommended traffic list. Since the promoted product object is determined based on the recommended traffic list adapted to the preset store, and the recommended traffic lists corresponding to different preset stores can be different, and the promoted product objects determined by different preset stores and recommended traffic lists can be different, to a certain extent, the operation of determining a personalized promoted product object in the store dimension is realized, that is, the promoted product object can be flexibly adjusted as the recommended traffic list changes, and the determined promoted product object can capture or attract the hot traffic information corresponding to each traffic keyword in the recommended traffic list. In this way, when promoting based on the promoted product object, the quality and effect of the product object promotion operation can be improved to a certain extent, and thus the practicability of this method is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application, form a part of the present application, and the illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0018] Figure 1 It is a schematic diagram of a scenario of a method for determining a promoted product object provided by an exemplary embodiment of the present application;

[0019] Figure 2Flow chart of a method for determining a promoted product object provided by an exemplary embodiment of the present application;

[0020] Figure 3 Flow chart of a method for determining a recommended traffic list matching a preset store based on object information corresponding to each of the at least one product object provided by an exemplary embodiment of the present application;

[0021] Figure 4 Flow chart of another method for determining a promoted product object provided by an exemplary embodiment of the present application;

[0022] Figure 5 Flow chart of yet another method for determining a promoted product object provided by an exemplary embodiment of the present application;

[0023] Figure 6 Schematic diagram of a hot marketing system for merchants provided by an exemplary application embodiment of the present application;

[0024] Figure 7 Schematic diagram of the structure of a device for determining a promoted product object provided by an exemplary embodiment of the present application;

[0025] Figure 8 Schematic diagram of the structure of a computing platform provided by an exemplary embodiment of the present application. Detailed implementation manners

[0026] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0027] It should be noted that in the case where the present application embodiments involve user information, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application embodiments are all information and data that have been authorized by the user or fully authorized by all parties. Moreover, the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards of relevant countries and regions, and corresponding operation entrances are provided for users to choose to authorize or reject. Additionally, various models involved in the present application (including but not limited to language models or large models) comply with relevant laws and standards.

[0028] In addition, it should be noted that in the case where the embodiments of the present application involve user interaction operations or trigger operations, the user interaction operations or trigger operations involved in the embodiments of the present application include, but are not limited to: interaction operations in various ways such as touch operations, gesture operations, voice operations, head movement operations, eye movement operations, etc.; among them, touch operations include, but are not limited to: click operations, double-click operations, long-press operations, slide operations, pinch operations, or mouse hover operations, etc. Slide operations include, but are not limited to: linear slides, curved slides, etc.

[0029] In order to solve the problem of "the commodity object corresponding to the commodity object promotion operation in the current promotion technology is relatively fixed, thus unable to adapt to the changing market demands and reducing the quality and efficiency of the commodity object promotion operation", this embodiment provides a method, device, computing platform and computer storage medium for determining a promoted commodity object. Specifically, referring to the attached Figure 1 As shown, the execution subject of the method for determining a promoted commodity object can be the determining device 200 for a promoted commodity object. The determining device 200 for a promoted commodity object can be implemented as a local server, a cloud server, or a preset device. Among them, when the determining device 200 for a promoted commodity object is implemented as a cloud server, the method for determining a promoted commodity object can be executed in the cloud. A number of computing nodes (cloud servers) can be deployed in the cloud, and each computing node has processing resources such as computing and storage. In the cloud, a certain service can be organized by multiple computing nodes to provide. Of course, a single computing node can also provide one or more services. The way for the cloud to provide this service can be to provide a service interface externally, and a user calls this service interface to use the corresponding service. The service interface includes forms such as a Software Development Kit (SDK) and an Application Programming Interface (API).

[0030] The determining device 200 for the promoted product object is communicatively connected to the client 100. Among them, the client 100 is used for users to perform applications so as to trigger the confirmation operation of the promoted product object. The above-mentioned client 100 can be any computing device with certain information interaction capabilities. Specifically, in implementation, the client 100 can be a mobile phone, a personal computer (PC), a tablet computer, a set application program, etc. In addition, the basic structure of the client 100 may include: at least one processor. The number of processors depends on the configuration and type of the client. The client 100 may also include a memory, which may be volatile, for example: Random Access Memory (RAM), or may be non-volatile, for example: Read-Only Memory (ROM), flash memory, etc., or may also include both types at the same time. The memory usually stores an operating system (OS), one or more application programs, and may also store program data, etc. In addition to the processing unit and the memory, the client 100 also includes some basic configurations, such as: a network card chip, an IO bus, a display component, and some peripheral devices, etc. Optionally, some peripheral devices may include, for example: a keyboard, a mouse, a stylus, a printer, etc. Other peripheral devices are well known in the art and will not be elaborated here.

[0031] The determining device 200 for the promoted product object refers to a device that can implement the determination operation of the promoted product object in a network virtual environment, usually referring to a device that uses the network for information planning and the determination operation of the promoted product object. Physically, the determining device 200 for the promoted product object can be any device that can provide computing services and can perform the corresponding determination operation of the promoted product object, such as: it can be a cluster server, a conventional server, a cloud server, a cloud host, a virtual center, etc. The composition of the determining device 200 for the promoted product object mainly includes a processor, a hard disk, a memory, a system bus, etc., which is similar to a general computer architecture.

[0032] In the above-mentioned embodiment of the present invention, the client 100 is network-connected to the determining device 200 for the promoted product object, and this network connection can be a wireless or wired network connection. If the client 100 can be communicatively connected to the determining device 200 for the promoted product object, the network mode of this mobile network can be any one of 2G (Global System for Mobile Communications GSM), 2.5G (General Packet Radio Service GPRS), 3G (Wideband Code Division Multiple Access WCDMA, Time Division-Synchronous Code Division Multiple Access TD-SCDMA), 4G (Long Term Evolution LTE), 4G+ (Enhanced Long Term Evolution LTE+), Worldwide Interoperability for Microwave Access WiMax, 5G, 6G, etc.

[0033] In the embodiment of the present application, the client 100 is for users to use to generate a determination request for a promoted product object. The determination request for the promoted product object can be generated through human-computer interaction operations or voice interaction operations. In order to implement the determination operation of the promoted product object, the determination request for the promoted product object can be sent to the determination device 200 of the promoted product object, so that the determination device 200 of the promoted product object can perform corresponding determination operations on the promoted product object based on the determination request for the promoted product object.

[0034] The determination device 200 of the promoted product object is used to obtain the determination request for the promoted product object through the client 100, and then can obtain the object information corresponding to each of at least one product object in a preset store based on the determination request for the promoted product object. The object information may include at least one of the following: product object name, product object category, product object image, historical interaction data of the product object, etc. After obtaining the object information corresponding to each of at least one product object, a recommended traffic list matching the preset store can be determined based on the object information corresponding to each of at least one product object. The recommended traffic list includes multiple traffic keywords adapted to the preset store and the sorting information of the multiple traffic keywords. Moreover, different preset stores may correspond to different recommended traffic lists. After obtaining the recommended traffic list, the promoted product object can be determined from at least one product object based on the recommended traffic list, thus effectively realizing the ability to recommend promoted product objects adapted to the recommended traffic list for the preset store. Since the recommended traffic list can be dynamically adjusted according to changes in network traffic, the determined promoted product object is not fixed either. This ensures the flexibility and reliability of the promoted product object, thus overcoming the problem in the related technology that "relatively fixed promoted product objects cannot adapt to the changing market demands and reduce the quality and efficiency of the product object promotion operation". Then, promotion operations can be performed based on the determined promoted product object, ensuring the quality and effect of the traffic promotion operation from the merchant dimension and improving the practicability of this method.

[0035] The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0036] Figure 2 It is a schematic flowchart of a method for determining a promoted product object provided by an exemplary embodiment of the present application; refer to the accompanying drawings Figure 2, this embodiment provides a method for determining a promoted product object. The execution subject of this method is a device for determining a promoted product object. Among them, the device for determining a promoted product object can be implemented as software, or a combination of software and hardware. When the device for determining a promoted product object is implemented as hardware, it can specifically be various computing platforms capable of implementing the determination operation of the promoted product object, including but not limited to personal computers, servers, etc. When the device for determining a promoted product object is implemented as software, it can be installed in the computing platforms exemplified above. Based on the above device for determining a promoted product object, the determination operation of the promoted product object can be implemented. Specifically, the method for determining a promoted product object can include:

[0037] Step S201: Obtain the object information corresponding to each of at least one product object in a preset store.

[0038] Step S202: Based on the object information corresponding to each of at least one product object, determine a recommended traffic list that matches the preset store, where the recommended traffic list includes multiple traffic keywords adapted to the preset store and the sorting information of the multiple traffic keywords.

[0039] Step S203: Based on the recommended traffic list, determine a promoted product object among at least one product object.

[0040] The specific implementation methods and implementation principles of the above steps will be described in detail below:

[0041] Step S201: Obtain the object information corresponding to each of at least one product object in a preset store.

[0042] When the user has a need to determine a promoted product object, it can cause the device for determining a promoted product object (hereinafter referred to as the "determination device") to obtain the object information corresponding to each of at least one product object in a preset store. Among them, the object information can include at least one of the following: product object name, product object category, product object picture, historical interaction data of the product object. The above historical interaction data of the product object can include at least one of the following: historical transaction data of the product object, historical click data of the product object, historical exposure data of the product object, etc.

[0043] In some instances, the object information corresponding to each of at least one commodity object can be obtained through an e-commerce platform. Among them, the determination device can be communicatively connected to the e-commerce platform, or the determination device can be integrated on the e-commerce platform. Specifically, one or more preset stores can be settled in the e-commerce platform, and the object information corresponding to each of at least one commodity object in the preset store can be stored in the database or server of the e-commerce platform. Then, the object information corresponding to each of at least one commodity object in the preset store can be obtained through the database or server of the e-commerce platform; alternatively, the object information corresponding to each of at least one commodity object in the preset store is published on the e-commerce platform, and the object information corresponding to each of at least one commodity object in the preset store can be obtained by performing a web crawler operation or information extraction operation on the e-commerce platform.

[0044] In other instances, the object information corresponding to each of at least one commodity object in the preset store can be obtained not only through the e-commerce platform but also through a preset device (client or third device). At this time, obtaining the object information corresponding to each of at least one commodity object in the preset store can include: determining the preset device communicatively connected to the determination device; actively or passively obtaining the object information corresponding to each of at least one commodity object through the preset device, which to a certain extent ensures the flexible reliability of obtaining the object information.

[0045] Step S202: Based on the object information corresponding to each of at least one commodity object, determine a recommended traffic list that matches the preset store, where the recommended traffic list includes multiple traffic keywords that are adapted to the preset store and the sorting information of the multiple traffic keywords.

[0046] For any preset store, the commodity objects sold in the preset store can be reflected or identified through object information, that is, different commodity objects can correspond to different object information. In order to accurately determine the promotion of commodity objects for the preset store, after obtaining the object information corresponding to each of at least one commodity object, the object information corresponding to each of at least one commodity object can be analyzed and processed to determine a recommended traffic list that matches the preset store. Among them, different preset stores can correspond to the same or different recommended traffic lists. Generally, different preset stores often correspond to different recommended traffic lists.

[0047] For the recommended traffic list, it may include multiple traffic keywords adapted to a preset store and sorting information of the multiple traffic keywords. The above-mentioned traffic keywords may refer to topics or events related to the transaction of commodity objects within a specific time period and widely concerned and / or discussed by the public. The specific time period may be a preset list update cycle. For example, the list update cycle may be implemented as a specific time period within 1 day, 3 days, 5 days, or 7 days, etc. Moreover, for the traffic keywords included in the recommended traffic list, they may include topics from various fields. For example, topics from the technology field, entertainment field, economic field, or social issues, etc. The above-mentioned topics from various fields can spread rapidly through social media platforms, news media, and daily communications. In addition, for the traffic keywords included in the recommended traffic list, they can be determined by analyzing and processing the hot data from different data sources. The hot data from different data sources may include in-site hot data corresponding to e-commerce platforms and off-site hot data corresponding to other platforms, etc.; and the hot data from different data sources can be obtained through data mining operations, hot recommendation operations provided by operation and maintenance personnel, or hot recommendation operations of e-commerce platforms.

[0048] In addition, for the recommended traffic list, it can analyze and process the object information corresponding to at least one commodity object respectively through a pre-trained neural network model. At this time, based on the object information corresponding to at least one commodity object respectively, determining the recommended traffic list matching the preset store may include: obtaining the pre-trained neural network model; inputting the object information corresponding to at least one commodity object respectively into the neural network model for analysis and processing, and obtaining the recommended traffic list matching the preset store output by the neural network model, which to a certain extent ensures the accuracy and reliability of determining the recommended traffic list.

[0049] In some other instances, after determining the recommended traffic list, an update operation can be performed on the recommended traffic list according to a preset cycle. At this time, the method in this embodiment may further include: obtaining the update cycle corresponding to the recommended traffic list; and updating and displaying the recommended traffic list based on the update cycle.

[0050] For the recommended traffic list, to ensure the timeliness and effectiveness of the recommended traffic list, the data in the recommended traffic list can be updated according to a preset update cycle. At this time, the update cycle corresponding to the recommended traffic list can be obtained first. The update cycle can be obtained through human-computer interaction operations, or the update cycle can be the default parameter corresponding to the determining device of the promoted commodity object. In the actual scenario, the update cycle can be implemented as any one of the following: 1 day, 3 days, 5 days, 7 days, etc. After obtaining the update cycle corresponding to the recommended traffic list, an update display operation can be performed on the recommended traffic list based on the update cycle. Specifically, the recommended traffic list before the update and the recommended traffic list after the update can be obtained first, and then the recommended traffic list before the update and the recommended traffic list after the update can be analyzed and compared. When the recommended traffic list before the update is different from the recommended traffic list after the update, the recommended traffic list before the update can be updated and adjusted to the recommended traffic list after the update, thus realizing the timely update operation of the recommended traffic list, and then ensuring the timeliness and effectiveness of the display of the recommended traffic list to a certain extent.

[0051] In some other examples, after determining the recommended traffic list that matches the preset store, relevant explanatory information related to the recommended traffic list can also be generated to facilitate users' understanding of the data in the recommended traffic list. At this time, the method in this embodiment can further include: generating insight information corresponding to any one of the traffic keywords in the recommended traffic list, where the insight information is used to explain the data of the traffic keyword; and associatively displaying the traffic keyword and the insight information corresponding to the traffic keyword.

[0052] Specifically, since the recommended traffic list can include multiple related data, for example: the recommended traffic list can include traffic keywords, trend indexes, increase degrees, etc. To facilitate users' understanding of the meanings and situations of each data in the recommended traffic list, insight information corresponding to any one of the traffic keywords in the recommended traffic list can be generated, and the insight information is used to explain the traffic keywords in the recommended traffic list. In some examples, the insight information can include at least one of the following: explanatory information of the traffic keyword, a picture of the commodity object corresponding to the traffic keyword, search keywords of the traffic keyword in the e-commerce platform, interpretation information of the interest group corresponding to the traffic keyword, etc. After obtaining the insight information corresponding to the traffic keyword, the traffic keyword and the insight information can be associatively displayed, and users can flexibly view according to the insight information of any one of the traffic keywords, so as to facilitate users' understanding of the viewed traffic keyword, thus ensuring the accurate reliability of users' viewing of the recommended traffic list to a certain extent.

[0053] Step S203: Based on the recommended traffic list, determine the promoted product objects among at least one product object.

[0054] Since the recommended traffic list includes traffic keywords adapted to the preset store, and traffic keywords often correspond to a large amount of promoted traffic. To ensure the promotion quality and effect of the promoted product objects to a certain extent, after obtaining the recommended traffic list, the recommended traffic list and at least one product object can be analyzed and processed to determine the promoted product objects among at least one product object. Among them, the number of promoted product objects can be one or more, and the promoted product objects can be product objects having an associated degree with the traffic keywords in the recommended traffic list. Therefore, the promotion operations of the promoted product objects can often capture the hot traffic corresponding to the traffic keywords.

[0055] In addition, for at least one traffic keyword included in the recommended traffic list, different traffic keywords can be sorted according to the traffic heat, that is, different traffic keywords can correspond to different hot traffic, and any one traffic keyword can correspond to one or more promoted product objects. Therefore, the promoted product objects corresponding to different traffic keywords can correspond to different estimated promoted traffic. Then, corresponding recommendation degree labels can be added to the promoted product objects based on the estimated promoted traffic corresponding to the promoted product objects. The promoted product objects with different recommendation degree labels can correspond to different estimated promoted traffic. To enable merchants to timely understand the traffic promotion levels corresponding to each promoted product object, the promoted product objects can be associated and displayed with the added recommendation degree labels.

[0056] For the promoted product objects, the determination method of the promoted product objects in this embodiment is not limited. In some examples, the promoted product objects can be determined by the matching degree between the product objects and each traffic keyword in the recommended traffic list. At this time, based on the recommended traffic list, determining the promoted product objects among at least one product object can include: determining the matching degree between each product object and the traffic keyword based on the object information corresponding to each of the at least one product object and the traffic keyword in the recommended traffic list; determining the hot product objects matched by each traffic keyword based on the matching degree between each product object and the traffic keyword. Specifically, when the matching degree between the product object and the traffic keyword is greater than or equal to the preset threshold, the product object corresponding to the matching degree is determined as the hot product object; unifying the hot product objects matched by all traffic keywords as the promoted product objects, which ensures the accuracy and reliability of determining the promoted product objects to a certain extent.

[0057] In some other instances, the promoted product object can be determined not only by the matching degree between the product object and each traffic keyword in the recommended traffic list, but also by the matching degree between the product object and any traffic keyword in the recommended traffic list. At this time, based on the recommended traffic list, determining the promoted product object among at least one product object may include: in response to a selection operation on the recommended traffic list, obtaining a target traffic keyword; among at least one product object, determining a recommended product object related to the target traffic keyword; and based on the recommended product object, determining the promoted product object.

[0058] After obtaining the recommended traffic list, the user can input a selection operation for some traffic keywords in the recommended traffic list. In response to the selection operation on the recommended traffic list, a target traffic keyword is obtained. The number of target traffic keywords can be one or more. After obtaining the target traffic keyword, a recommended product object related to the target traffic keyword can be determined among at least one product object. The recommended product object can be one or more product objects whose matching degree with the target traffic keyword is greater than or equal to a preset threshold, or the recommended product object can be one or more product objects whose association degree with the target traffic keyword is greater than or equal to a preset threshold. After obtaining the recommended product object, the recommended product object can be analyzed and processed to determine the promoted product object. In some instances, the recommended product object can be directly determined as the promoted product object; or, after obtaining the recommended product object, it can be identified whether the recommended product object meets the preset promotion requirements. Specifically, it can be identified whether the recommended product object meets the preset promotion requirements through a manual review operation or a device review operation; when the recommended product object meets the preset promotion requirements, the recommended product object can be directly determined as the promoted product object, which to a certain extent ensures the accuracy and reliability of determining the promoted product object.

[0059] In some other instances, not only can the recommended product object be directly determined as the promoted product object, but also the promoted product object can be determined in combination with the user's self-selection operation on the product object. At this time, based on the recommended product object, determining the promoted product object may include: in response to a self-selection operation on at least one product object, obtaining a self-selected product object; and based on the recommended product object and the self-selected product object, determining the promoted product object.

[0060] Specifically, after obtaining the recommended product objects, the user can identify whether the recommended product objects meet the preset promotion requirements. Among them, the preset promotion requirements may include at least one of the following: the preset number of promoted product objects, the preset category of promoted product objects, the preset class of promoted product objects, and so on. For example, when the preset promotion requirement includes the preset number of promoted product objects, it is possible to first determine whether the number of recommended product objects meets the preset number of promoted product objects. When the number of recommended product objects is less than the preset number of promoted product objects, the selection operation can be continued for at least one product object. At this time, the selection operation for at least one product object can be obtained. The selection operation is used to perform a selection operation on some of the at least one product object. The selection operation can be implemented as a click operation, a check operation, and so on. Then, the selected product objects can be determined based on the obtained selection operation for at least one product object. The number of the selected product objects is one or more. After obtaining the selected product objects, the promoted product objects can be determined based on the recommended product objects and the selected product objects. Specifically, the recommended product objects and the selected product objects are determined as the promoted product objects, so as to ensure to a certain extent that the determined promoted product objects can meet the personalized promotion requirements.

[0061] Similarly, when the preset promotion requirement includes the preset category of promoted product objects, it is possible to first determine whether the category of the recommended product objects meets the preset category of promoted product objects. When the category of the recommended product objects does not meet the preset category of promoted product objects, the selection operation for at least one product object can be obtained, and the selected product objects corresponding to the selection operation can be obtained. The number of the selected product objects is one or more. After obtaining the selected product objects, the promoted product objects can be determined based on the recommended product objects and the selected product objects. Specifically, the recommended product objects and the selected product objects are determined as the promoted product objects, so as to ensure to a certain extent that the determined promoted product objects can meet the personalized promotion requirements.

[0062] The method for determining the promoted product object provided in this embodiment determines a recommended traffic list that matches the preset store through the object information corresponding to at least one product object in the preset store, and then can determine the promoted product object from at least one product object based on the recommended traffic list. Since the promoted product object is determined based on the recommended traffic list adapted to the preset store, and the recommended traffic lists corresponding to different preset stores can be different, the promoted product objects determined by different preset stores and recommended traffic lists can be different. Thus, to a certain extent, the operation of determining the promoted product object personalized at the store dimension is realized, that is, the promoted product object can be flexibly adjusted with the change of the recommended traffic list, and the determined promoted product object can capture or attract the hot traffic information corresponding to each traffic keyword in the recommended traffic list. In this way, when promoting based on the promoted product object, the quality and effect of the product object promotion operation can be improved to a certain extent, and thus the practicability of this method is improved.

[0063] Figure 3 It is a schematic flowchart for determining a recommended traffic list that matches a preset store based on the object information corresponding to at least one product object provided in an exemplary embodiment of this application; on the basis of the above embodiment, refer to the appendix Figure 3 As shown, for the recommended traffic list, it can not only be determined by analyzing and processing the object information corresponding to at least one product object through a pre-trained neural network model, but also can be determined by the platform traffic keywords of the platform to which the preset store belongs. At this time, based on the object information corresponding to at least one product object, determining a recommended traffic list that matches the preset store includes:

[0064] Step S301: Obtain the platform traffic keywords of the platform to which the preset store belongs.

[0065] Since the recommended traffic list includes at least one traffic keyword that matches the preset store, in order to accurately determine the recommended traffic list that matches the preset store, the platform traffic keywords of the platform to which the preset store belongs can be obtained first. The platform to which the preset store belongs can be a preset e-commerce platform or a preset promotion platform. One or more online stores can be settled in the platform to which the preset store belongs, and the preset store can be any one of one or more online stores.

[0066] In some examples, the platform traffic keyword can be at least one platform theme on the platform of the preset store with the traffic popularity reaching a preset threshold. Specifically, whether a platform theme can become a platform traffic keyword can be determined by the traffic popularity of the platform theme. At this time, obtaining the platform traffic keywords of the platform of the preset store may include: obtaining all the platform themes of the preset store platform; determining the traffic information corresponding to each platform theme; when the traffic information is greater than or equal to the preset traffic threshold, determining the platform theme corresponding to the traffic information as the platform traffic keyword; when the traffic information is less than the preset traffic threshold, determining the platform theme corresponding to the traffic information as not a platform traffic keyword, which to a certain extent ensures the accuracy and reliability of obtaining the platform traffic keywords.

[0067] Step S302: Based on the platform traffic keywords and the object information corresponding to at least one commodity object, determine a recommended traffic list that matches the preset store.

[0068] After obtaining the platform traffic keywords, the platform traffic keywords and the object information corresponding to at least one commodity object can be analyzed and processed to determine a recommended traffic list that matches the preset store. In some examples, the recommended traffic list can be determined by analyzing and processing the platform traffic keywords and the object information corresponding to at least one commodity object through a pre-trained deep learning model. At this time, based on the platform traffic keywords and the object information corresponding to at least one commodity object, determining a recommended traffic list that matches the preset store may include: obtaining the pre-trained deep learning model; inputting the platform traffic keywords and the object information corresponding to at least one commodity object into the deep learning model for analysis and processing to obtain the recommended traffic list that matches the preset store output by the deep learning model, which to a certain extent ensures the accuracy and reliability of determining the recommended traffic list.

[0069] In other examples, the recommended traffic list can not only be determined by a pre-trained deep learning model, but also be determined by the matching degree between each commodity object and the platform traffic keywords. At this time, based on the platform traffic keywords and the object information corresponding to at least one commodity object, determining a recommended traffic list that matches the preset store may include: obtaining the heat performance data of at least one commodity object in the historical promotion period, where the heat performance data includes at least one of the following: transaction data, click data, exposure data; determining the matching degree between each commodity object and the platform traffic keywords based on the heat performance data and the object information; determining the recommended traffic list based on the matching degree.

[0070] For a preset store, since different preset stores often sell different product objects, in order to generate corresponding and adapted recommended traffic lists for different preset stores, it is possible to first obtain the heat performance data of at least one product object in the historical promotion period. Specifically, the heat performance data can be obtained by statistically calculating the relevant data of each product object. And the above historical promotion period can be implemented as at least one of the following: within the past 1 day, within the past 3 days, within the past 5 days, or within the past 7 days, etc. The above heat performance data can include heat-related data corresponding to at least one product object, which can include at least one of the following: transaction data, click data, exposure data, etc. It can be understood that the heat performance data can not only include transaction data, click data, and exposure data, but also include user interaction behavior data for product objects. For example, the heat performance data can include at least one of the following: the number of views corresponding to the product object, the number of times added to the shopping cart corresponding to the product object, the collection information corresponding to the product object, the attention information corresponding to the product object, etc. Those skilled in the art can flexibly adjust or configure the heat performance data according to specific application scenarios or application requirements.

[0071] After obtaining the heat performance data, it is possible to analyze and process the heat performance data and the object information corresponding to each of the at least one product object to determine the matching degree between each product object and the platform traffic keywords. In some instances, the matching degree between each product object and the platform traffic keywords can be determined by the similarity between the heat performance data and the object information. At this time, based on the heat performance data and the object information, determining the matching degree between each product object and the platform traffic keywords can include: determining the similarity between each product object and the platform traffic keywords based on the heat performance data and the object information; determining the matching degree between each product object and the platform traffic keywords based on the similarity, where the matching degree is positively correlated with the similarity.

[0072] After obtaining the matching degree between each product object and the platform traffic keywords, it is possible to analyze and process the matching degree between each product object and the platform traffic keywords to determine a recommended traffic list adapted to the preset store. In some instances, when the matching degree is greater than or equal to the preset threshold, the platform hot topic corresponding to the matching degree is determined as the traffic keyword included in the recommended traffic list; when the matching degree is less than the preset threshold, the platform hot topic corresponding to the matching degree is determined as not being the traffic keyword included in the recommended traffic list, thus realizing an accurate determination operation of the recommended traffic list.

[0073] In some application scenarios, the traffic keywords in the recommended traffic list may include not only the platform traffic keywords of the platform to which the preset store belongs, but also the off-platform traffic keywords of other platforms. At this time, based on the object information corresponding to each of at least one commodity object, determining the recommended traffic list that matches the preset store includes: obtaining the in-platform traffic keywords of the platform to which the preset store belongs (corresponding to the platform traffic keywords in the above embodiment) and the off-platform traffic keywords corresponding to other platforms (for example: third-party platforms); based on the in-platform traffic keywords, off-platform traffic keywords, and the object information corresponding to each of at least one commodity object, determining the recommended traffic list that matches the preset store. At this time, the multiple traffic keywords in the generated recommended traffic list include both in-platform traffic keywords and off-platform traffic keywords, which to a certain extent ensures the comprehensiveness of the recommended traffic list.

[0074] In other instances, after obtaining the matching degrees between each commodity object and the platform traffic keywords, the recommended traffic list can be determined based on the matching degrees and the hot trend data provided by the operation and maintenance personnel. At this time, determining the recommended traffic list based on the matching degrees may include: obtaining the trend experience data provided by the operation and maintenance personnel; based on the matching degrees between each commodity object and the platform traffic keywords and the trend experience data, determining the recommended traffic list that is suitable for the preset store. Specifically, based on the matching degrees between each commodity object and the platform traffic keywords and the trend experience data, determining the recommended traffic list that is suitable for the preset store may include: based on the trend experience data, determining a trend traffic list, where the trend traffic list includes the trend traffic themes obtained based on the trend experience data and the sorting information of each trend traffic theme; based on the matching degrees between each commodity object and the platform traffic keywords, determining a matching traffic list; based on the trend traffic list and the matching traffic list, determining the recommended traffic list that is suitable for the preset store, and this recommended traffic list can be determined by performing a fusion analysis process on the trend traffic list and the matching traffic list, which to a certain extent ensures the accuracy and reliability of determining the recommended traffic list.

[0075] It should be noted that when the recommended traffic list is determined based on the trend experience data provided by the operation and maintenance personnel, in order to ensure the accuracy and reliability of determining the recommended traffic list to a certain extent, before determining the recommended traffic list that matches the preset store, it is necessary to perform an audit operation on the trend experience data provided by the operation and maintenance personnel. Specifically, the preset audit rules can be used to perform compliance audit, legality audit operation, and rationality review operation on the trend experience data to ensure that the obtained trend experience data meets the risk control requirements, thereby improving the legal accuracy of generating the recommended traffic list based on the trend experience data.

[0076] In this embodiment, by obtaining the platform traffic keywords of the preset store's affiliated platform, and then determining the recommended traffic list matching the preset store based on the platform traffic keywords and the object information corresponding to each of at least one product object, the accuracy and reliability of determining the recommended traffic list are ensured to a certain extent.

[0077] Figure 4 It is a flowchart of another method for determining a promoted product object provided by an exemplary embodiment of the present application; on the basis of the above embodiment, refer to the attached Figure 4 As shown, after determining the recommended traffic list, the recommended traffic list can be flexibly displayed. At this time, the method in this embodiment may further include:

[0078] Step S401: Obtain preset dimension parameters for displaying the recommended traffic list.

[0079] For the recommended traffic list, corresponding recommended traffic lists can be generated through different parameter dimensions, and then the recommended traffic list can be displayed through different preset dimension parameters. Among them, the preset dimension parameters may include at least one of the following: trend index, production-investment index, search index, etc. The trend index is used to identify the traffic growth rate of the traffic keywords in the recommended traffic list on the preset platform, the production-investment index is used to identify the input-output ratio of the product object on the preset platform, and the search index is used to identify the search traffic of the traffic keywords on the preset platform. It should be noted that the recommended traffic lists corresponding to the above various preset dimension parameters are list data obtained through data desensitization processing, which can ensure the compliance and legality of the recommended traffic list.

[0080] In some instances, for the preset dimension parameters corresponding to the recommended traffic list, it can be determined through human-computer interaction operations. At this time, obtaining the preset dimension parameters for displaying the recommended traffic list may include: before generating the recommended traffic list, displaying a human-computer interaction interface; obtaining the parameter configuration operations input by the user in the human-computer interaction interface; and determining the preset dimension parameters for displaying the recommended traffic list based on the parameter configuration operations, so as to ensure the accuracy and reliability of obtaining the preset dimension parameters to a certain extent. It can be understood that the preset dimension parameters can not only be obtained through human-computer interaction operations, but also be default parameters in the system. At this time, the preset dimension parameters can be obtained by accessing the preset area or preset device storing the preset dimension parameters.

[0081] Step S402: Display the recommended traffic list based on the preset dimension parameters.

[0082] Since different preset dimension parameters correspond to different recommended traffic lists, after obtaining the preset dimension parameters, the corresponding recommended traffic lists can be displayed based on the preset dimension parameters. For example, the preset dimension parameters can include: trend index, production-investment index, search index. The trend index can correspondingly generate recommended traffic list 1, the production-investment index can correspondingly generate recommended traffic list 2, and the search index can correspondingly generate recommended traffic list 3. After generating the recommended traffic lists corresponding to each preset dimension parameter, the recommended traffic lists can be displayed based on the preset dimension parameters. For example, when the current preset dimension parameter is the trend index, recommended traffic list 1 corresponding to the trend index can be displayed; when the current preset dimension parameter is the search index, recommended traffic list 3 corresponding to the search index can be displayed. In this way, the flexible display operation of the corresponding recommended traffic lists can be realized based on different preset dimension parameters, meeting the user's viewing requirements for different recommended traffic lists.

[0083] In some other examples, after displaying the recommended traffic list, the displayed recommended traffic list can also be switched for display. At this time, the method in this embodiment can further include: in response to a parameter switching operation for the preset dimension parameter, obtaining the switched parameter; determining the switched recommended traffic list corresponding to the switched parameter; and switching the displayed recommended traffic list to the switched recommended traffic list.

[0084] Since the recommended traffic list can be displayed through different preset dimension parameters, users can view the recommended traffic lists corresponding to different preset dimension parameters according to their needs. To facilitate the user's switching and viewing operation of the recommended traffic lists corresponding to different preset dimension parameters, after displaying the recommended traffic list, when the preset dimension parameter corresponding to the recommended traffic list does not meet the user's requirements, the user can input a parameter switching operation for the preset dimension parameter. The parameter switching operation can be implemented as a manual switching operation, a voice switching operation, etc. After obtaining the parameter switching operation for the preset dimension parameter, the switched parameter can be obtained based on the parameter switching operation. Since different preset dimension parameters can correspond to different recommended traffic lists, the switched recommended traffic list corresponding to the switched parameter can be determined, and then the displayed recommended traffic list can be switched to the switched recommended traffic list. In this way, the switching display operation of the recommended traffic list is realized, meeting the user's flexible viewing requirement operation for the recommended traffic list.

[0085] In some other examples, for the recommended traffic list, in order to improve the usability of the recommended traffic list, a hierarchical tagging operation can be performed on the traffic keywords in the recommended traffic list. At this time, the method in this embodiment can further include: obtaining hierarchical rule information for analyzing and processing each traffic keyword in the recommended traffic list; performing hierarchical tagging on each traffic keyword based on the hierarchical rule information to generate hierarchical tags corresponding to each traffic keyword; and displaying the hierarchical tags corresponding to each traffic keyword in the recommended traffic list.

[0086] Among them, for each traffic keyword in the recommended traffic list, different traffic keywords can correspond to different traffic level levels. In order to enable users to intuitively view or understand the traffic levels corresponding to each traffic keyword in the recommended traffic list, a hierarchical processing operation can be performed on each traffic keyword in the recommended traffic list. In order to implement the hierarchical operation on each traffic keyword in the recommended traffic list, the hierarchical rule information for analyzing and processing each traffic keyword in the recommended traffic list can be obtained first. In some examples, the hierarchical rule information can be stored in a preset area or a preset device, and the hierarchical rule information can be obtained by accessing the preset area or the preset device. The hierarchical rule information can include the mapping relationship between different hot traffic degrees and hierarchical tags.

[0087] After obtaining the hierarchical rule information, a hierarchical tagging operation can be performed on each traffic keyword based on the hierarchical rule information, so as to generate hierarchical tags corresponding to each traffic keyword. Different hot traffic degrees can generate different hierarchical tags. In some examples, the hierarchical tags can include: the S-level tag for identifying very high hot traffic, the A+-level tag for identifying relatively high hot traffic, the A-level tag for identifying general hot traffic, the B-level tag for identifying poor hot traffic, and so on. After generating the hierarchical tags corresponding to each traffic keyword, the hierarchical tags corresponding to each traffic keyword can be displayed in the recommended traffic list, so that users can quickly and intuitively understand the hot traffic levels of each traffic keyword through the displayed hierarchical tags, and then it is convenient for users to flexibly view the traffic keywords with different hot traffic levels.

[0088] In this embodiment, by obtaining the preset dimension parameters for displaying the recommended traffic list and then displaying the recommended traffic list based on the preset dimension parameters, the flexible display operation of the recommended traffic list can be effectively realized from different parameter dimensions, and then the personalized viewing requirements of users for the recommended traffic list with different parameter dimensions can be met, and the usability of the method can be improved to a certain extent.

[0089] Figure 5 A schematic flowchart of another method for determining a promoted commodity object provided by an exemplary embodiment of the present application; based on any of the above embodiments, refer to the attached Figure 5 As shown, after determining the promoted commodity object among at least one commodity object, a corresponding promotion delivery plan can be generated based on the promoted commodity object to assist the merchant in promoting the commodity object based on the promotion delivery plan. At this time, the method in this embodiment may further include:

[0090] Step S501: Obtain a promotion bid corresponding to the promoted commodity object.

[0091] After obtaining the promoted commodity object, a promotion delivery plan for assisting the merchant in promoting operations can be automatically generated, which can improve the efficiency of the merchant in setting the promotion delivery plan to a certain extent. In order to accurately generate a promotion delivery plan corresponding to the promoted commodity object, the promotion bid corresponding to the promoted commodity object can be obtained first. Among them, the promotion bid can include daily limit bid or weekly limit bid, etc. Specifically, the promotion bid can be obtained through human-computer interaction operations, or the promotion bid can be default parameters pre-configured by the determining device of the promoted commodity object, etc., as long as the accuracy and reliability of obtaining the promotion bid can be ensured.

[0092] Step S502: Generate a promotion delivery plan and a promotion traffic mode corresponding to the promoted commodity object based on the promotion bid.

[0093] Since the size of the promotion bid can affect the display opportunity and cost of the promoted commodity object, which is an important factor in the promotion delivery plan, after obtaining the promotion bid, a promotion delivery plan and a promotion traffic mode corresponding to the promoted commodity object can be generated based on the promotion bid. Among them, the promotion delivery plan can include at least one of the following: description information of the promoted commodity object, performance index parameters (such as click-through rate CTR, conversion rate CVR, etc.), target audience characteristics (date of birth, gender, geographical location, hobbies, consumption habits, etc.), user behavior insight information for identifying the online behavior of the audience, budget planning, etc.

[0094] For the promotion traffic method, different promotion traffic methods may include promotion traffic positions (or called hot traffic points) at different locations. Different promotion traffic methods may correspond to different promotion traffic levels. Among them, the promotion traffic positions at different locations may include at least one of the following: search engine advertising positions (the top of the search page, the bottom of the search page, the sidebar of the search page), social media platform advertising promotion positions, video platform advertising promotion positions, etc. Those skilled in the art can flexibly configure the promotion traffic method corresponding to the promoted commodity object according to the specific application scenario or application requirements, which will not be elaborated here.

[0095] Step S503: Associatively display the promotion delivery plan, the promotion traffic method, and the promoted commodity object.

[0096] After obtaining the promotion delivery plan and the promotion traffic method, in order to facilitate the user to timely understand the detailed information of the generated promotion delivery plan and flexibly view the promotion delivery plan and the promotion traffic method, the promotion delivery plan, the promotion traffic method, and the promoted commodity object can be associatively displayed.

[0097] In some examples, after associatively displaying the promotion delivery plan, the promotion traffic method, and the promoted commodity object, the user can also flexibly adjust or configure the promotion delivery plan according to the needs. At this time, the method in this embodiment may include: obtaining the adjustment operation input by the user for the promotion delivery plan; adjusting the promotion delivery plan based on the adjustment operation to obtain the adjusted promotion delivery plan, which to a certain extent ensures that the obtained promotion delivery plan can meet the user's needs.

[0098] In some other examples, after associatively displaying the promotion delivery plan, the promotion traffic method, and the promoted commodity object, the promotion delivery plan can be flexibly adjusted based on whether the estimated promotion traffic of the promoted commodity object meets the user's needs. At this time, the method in this embodiment may further include: obtaining the estimated promotion traffic corresponding to the promoted commodity object; when the estimated promotion traffic does not meet the preset traffic threshold, generating a modification suggestion corresponding to the promotion delivery plan.

[0099] After obtaining the promotion plan, the estimated promotion traffic corresponding to the promoted product object can be obtained first. Specifically, the promotion plan can be analyzed to obtain the estimated promotion traffic corresponding to the promoted product object. In some examples, the estimated promotion traffic can include at least one of the following: the product object conversion traffic corresponding to the promoted product object, the product object click traffic corresponding to the promoted product object, and so on. Moreover, the estimated promotion traffic can be determined through a preset relationship corresponding to the promotion bid. At this time, obtaining the estimated promotion traffic corresponding to the promoted product object can include: based on the promotion plan, determining the promotion bid corresponding to the promoted product object; based on the promotion bid corresponding to the promoted product object and the traffic promotion method, determining the estimated promotion traffic corresponding to the promoted product object.

[0100] Furthermore, after obtaining the estimated promotion traffic corresponding to the promoted product object, the estimated promotion traffic can be analyzed and compared with the preset traffic threshold. When the estimated promotion traffic meets the preset traffic threshold, it indicates that the promotion effect of the promotion plan can meet the user's needs, and at this time, no adjustment or modification operation needs to be performed on the promotion plan; when the estimated promotion traffic does not meet the preset traffic threshold, it indicates that the promotion effect of the promotion plan cannot meet the user's needs. To ensure the promotion quality and effect for the promoted product object to a certain extent, a modification suggestion corresponding to the promotion plan can be generated. The modification suggestion can include a modification suggestion for the promotion bid, a modification suggestion for the traffic promotion method, and so on. The generated modification suggestion can assist the user in flexibly adjusting the promotion plan, ensuring the quality and efficiency of the flexible adjustment of the promotion plan.

[0101] In some other examples, after determining the promoted product object among at least one product object, suggestion information for the promotion content corresponding to the promoted product object can be generated. At this time, the method in this embodiment can further include: based on the recommended traffic list, generating keywords corresponding to each traffic keyword; based on the keywords, generating promotion suggestion information for the promoted product object, where the promotion suggestion information includes at least one of the following: picture suggestion information for the promoted product object, copywriting suggestion information for the promoted product object, video suggestion information for the promoted product object; based on the promotion suggestion information, generating the promotion content corresponding to the promoted product object.

[0102] For the promoted product object, in order to effectively promote the promoted product object, corresponding promotion content can be created for the promoted product object. The promotion content can include at least one of the following: promotion pictures, promotion copywriting, promotion videos, etc. In order to ensure the quality and effect of the promotion operation of the promoted product object to a certain extent, keywords corresponding to each traffic keyword can be generated based on the recommended traffic list. Specifically, keyword extraction operations can be performed on the traffic keywords in the recommended traffic list, so as to obtain the keywords corresponding to each traffic keyword in the recommended traffic list.

[0103] After obtaining the keywords, promotion suggestion information for the promoted product object can be generated based on the keywords. The promotion suggestion information can include at least one of the following: picture suggestion information for the promoted product object, copywriting suggestion information for the promoted product object, video suggestion information for the promoted product object. The above-mentioned picture suggestion information for the promoted product object is used to generate: promotion product object pictures including keywords, promotion product object pictures corresponding to keywords, etc.; the copywriting suggestion information for the promoted product object is used to generate: promotion product object copywriting including keywords, promotion product object copywriting corresponding to keywords, etc.; the video suggestion information for the promoted product object is used to generate: promotion product object video content including keywords, promotion product object video content corresponding to keywords, etc.

[0104] After obtaining the promotion suggestion information, the promotion suggestion information can be analyzed and processed to generate promotion content corresponding to the promoted product object. Among them, the promotion content can include at least one of the following: promotion product object pictures, promotion product object copywriting, promotion product object videos, etc. Specifically, the keywords corresponding to the hot content can be added to the promotion content, and then the promoted product object can be effectively promoted based on the promotion content, thus realizing personalized recommendation operations in traffic distribution. This not only overcomes the problems existing in the related technology, such as "due to the large difference in the ability to obtain traffic caused by the different levels of content production of each merchant, it is very difficult for merchants to conduct hot marketing campaigns and there is no traffic certainty", but also can ensure the quality and effect of promoting the promoted product object to a certain extent.

[0105] In this embodiment, by obtaining the promotion bid corresponding to the promoted product object, and then generating a promotion delivery plan and a promotion traffic method corresponding to the promoted product object based on the promotion bid, and associating and displaying the promotion delivery plan, the promotion traffic method with the promoted product object, the user can flexibly view the displayed promotion delivery plan and promotion traffic method, and then can promote the promoted product object based on the promotion delivery plan and the promotion traffic method, thereby improving the practicability of this method.

[0106] In specific applications, taking the trending hot topic list as the recommended traffic list and the hot traffic theme as the traffic keyword as an example, at this time, the trending hot topic list may include multiple hot traffic themes adapted to the preset store and the sorting information of multiple hot traffic entities. Refer to the appendix Figure 6 As shown in the figure, the present application embodiment provides a system for determining hot information. This determination system can help merchants obtain the new trend consumption demands of the entire network (which can be reflected by the hot traffic list), and at the same time can cover the commodity categories with mature hotspots based on the trending hot topic list. Among them, the trending hot topic list can be updated periodically. In this way, based on the trending hot topic list, it can help merchants understand or count the periodic stock demands. Specifically, the system for determining hot information can perform the following operations:

[0107] Step 1: Trend supply operation.

[0108] Among them, the trend supply operation may include: trend mining operation, trend presentation operation, and trend input operation on the operation and maintenance side; the above-mentioned trend mining operation is used to implement the mining and determination operation of the trending hot topic list, the trend presentation operation is used to implement the flexible display operation of the determined trending hot topic list, and the trend input operation is used to obtain the relevant trend reference information input by the operation and maintenance personnel (including at least one of the following: industrial track information, industry decision-making factors, industry hotspots / trends, trend review rules, etc.), so as to generate a trending hot topic list in combination with the relevant trend reference information.

[0109] The above-mentioned trend mining operation may include: obtaining the commodity information of at least one commodity in the preset store, and the commodity information may include at least one of the following: commodity name, commodity category, commodity picture, etc.; determining the hot traffic themes corresponding to at least one data source, where the hot traffic themes may include the platform hot traffic themes of the platform to which the preset store belongs and the off-site hot traffic themes of other data sources. Specifically, users can flexibly expand the data sources corresponding to the hot traffic themes according to application requirements; obtaining the heat performance data of at least one commodity in the historical promotion period, where the hot performance data includes at least one of the following: transaction data, click data, exposure data; then, based on the heat performance data and the commodity information, determining the matching degree between each commodity and the platform hot traffic theme; determining at least one hot traffic theme used to form the trending hot topic list based on the matching degree, and the determined trending hot topic list may correspond to the preset store, and different preset stores correspond to different trending hot topic lists.

[0110] For the hot traffic topics corresponding to at least one data source, in order to ensure the rationality and legality of obtaining hot traffic topics, before determining the trend hot list, the hot traffic topics corresponding to at least one data source can be subjected to heat review, compliance and legality review operations using pre-configured trend access rules. When the hot traffic topics pass the heat review, compliance and legality review operations, the trend hot list is allowed to be determined based on the hot traffic topics corresponding to at least one data source; when the hot traffic topics do not pass the heat review, compliance and legality review operations, it is prohibited to determine the trend hot list based on the hot traffic topics corresponding to at least one data source, which ensures the legality and rationality of generating the trend hot list to a certain extent.

[0111] In some other instances, after obtaining the trend hot list, in order to facilitate users' interpretation and understanding of the data in the trend hot list, the trend hot list can be interpreted. Specifically, the trend hot list can be subjected to trend insight analysis through a pre-set large model and combined with the merchant data provided by the merchant, so as to obtain the insight information corresponding to the trend hot list. Since the trend hot list can include multiple hot traffic topics, different hot traffic topics can correspond to corresponding insight information, and the merchant can display the insight information corresponding to the selected hot traffic topic. In some instances, the insight information corresponding to the hot traffic topic can be presented on the right side of the hot traffic topic; thus, the operation of trend insight analysis on the trend hot list is realized, and through the obtained insight information, it is convenient for users to interpret and understand the trend hot list.

[0112] In addition, for the multiple hot traffic themes included in the trend hot list, different hot traffic themes may correspond to different traffic levels. For each hot traffic theme in the trend hot list, different hierarchical labels can be used to label each hot traffic theme based on different traffic levels, such as: A-level labels, S-level labels, etc. Different hierarchical labels are used to identify different traffic levels. Moreover, for the multiple hot traffic themes in the trend hot list, the trend hot list can be displayed according to different preset parameters. For example, the preset parameters for displaying the trend hot list may include industry parameters / first-level category information, and then the trend hot list can be displayed based on the industry parameters / first-level category information. In this way, it realizes that merchants can display the trend hot list according to the industry parameters / first-level category information, and can meet the personalized viewing needs for displaying the trend hot list. In some other examples, for the trend hot list, it can be updated in a timely manner according to a preset update frequency. For example, the historical trend hot list includes multiple historical hot traffic themes; the current trend hot list includes multiple current hot traffic themes. When the multiple current hot traffic themes are different from the multiple historical hot traffic themes, the displayed historical trend hot list can be updated and displayed according to a preset update and replacement mechanism, so as to realize the update and display operation of the trend hot list.

[0113] After obtaining the trend hot list, the trend hot list can be displayed. Specifically, the trend hot list, the insight information corresponding to the trend hot list, and the traffic supporting resources corresponding to the trend hot list (which can refer to the promoted traffic positions corresponding to the hot traffic themes in the trend hot list) can be presented in layers, thus effectively realizing the flexible display operation of the trend hot list. Moreover, merchants can perform flexible query operations on the trend hot list. For example, they can perform trend query operations on the trend hot list based on keywords; or they can perform trend query operations on the trend hot list based on product information.

[0114] Step 2: Merchant supply operation;

[0115] After obtaining the trend hot list, merchant supply information corresponding to a preset store can be generated based on the trend hot list. The merchant supply information can include at least one of the following: product / creative suggestions, content guidance information, etc. Among them, the product / creative suggestions can include at least one of the following: product matching recommendations (used to identify at least one promotional product suitable for promotion operations among multiple products), competitor analysis information (used to identify the promotion competition situation of the same products in the industry), product suggestion information (used to determine the traffic promotion methods corresponding to each promotional product), and creative guidance corresponding to the promotional products; the content guidance information can be used to guide the creation of the promotional content corresponding to the promotional products. Specifically, after obtaining the content upload rules, the upload operations can be performed on each hot traffic theme included in the trend hot list based on the content upload rules, and the trend-related content in the trend hot list can be analyzed and processed to obtain keywords corresponding to each hot traffic theme; and based on the keywords and the preset graphic / video content templates, promotional graphics / videos corresponding to the recommended products can be generated, etc. Then, the promotional products can be promoted based on the promotional graphics / videos, which to a certain extent ensures the quality and effect of the promotion operations for the promotional products.

[0116] In addition, when determining the promotional products, it can be determined not only by analyzing and processing the trend hot list, but also by combining the user's selection operations. At this time, after obtaining the platform-recommended products determined based on the trend hot list, it can be first identified whether the platform-recommended products meet the user's needs. When the platform-recommended products do not meet the user's needs, the user can flexibly adjust and configure the platform-recommended products, and the self-selected recommended products selected by the user can be added. Then, the promotional products can be determined based on the platform-recommended products and the self-selected recommended products. In this way, the association relationship between the trend hot list and the associated products can be observed or adjusted, ensuring that the determined promotional products can meet the merchant's needs. It should be noted that in the process of determining the promotional products, the promotional products can be determined by combining the data provided by the operation and maintenance personnel. For the relevant data provided by the operation and maintenance personnel, content review operations can be performed on the relevant data provided by the operation and maintenance personnel (including in-site data and off-site data) to ensure the compliance and legality of the data. In addition, after determining the promotional products, in-site promotion operations and off-site promotion operations can be carried out for the promotional products, thereby improving the practicability of this method.

[0117] Step 3: Deploy the application.

[0118] After determining the trending hot topic list, promoted products in a preset store can be matched according to the trending hot topic list selected by the merchant, and then an advertising placement plan can be quickly created for the promoted products. This advertising placement plan can help the promoted products obtain the interest traffic corresponding to the trending hot topics across the board. This not only enables the merchant to complete more efficient trending hot topic marketing operations, but also helps the merchant capture the rapidly rising trending hot topic traffic, thereby ensuring to a certain extent the quality and effect of the promotion operations for the promoted products.

[0119] In addition, after obtaining the promoted products, since different promoted products can correspond to different stratification identifiers, and different stratification identifiers can correspond to different stratification traffic resource points, then the promoted products can be promoted based on different stratification traffic resource points. Among them, the stratification traffic resource points can be configured based on the traffic movement line design information of consumers, which is conducive to ensuring the promotion quality and effect of the promoted products. Moreover, when promoting the promoted products, the promoted products can be controlled based on a preset placement strategy. In some instances, the traffic commercial mechanism (used to identify the traffic monetization plan for specific products), the content recall mechanism, and the product recall mechanism can be determined first, and the traffic commercial mechanism, the content recall mechanism, and the product recall mechanism are used to control the promotion operations of the promoted products, so that different promoted products can be generated for different consumers, which is conducive to ensuring the promotion quality and effect of the promoted products.

[0120] The technical solution provided by this application embodiment can be realized as a tool for trend discovery and marketing delivery for merchants. Specifically, trend data is obtained through algorithm mining and operation input management, and artificial intelligence technology is used to process trend data to mine trend hot spots corresponding to preset stores (or merchants), and provide functions for interpreting trend hot spots and analyzing crowd portraits. Merchants can use this tool to query trend hot spots and create marketing delivery plans, and can also optimize commercial traffic through data analysis and regulation of competitive factors. In addition, this solution can recommend trend hot words to consumers in a personalized way, and does not require merchants to browse through many trend hot spots. Creative content production can be carried out by manual selection from hot spots, and trend hot spots suitable for placement can be recommended based on the merchant’s store products. Products matching the trend hot spots can be automatically brought out during placement, and the algorithm can be used to estimate the volume of traffic when the merchant places promotions, which is conducive to reducing the merchant’s manpower and financial cost investment, and improving the certainty of advertising placement, as well as doing a good job in the acceptance and promotion of trend hot spots across the entire network on the platform; in addition, by connecting the supply and demand of the merchant and consumer ends through trend hot spots, it can not only help merchants complete trend hot spot marketing more conveniently and improve the merchant’s advertising efficiency, but also improve the merchant’s trend product supply and trend consumption experience, further ensuring the practicality of this method.

[0121] In addition, in some of the processes described in the above embodiments and the accompanying drawings, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel, and the sequence numbers of the operations, such as 11, 12, etc., are only used to distinguish between different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0122] Figure 7 A schematic diagram of a device for determining a promotional commodity object provided by an exemplary embodiment of the present application; Figure 7 As shown, this embodiment provides a device for determining a promotional commodity object, the device for determining a promotional commodity object is used to perform the above Figure 2 The method for determining the promotional commodity object shown in the figure, specifically, the device for determining the promotional commodity object may include:

[0123] The first acquisition module 11 is used to acquire object information corresponding to at least one commodity object in a preset store;

[0124] The first determination module 12 is configured to determine a recommended traffic list based on the object information corresponding to at least one commodity object, where the recommended traffic list includes multiple traffic keywords adapted to a preset store and sorting information of the multiple traffic keywords;

[0125] The first processing module 13 is configured to determine a promoted commodity object among at least one commodity object based on the recommended traffic list.

[0126] The device for determining the promoted commodity object in this embodiment may further include the description content of the embodiments shown in the foregoing embodiments Figures 1 - 6 For the specific content, reference may be made to the detailed description in the above embodiments, and no detailed description will be given here.

[0127] Figure 8 It is a schematic structural diagram of a computing platform provided by an exemplary embodiment of the present application; as Figure 8 shown, this embodiment provides a computing platform, which is used to execute the method for determining the promoted commodity object shown above Figure 8 In practice, the computing platform may include: a memory 24 and a processor 25.

[0128] The memory 24 is used to store computer programs and can be configured to store various other data to support operations on the computing platform. Examples of these data include instructions for any application program or method operating on the computing platform, data structures, contact data, phone book data, messages, pictures, videos, etc.

[0129] The processor 25 is coupled to the memory 24 and is configured to execute the computer program in the memory 24 for: obtaining the object information corresponding to at least one commodity object in the preset store; determining a recommended traffic list based on the object information corresponding to at least one commodity object, where the recommended traffic list includes multiple traffic keywords adapted to the preset store and sorting information of the multiple traffic keywords; determining a promoted commodity object among at least one commodity object based on the recommended traffic list.

[0130] Further, as Figure 8 shown, the computing platform further includes: other components such as a communication component 26, a display 27, a power supply component 28, an audio component 29, etc. Figure 8 Only some components are schematically shown, which does not mean that the computing platform only includes Figure 8 the components shown. Additionally, Figure 8The components within the middle wireframe are optional components, rather than mandatory components, and can be determined according to the product form of the working node. The working node of this embodiment can be implemented as a terminal device such as a desktop computer, a laptop computer, a smart phone, or an IOT device, or can also be a server device such as a conventional server, a cloud server, or a server array. If the working node of this embodiment is implemented as a terminal device such as a desktop computer, a laptop computer, or a smart phone, it may include Figure 8 the components within the middle wireframe; if the working node of this embodiment is implemented as a server device such as a conventional server, a cloud server, or a server array, it may not include Figure 8 the components within the middle wireframe.

[0131] The above-mentioned memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disc.

[0132] The above-mentioned communication component is configured to facilitate communication between the device where the communication component is located and other devices in a wired or wireless manner. The device where the communication component is located can access a wireless network based on communication standards, such as 2G, 3G, 4G / LTE, 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel.

[0133] The above-mentioned display includes a screen, and the screen can include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can not only sense the boundaries of touch or swipe actions, but also detect the duration and pressure associated with the touch or swipe operations.

[0134] The above power supply component provides power for various components of the device where the power supply component is located. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device where the power supply component is located.

[0135] The above audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (abbreviated as MIC). When the device where the audio component is located is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive external audio signals. The received audio signals can be further stored in the memory or sent via the communication component. In some embodiments, the audio component further includes a speaker for outputting audio signals.

[0136] The above audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (abbreviated as MIC). When the device where the audio component is located is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive external audio signals. The received audio signals can be further stored in the memory or sent via the communication component. In some embodiments, the audio component further includes a speaker for outputting audio signals.

[0137] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it causes the processor to be able to implement the steps in the above method embodiments. Among them, the computer-readable storage medium can be implemented by volatile or non-volatile or a combination thereof, and can be removable or non-removable. Examples of computer-readable storage media include, but are not limited to, phase-change random access memory (abbreviated as PRAM), static random access memory (SRAM), dynamic random access memory (abbreviated as DRAM), other types of random access memory (abbreviated as RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (abbreviated as DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices or any other non-transmission medium

[0138] Accordingly, an embodiment of the present application further provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, the processor is enabled to implement each step in the above method embodiment. It should be understood that each process or a combination of multiple processes in the above method flow can be implemented by the computer program or instructions. In addition, these computer programs or instructions can be applied to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices, so that the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices can be used as devices to implement the corresponding functions in the above method embodiment.

[0139] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity object or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity object or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, commodity object or device including the element.

[0140] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for determining promotional commodity objects, characterized in that: include: Obtain object information corresponding to at least one product object in a preset store; Based on the object information corresponding to each of the at least one commodity object, a recommended traffic list matching the preset store is determined, wherein the recommended traffic list includes a plurality of traffic keywords adapted to the preset store and sorting information of the plurality of traffic keywords; Based on the recommended traffic list, a promotion commodity object is determined in the at least one commodity object.

2. The method according to claim 1, characterized in that Determining a recommended traffic list matching the preset store based on the object information corresponding to each of the at least one commodity objects includes: Get the platform traffic keywords of the platform to which the preset store belongs; Based on the platform traffic keyword and the object information corresponding to each of the at least one commodity object, a recommended traffic list matching the preset store is determined.

3. The method according to claim 2, characterized in that Determining a recommended traffic list matching the preset store based on the platform traffic keyword and the object information corresponding to the at least one commodity object includes: Acquire the popularity performance data of the at least one commodity object in the historical promotion cycle, wherein the popularity performance data includes at least one of the following: transaction data, click data, and exposure data; Determine the matching degree between each commodity object and the platform traffic keyword based on the popularity performance data and the object information; Based on the matching degree, the recommended traffic list is determined.

4. The method according to claim 1, characterized in that: Determining a promotional commodity object from the at least one commodity object based on the recommended traffic list includes: In response to a selection operation on the recommended traffic list, obtaining a target traffic keyword; Determine, among the at least one commodity object, a recommended commodity object related to the target traffic keyword; Based on the recommended commodity object, the promoted commodity object is determined.

5. The method according to claim 4, characterized in that Determining the promoted commodity object based on the recommended commodity object includes: In response to a self-selection operation on the at least one commodity object, obtaining a self-selected commodity object; The promoted commodity object is determined based on the recommended commodity object and the self-selected commodity object.

6. The method according to claim 1, characterized in that After determining the recommended traffic list, the method further includes: Obtaining preset dimension parameters for displaying the recommended traffic list; Based on the preset dimension parameters, the recommended traffic list is displayed.

7. The method according to claim 6, characterized in that After displaying the recommended traffic list, the method further includes: In response to a parameter switching operation on the preset dimension parameter, obtaining a switched parameter; Determine a post-switching recommended flow list corresponding to the post-switching parameters; The displayed recommended traffic list is switched to the switched recommended traffic list.

8. The method according to claim 6, characterized in that The method further comprises: Acquire hierarchical rule information for analyzing and processing each traffic keyword in the recommended traffic list; Based on the hierarchical rule information, each traffic keyword is hierarchically marked to generate a hierarchical label corresponding to each traffic keyword; Hierarchical tags corresponding to the various traffic keywords are displayed in the recommended traffic list.

9. The method according to any one of claims 1 to 8, characterized in that: After determining the recommended traffic list matching the preset store, the method further includes: Generate insight information corresponding to any traffic keyword in the recommended traffic list, wherein the insight information is used to provide data description for the traffic keyword; The traffic keyword and the insight information corresponding to the traffic keyword are associated and displayed.

10. The method according to any one of claims 1 to 8, characterized in that: After determining a promotional commodity object in the at least one commodity object, the method further includes: Obtaining a promotion bid corresponding to the promotion product object; Based on the promotion bid, a promotion delivery plan and a promotion traffic method corresponding to the promotion commodity object are generated; The promotion plan, promotion traffic mode and promotion commodity object are displayed in association.

11. The method according to claim 10, characterized in that After the promotion delivery plan, the promotion traffic mode and the promotion commodity object are displayed in association, the method further includes: Obtaining an estimated promotional flow corresponding to the promotional product object; When the estimated promotion traffic does not meet the preset traffic threshold, a modification suggestion corresponding to the promotion delivery plan is generated.

12. The method according to any one of claims 1 to 8, characterized in that: After determining a promotional commodity object in the at least one commodity object, the method further includes: Based on the recommended traffic list, generating keywords corresponding to each traffic keyword; Based on the keyword, generating promotion suggestion information of the promoted product object, wherein the promotion suggestion information includes at least one of the following: picture suggestion information of the promoted product object, text suggestion information of the promoted product object, and video suggestion information of the promoted product object; Based on the promotion suggestion information, promotion content corresponding to the promoted product object is generated.

13. A device for determining a promotional commodity object, characterized in that: include: A first acquisition module is used to acquire object information corresponding to at least one commodity object in a preset store; A first determination module is used to determine a recommended traffic list based on the object information corresponding to each of the at least one commodity objects, wherein the recommended traffic list includes a plurality of traffic keywords adapted to the preset store and sorting information of the plurality of traffic keywords; The first processing module is used to determine a promotion commodity object from the at least one commodity object based on the recommended traffic list.

14. A computing platform, characterized in that: include: A memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the method of any one of claims 1 to 12.

15. A computer storage medium, characterized in that Used to store a computer program, which enables a computer to implement the method of any one of claims 1 to 12 when executed.

16. A computer program product, characterized in that include: A computer-readable storage medium storing computer instructions, when the computer instructions are executed by one or more processors, causes the one or more processors to execute the steps in any one of the methods of claims 1-12.

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