A message push method and its device, equipment and storage medium

Abstract

The embodiments of the present application provide a message push method and its apparatus, device, and storage medium, wherein consumption information of a contact is obtained based on the contact information of a user to be recommended; a shopping preference list of the contact is determined based on the consumption information of the contact; wherein the shopping preference list of the contact is used to indicate the contact's preference for each category of goods; a message to be pushed is determined based on the shopping preference list of the contact, and the message to be pushed is sent to the user to be recommended.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of information processing technology, and relate to but are not limited to a message push method and its apparatus, device, and storage medium. Background Art

[0002] In the related art, many recommendation systems have the problem of cold start. Because the user is a new user, very little user information can be obtained, and even the usefulness of the obtained user information is almost zero. The general recommendation effect is still mainly to recommend popular products, and it is impossible to make personalized recommendations for users, or the recommendation effect based on limited user information is very poor. Moreover, popular recommendations cannot meet the personalized needs of users, and there is no feature of thousands of faces for thousands of people. Some popular products occupy the popular positions for a long time, resulting in poor recommendation effects. Summary of the invention

[0003] In view of this, an embodiment of the present application provides a message push method and its device, equipment, and storage medium.

[0004] The technical solution of the embodiment of the present application is implemented as follows:

[0005] The present application provides a message push method, the method comprising:

[0006] Obtain consumption information of the contact according to the contact information of the user to be recommended;

[0007] Determining a shopping preference list of the contact according to the consumption information of the contact; wherein the shopping preference list of the contact is used to indicate the preference of the contact for each category of goods;

[0008] The message to be pushed is determined according to the shopping preference list of the contact, and the message to be pushed is sent to the user to be recommended.

[0009] In the above method, the method further comprises:

[0010] Acquire contact information corresponding to a user to be recommended who registers a preset application for the first time; wherein the user to be recommended is a user who registers a preset application for the first time, and the contact information is relevant information of contacts owned by the user to be recommended.

[0011] In the above method, the step of obtaining the consumption information of the contact person according to the contact information of the user to be recommended includes:

[0012] According to the contact information of the user to be recommended, statistics are collected on the shopping webpage browsing data of the contact; wherein the contact information is obtained according to the input authorization instruction;

[0013] The consumption information of the contact is generated according to the data of the contact browsing the shopping webpage.

[0014] In the above method, determining the shopping preference list of the contact according to the consumption information of the contact includes:

[0015] Determine a commodity category set corresponding to the consumption information of the contact;

[0016] Determine the purchase probability corresponding to each category of goods in the commodity category set according to the consumption information of the contact;

[0017] A shopping preference list of the contact is determined according to the shopping probability.

[0018] In the above method, determining the shopping probability corresponding to each category of goods in the commodity category set according to the consumption information of the contact person includes:

[0019] Determine the total number of shopping behaviors of the contact for the commodities in the commodity category set according to the consumption information of the contact; wherein the shopping behaviors at least include purchasing commodities, browsing commodities or paying attention to commodities;

[0020] For each category of goods in the commodity category set, determining the number of single shopping behaviors of the contact person for each category of goods;

[0021] The shopping probability corresponding to each category of the commodity in the commodity category set is determined according to the total number of shopping behaviors and the number of single shopping behaviors.

[0022] In the above method, determining the shopping preference list of the contact according to the shopping probability includes:

[0023] The shopping probabilities are arranged in descending order to obtain a shopping preference list of the contacts.

[0024] In the above method, determining the message to be pushed according to the shopping preference list of the contact, and sending the message to be pushed to the user to be recommended includes:

[0025] In the shopping preference list of the contact, determining a commodity category corresponding to a probability that a shopping probability satisfies a preset probability threshold;

[0026] According to the commodity category corresponding to the probability of satisfying the preset probability threshold, the message to be pushed is determined and sent to the user to be recommended.

[0027] The embodiment of the present application provides a message push device, the device comprising: a first acquisition module, a first determination module and a first output module, wherein:

[0028] The first acquisition module is used to acquire the consumption information of the contact according to the contact information of the user to be recommended;

[0029] The first determination module is used to determine the shopping preference list of the contact according to the consumption information of the contact; wherein the shopping preference list of the contact is used to indicate the preference of the contact for each category of goods;

[0030] The first output module is used to determine the message to be pushed according to the shopping preference list of the contact, and send the message to be pushed to the user to be recommended.

[0031] An embodiment of the present application provides a data storage device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, the steps in the above-mentioned message push method are implemented.

[0032] An embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned message push method are implemented.

[0033] The embodiment of the present application provides a message push method and its apparatus, device, and storage medium, wherein, first, the consumption information of the contact is obtained according to the contact information of the user to be recommended; then, the shopping preference list of the contact is determined according to the consumption information of the contact; wherein the shopping preference list of the contact is used to indicate the contact's preference for each category of goods; finally, the message to be pushed is determined according to the shopping preference list of the contact, and the message to be pushed is sent to the user to be recommended; in this way, by obtaining the mobile phone contact list, based on a large amount of historical data such as browsing records and purchase records of the user's mobile phone contacts, the preferences of the contacts are determined, and then the messages to be pushed to the user are determined according to the goods preferred by the contacts, and personalized recommendations for goods are made to a certain extent, thereby improving the user's shopping experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a schematic diagram of the implementation process of the message push method of the embodiment of the present application;

[0035] Figure 2 This is another implementation flow diagram of the message push method of the embodiment of the present application;

[0036] Figure 3 This is a schematic diagram of the implementation process of the message push method of the embodiment of the present application;

[0037] Figure 4 This is a schematic diagram of the structure of the message push device according to an embodiment of the present application;

[0038] Figure 5 It is a schematic diagram of the composition structure of the electronic device according to the embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the specific technical solution of the invention will be further described in detail below in conjunction with the drawings in the embodiments of the present application. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0040] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0041] The electronic device may be implemented in various forms. For example, the electronic device described in this application may include mobile electronic devices such as personal digital assistants (PDAs), navigation devices, wearable devices, and fixed electronic devices such as digital TVs and desktop computers.

[0042] The following description will be made by taking mobile electronic devices as an example, and those skilled in the art will understand that, in addition to components specifically used for mobile purposes, the construction according to the embodiments of the present application can also be applied to fixed-type electronic devices.

[0043] The present application embodiment provides a message push method, Figure 1 Schematic diagram of the implementation process of the message push method of the present application embodiment, such as Figure 1 As shown, the method comprises the following steps:

[0044] Step S101, obtaining consumption information of the contact according to the contact information of the user to be recommended.

[0045] Here, the contact information is the contact information in the electronic device such as the mobile phone of the user to be recommended, and the contact information may include: the contact name, gender, telephone number, etc. The step S101 can be understood as counting the data of the contact browsing the shopping webpage according to the contact information, and then analyzing the data to obtain the consumption information of the contact. The consumption information includes: the record of the contact purchasing goods, the record of browsing goods, the category of goods concerned, the favorite store, etc. The contact information is obtained by obtaining the contact information corresponding to the registration information of the first registration of the preset application, wherein the registration information at least includes the login password and the login account; that is, in this embodiment, the contact information refers to the contact information in the mobile phone of the new user, and the so-called new user is the user who logs in to a certain application for the first time, such as user A logs in to a shopping application (Application, app) for the first time. At this time, all the contact information in the mobile phone of user A is obtained, and then the shopping preferences of these contacts are analyzed, and targeted recommendations are made for user A according to the shopping preferences of these contacts.

[0046] Here, the registration information at least includes a login password and a login account.

[0047] Step S102: determining the shopping preference list of the contact according to the consumption information of the contact.

[0048] Here, the shopping preference list of the contact is used to indicate the contact's preference for each category of goods. The goods listed in the front of the shopping preference list of the contact are the categories of goods that the contact has purchased more times.

[0049] Step S103: determining the message to be pushed according to the shopping preference list of the contact, and sending the message to be pushed to the user to be recommended.

[0050] Here, according to the products ranked at the front in the shopping preference list of the contacts, a push message of the products is generated, and the push message is sent to the users to be recommended who have the contact information.

[0051] In this embodiment, by determining the purchase preferences of the user's contacts, messages are pushed to the user who logs in to the preset application for the first time based on the purchase preferences of the user's contacts. Because "birds of a feather flock together", the shopping preferences of the user's contacts can reflect the user's shopping preferences to a certain extent. Therefore, the purchase preferences of the user's contacts are pushed to the user who logs in to the preset application for the first time based on the purchase preferences of the user's contacts, which can provide the user with personalized product recommendations and improve the user's shopping experience.

[0052] The present application embodiment provides a message push method, Figure 2 This is another implementation flow diagram of the message push method of the embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0053] Step S201, collecting statistics of the shopping webpage browsing data of the contact according to the contact information.

[0054] Here, the contact information is obtained according to the authorization instruction input by the user to be recommended. The step S201 can be understood as counting the categories of goods in the shopping web pages browsed by these contacts, or the categories of goods purchased, etc., according to the contact information in the mobile phone of the user to be recommended. The contact information is obtained according to the input authorization instruction, and the authorization instruction is used to authorize the disclosure of the contact information. In other words, it is necessary to first send a request to the user to obtain the contact information. If the user agrees to disclose the contact information in the mobile phone, then enter the authorization instruction, so that the contact information in the user's mobile phone can be obtained.

[0055] Step S202: generating consumption information of the contact according to the data of the contact browsing shopping web pages.

[0056] Here, step S202 may be understood as determining which categories of goods the contact has purchased, which goods the contact has paid attention to, which goods the contact has browsed, etc., by analyzing the data of the contact browsing shopping web pages.

[0057] In this embodiment, the consumption information of the contact is obtained by counting the data of the shopping web pages browsed by the contact, thereby ensuring the richness and accuracy of the consumption information of the contact.

[0058] Step S203: determining a commodity category set corresponding to the consumption information of the contact.

[0059] Here, the categories of goods that the user has a tendency to buy or has already purchased are determined based on the consumption information of the contact, such as the purchase record, browsing record, followed goods and favorited stores of the contact.

[0060] Step S204: determining the shopping probability corresponding to each category of the commodity in the commodity category set according to the consumption information of the contact.

[0061] Here, the shopping probability of a product category is obtained by counting the total number of shopping behaviors of all users in a certain category j and the total number of shopping behaviors of all users in all categories.

[0062] Step S205: determining the shopping preference list of the contact according to the shopping probability.

[0063] Here, the shopping probabilities are arranged in descending order to obtain the shopping preference list of the contact. For example, if the shopping probability of the contact purchasing category A products is 0.9, which is the highest shopping probability, then category A products are arranged first in the shopping preference list.

[0064] Step S206, in the shopping preference list of the contact, determining the commodity category corresponding to the probability that the shopping probability meets a preset probability threshold.

[0065] Here, the preset probability threshold can be set according to the needs of the user. For example, if the preset probability threshold is set to 0.5, then step S206 can be understood as determining the product categories corresponding to the probabilities of shopping in the contact's shopping preference list that have a shopping probability greater than 0.5. For example, if the shopping probabilities of products in categories B, C, and D are all greater than 0.5, then find the products in these categories and generate push messages for the products in these categories.

[0066] Step S207, determining and sending the message to be pushed to the user to be recommended according to the commodity category corresponding to the probability of satisfying the preset probability threshold.

[0067] Here, push messages corresponding to product categories with shopping probabilities greater than 0.5 are sent to the recommended users who register the current application for the first time.

[0068] In this embodiment, by determining the shopping preferences of the new user's contacts, push messages of corresponding products are pushed to the new user, so as to make personalized product recommendations for the new user instead of just recommending popular products, thereby improving the user's shopping experience.

[0069] In other embodiments, the step S204, i.e., determining the purchase probability corresponding to each category of goods in the commodity category set according to the consumption information of the contact, can be implemented by the following steps:

[0070] Step S241, determining the total number of shopping behaviors of the contact for the commodities in the commodity category set according to the consumption information of the contact.

[0071] Here, the shopping behavior at least includes purchasing a product, browsing a product, or paying attention to a product. The step S241 can be understood as, assuming that the new user has n contacts and m categories, the number of times a user i browses, purchases, pays attention to, and other shopping behaviors in a certain category j is represented as x. ij , the total number of shopping behaviors is the sum of the number of shopping behaviors of all contacts in all categories.

[0072] Step S242, for each category of goods in the commodity category set, determine the number of single shopping behaviors of the contact for each category of goods.

[0073] Here, the number of single shopping behaviors is the sum of the number of shopping behaviors of all contacts under a certain category j.

[0074] Step S243, determining the shopping probability corresponding to each category of the commodity in the commodity category set according to the total number of shopping behaviors and the number of single shopping behaviors.

[0075] Here, the purchase probability of a category of goods is obtained by dividing the number of the single shopping behavior by the total number of shopping behaviors.

[0076] In this embodiment, the purchase probability of a certain category of goods is determined by determining the total number of times all contacts purchase all categories of goods and the number of times all contacts purchase a certain category of goods. The purchase probability of all categories of goods that the contacts like can be accurately obtained, and personalized recommended goods are provided to the users corresponding to these contacts in turn.

[0077] The embodiment of the present application provides a message push method, which obtains the user's contact list by obtaining the corresponding permissions of the mobile phone contacts, and queries the user's browsing history, purchase history and other historical information in the commodity system through the contact's mobile phone number to obtain the shopping preference ranking list in the user's contact list. For example, many of the mobile phone contacts of a sports enthusiast often buy some sports shoes, jerseys and other sports goods in the system. In this way, when this user comes to the platform as a new user, the cold start recommendation system can push some sports goods related products and make personalized recommendations.

[0078] Figure 3 Schematic diagram of the implementation process of the message push method of the present application embodiment, such as Figure 3 As shown, the method comprises the following steps:

[0079] Step S301, obtaining the contact authority of the user's mobile phone and the user's mobile phone contact list, and querying the historical records of the mobile phone contacts in the commodity system.

[0080] Here, the user is a new user of a certain application program, that is, when the new user enters the app, the user's mobile phone contact list is obtained by obtaining the permission of the user's mobile phone contacts.

[0081] Step S302, based on the historical records of the mobile phone contacts, a ranked list of the shopping preferences of the mobile phone contacts is obtained through big data and machine learning.

[0082] Here, based on the data in the cold start user's contact list, the product browsing records, order records, favorites, and attention records of all contacts are queried in the product system. The frequency of a user's shopping behavior in a certain category is simply used as the basis for judging the user's shopping preference. Assuming that a new user has n contacts and m categories, the number of times a user i browses, buys, and follows a certain category j is represented as x. ij , then the shopping behavior frequency of a certain category j is in, represents the total number of shopping behaviors of all contacts in a certain category j, Represents the total number of shopping behaviors of all contacts in all categories.

[0083] By f j Statistical calculations are performed according to f j By arranging in descending order, the shopping preference categories of the contacts (i.e., the shopping preference list) can be obtained.

[0084] Step S303: When a new user enters the app platform, personalized recommendations are made based on the order of the commodities in the shopping preference list.

[0085] Here, when a new user enters the app, that is, in the case of a user cold start, the corresponding products ranked in the top few preferred categories are obtained according to the acquired shopping preference list for personalized recommendations.

[0086] Since obtaining the shopping preferences of the user's mobile phone contacts can reflect the user's shopping preferences to a certain extent, based on the above steps, when the user cold starts, relevant personalized recommendations can be made based on the shopping preferences of the mobile phone contacts.

[0087] In this embodiment, the user's mobile phone contacts are obtained by obtaining permission to access the mobile phone contact list, and based on a large amount of historical data such as browsing records and purchase records of the user's mobile phone contacts, the user's shopping preference ranking list is obtained through big data and machine learning and other methods. When a new user enters the app, that is, in the case of a cold start, through this optimization, personalized product recommendations can be made to a certain extent, thereby improving the user's shopping experience.

[0088] An embodiment of the present application further provides a message push device, which includes the modules included, and the sub-modules and units included in each module, which can be implemented by a processor in an electronic device; of course, it can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA), etc.

[0089] This embodiment provides a message push device, Figure 4 Schematic diagram of the structure of the message push device according to the embodiment of the present application. Figure 4 As shown, the device 400 includes: a first acquisition module 401, a first determination module 402 and a first output module 403, wherein:

[0090] The first acquisition module 401 is used to acquire the consumption information of the contact according to the contact information of the user to be recommended;

[0091] The first determination module 402 is used to determine the shopping preference list of the contact according to the consumption information of the contact; wherein the shopping preference list of the contact is used to indicate the preference of the contact for each category of goods;

[0092] The first output module 403 is used to determine the message to be pushed according to the shopping preference list of the contact, and send the message to be pushed to the user to be recommended.

[0093] It should be noted that the description of the above device embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the device embodiment of the present application, please refer to the description of the method embodiment of the present application for understanding.

[0094] It should be noted that in the embodiment of the present application, if the above-mentioned message push method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a message push device (which can be a terminal, a server, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0095] Correspondingly, an embodiment of the present application further provides a readable storage medium, on which a message push program is stored. When the message push program is executed by a processor, the steps of the above-mentioned message push method are implemented.

[0096] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, the steps in the above-mentioned message push method are implemented.

[0097] The description of the above electronic device and storage medium embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the message push device and storage medium embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0098] It should be noted that Figure 5 is a schematic diagram of a hardware entity of an electronic device in an embodiment of the present application, such as Figure 5 As shown, the hardware entity of the device 500 includes: a processor 501, a communication interface 502 and a memory 503, wherein

[0099] Processor 501 generally controls the overall operation of device 500 .

[0100] The communication interface 502 enables the device to communicate with other terminals or servers through a network.

[0101] The memory 503 is configured to store instructions and applications executable by the processor 501, and can also cache data to be processed or processed by the processor 501 and various modules in the device 500 (for example, audio data, voice communication data, and video communication data), which can be implemented through flash memory (FLASH) or random access memory (Random Access Memory, RAM).

[0102] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0103] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0104] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0105] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0106] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately configured as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0107] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, etc., various media that can store program codes.

[0108] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer or a server, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0109] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A message push method, characterized in that: The method comprises: Obtain consumption information of the contact according to the contact information of the user to be recommended; Determine a commodity category set corresponding to the consumption information of the contact; the commodity category set includes categories of commodities that the contact has a tendency to purchase; Determine the purchase probability corresponding to each category of goods in the commodity category set according to the consumption information of the contact; Determining the shopping preference list of the contact according to the shopping probability; wherein the shopping preference list of the contact is used to indicate the preference of the contact for each category of goods; The message to be pushed is determined according to the shopping preference list of the contact, and the message to be pushed is sent to the user to be recommended.

2. The method according to claim 1, characterized in that The method further comprises: Acquire contact information corresponding to a user to be recommended who registers a preset application for the first time; wherein the user to be recommended is a user who registers a preset application for the first time, and the contact information is relevant information of contacts owned by the user to be recommended.

3. The method according to claim 1, characterized in that The step of obtaining the consumption information of the contact person according to the contact information of the user to be recommended includes: According to the contact information of the user to be recommended, statistics are collected on the shopping webpage browsing data of the contact; wherein the contact information is obtained according to the input authorization instruction; The consumption information of the contact is generated according to the data of the contact browsing the shopping webpage.

4. The method according to claim 1, characterized in that Determining the shopping probability corresponding to each category of goods in the commodity category set according to the consumption information of the contact person includes: Determine the total number of shopping behaviors of the contact for the commodities in the commodity category set according to the consumption information of the contact; wherein the shopping behaviors at least include purchasing commodities, browsing commodities or paying attention to commodities; For each category of goods in the commodity category set, determining the number of single shopping behaviors of the contact person for each category of goods; The shopping probability corresponding to each category of the commodity in the commodity category set is determined according to the total number of shopping behaviors and the number of single shopping behaviors.

5. The method according to claim 1, characterized in that Determining the shopping preference list of the contact according to the shopping probability includes: The shopping probabilities are arranged in descending order to obtain a shopping preference list of the contacts.

6. The method according to claim 4 or 5, characterized in that Determining a message to be pushed according to the shopping preference list of the contact, and sending the message to be pushed to the user to be recommended, including: In the shopping preference list of the contact, determining a commodity category corresponding to a probability that a shopping probability satisfies a preset probability threshold; According to the commodity category corresponding to the probability of satisfying the preset probability threshold, the message to be pushed is determined and sent to the user to be recommended.

7. A message push device, characterized in that: The device comprises: a first acquisition module, a first determination module and a first output module, wherein: The first acquisition module is used to acquire the consumption information of the contact according to the contact information of the user to be recommended; The first determination module is used to determine a commodity category set corresponding to the consumption information of the contact; the commodity category set includes categories of commodities that the contact has a tendency to purchase; based on the consumption information of the contact, determine the shopping probability corresponding to each category of commodities in the commodity category set; and determine the shopping preference list of the contact based on the shopping probability; wherein the shopping preference list of the contact is used to indicate the preference of the contact for each category of commodities; The first output module is used to determine the message to be pushed according to the shopping preference list of the contact, and send the message to be pushed to the user to be recommended.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the program, the steps in the message pushing method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the message pushing method described in any one of claims 1 to 6 are implemented.

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