Information recommendation method, device, storage medium and electronic device

Abstract

This specification discloses an information recommendation method, device, storage medium and electronic device. The information recommendation method includes: in response to the interactive operations performed by each user on the target business to be released in different forms of business recommendation information, obtaining the interactive data of each user for each business recommendation information as the interactive data corresponding to the various business recommendation information; in response to the activation operation performed by each user on the target business after the target business is released, obtaining the activation data corresponding to the target business; based on the activation data and the interactive data corresponding to the various business recommendation information, determining the information recommendation strategy for the different forms of business recommendation information, and performing information recommendation according to the information recommendation strategy. This solution can accurately attribute the business recommendation information based on the interactive data and the activation data, further improving the accuracy of subsequent information recommendation.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an information recommendation method, device, storage medium and electronic device. Background Art

[0002] In the era of digital marketing, information recommendation (advertising) is no longer limited to traditional media, but widely covers multiple channels such as the Internet, mobile applications, and social media. This information recommendation method has greatly improved the publicity effect of information and provided a solid user base for the business products promoted by the recommended information.

[0003] However, due to the complexity of the above multi-channel and multi-touchpoint information flow delivery method, it is difficult to accurately measure the contribution of each channel or touchpoint to the end-user conversion, resulting in low accuracy in subsequent information recommendations, further increasing the cost of information recommendation.

[0004] Therefore, how to accurately recommend information is a problem that needs to be solved urgently. Summary of the invention

[0005] This specification provides an information recommendation method, device, storage medium and electronic device to partially solve the above problems existing in the prior art.

[0006] This manual adopts the following technical solutions:

[0007] This specification provides an information recommendation method, including:

[0008] In response to interaction operations performed by each user on different forms of business recommendation information of a target business to be released, interaction data of each user on each form of business recommendation information is obtained as interaction data corresponding to various business recommendation information, wherein, for each form of business recommendation information, the interaction data corresponding to the form of business recommendation information includes user identification information corresponding to each user who performs interaction operations on the form of business recommendation information;

[0009] In response to activation operations performed by each user on the target service after the target service is released, acquiring activation data corresponding to the target service, wherein the activation data includes user identification information corresponding to each user who performs the activation operation;

[0010] Based on the activation data and the interaction data corresponding to the various types of service recommendation information, an information recommendation strategy for different forms of service recommendation information is determined, and information recommendation is performed according to the information recommendation strategy.

[0011] Optionally, the step of determining information recommendation strategies for different forms of service recommendation information based on the activation data and the interaction data corresponding to the various service recommendation information specifically includes:

[0012] For each type of service recommendation information, determining, according to the activation data and the interaction data corresponding to the type of service recommendation information, a contribution of the type of service recommendation information to the activation operation of the target service, as the contribution corresponding to the type of service recommendation information;

[0013] According to the contribution degree corresponding to each type of business recommendation information, an information recommendation strategy for different forms of business recommendation information is determined.

[0014] Optionally, the step of determining, for each type of service recommendation information, a contribution of the type of service recommendation information to the activation operation performed on the target service according to the activation data and the interaction data corresponding to the type of service recommendation information, as the contribution corresponding to the type of service recommendation information, specifically includes:

[0015] determining, according to the activation data, the number of users who perform activation operations on the target service as a first number of users, and

[0016] For each type of service recommendation information, according to the user identification information included in the activation data and the user identification information included in the service recommendation information, determine the number of users who perform an interactive operation on the service recommendation information and perform an activation operation on the target service as the second number of users;

[0017] A contribution degree corresponding to the service recommendation information is determined according to the number of the first users and the number of the second users.

[0018] Optionally, the interactive operation includes: browsing operation and reservation operation;

[0019] The step of obtaining, in response to the interaction operations performed by each user on the target business recommendation information in different forms to be released, the interaction data of each user on each type of business recommendation information as the interaction data corresponding to the various types of business recommendation information specifically includes:

[0020] For each type of service recommendation information, in response to a browsing operation performed by each user on the service recommendation information, determining browsing data corresponding to the service recommendation information, and, in response to a reservation operation performed by each user on the service recommendation information, determining reservation data corresponding to the service recommendation information;

[0021] The interactive data corresponding to the service recommendation information is determined according to the browsing data and the reservation data.

[0022] Optionally, the step of determining information recommendation strategies for different forms of service recommendation information based on the activation data and the interaction data corresponding to the various service recommendation information specifically includes:

[0023] for each type of service recommendation information, determining, according to the activation data and the interaction data corresponding to the type of service recommendation information, a contribution of the type of service recommendation information to the activation operation of the target service as a first contribution corresponding to the type of service recommendation information, and

[0024] Determining, according to the browsing data corresponding to the service recommendation information and the preset weight corresponding to the browsing data, the reservation data corresponding to the service recommendation information and the preset weight corresponding to the reservation data, the contribution of the service recommendation information to the activation operation of the target service as the second contribution corresponding to the service recommendation information;

[0025] Determining an actual contribution degree corresponding to the type of business recommendation information according to the first contribution degree corresponding to the type of business recommendation information and the second contribution degree corresponding to the type of business recommendation information;

[0026] According to the actual contribution degree corresponding to each type of business recommendation information, an information recommendation strategy for different forms of business recommendation information is determined.

[0027] Optionally, each service recommendation information corresponding to the target service to be released includes service recommendation information under several information recommendation channels, and for each information recommendation channel, the service recommendation information under the information recommendation channel includes at least one form of service recommendation information;

[0028] The step of determining the information recommendation strategy for different forms of service recommendation information based on the activation data and the interaction data corresponding to the various service recommendation information specifically includes:

[0029] Based on the activation data and the interaction data corresponding to each piece of service recommendation information, an information recommendation strategy for each piece of service recommendation information under at least part of the information recommendation channels is determined.

[0030] Optionally, the method further comprises:

[0031] For each type of service recommendation information, according to the user identification information contained in the interaction data corresponding to the service recommendation information, determine the users who perform the interaction operation on the service recommendation information as the interested users;

[0032] According to the user identification information included in the activation data, interested users who have not performed an activation operation on the target service are determined among the interested users, and activation prompt information is sent to the interested users who have not performed an activation operation on the target service.

[0033] This specification provides an information recommendation device, including:

[0034] An interaction module, configured to obtain, in response to interaction operations performed by each user on different forms of business recommendation information of a target business to be released, interaction data of each user on each form of business recommendation information as interaction data corresponding to various forms of business recommendation information, wherein, for each form of business recommendation information, the interaction data corresponding to the form of business recommendation information includes user identification information corresponding to each user who performs interaction operations on the form of business recommendation information;

[0035] an activation module, configured to obtain activation data corresponding to the target service in response to activation operations performed by each user on the target service after the target service is released, wherein the activation data includes user identification information corresponding to each user who performs the activation operation;

[0036] The recommendation module is used to determine information recommendation strategies for different forms of business recommendation information based on the activation data and the interaction data corresponding to the various business recommendation information, and perform information recommendation according to the information recommendation strategies.

[0037] This specification provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned information recommendation method is implemented.

[0038] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned information recommendation method when executing the program.

[0039] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0040] In the information recommendation method provided in the present specification, in response to the interactive operations performed by each user on different forms of business recommendation information of a target business to be released, the interactive data of each user on each type of business recommendation information is obtained as the interactive data corresponding to the various business recommendation information; in response to the activation operations performed by each user on the target business after the target business is released, the activation data corresponding to the target business is obtained; based on the activation data and the interactive data corresponding to the various business recommendation information, the information recommendation strategy for the different forms of business recommendation information is determined, and information recommendation is performed according to the information recommendation strategy.

[0041] It can be seen from the above method that this scheme can obtain the interaction data generated when the user interacts with the business recommendation information before the target business is released, and the activation data generated when the user performs business operations on the target business after the target business is released. In this way, based on the acquired data, different forms of business recommendation information can be accurately attributed to obtain recommendation strategies for different types of business recommendation information when making subsequent information recommendations. Compared with the current method of randomly delivering the business recommendation information, this scheme can determine the information recommendation strategy based on the data generated during the business release process and perform accurate information recommendation, thereby improving the accuracy of information recommendation, avoiding invalid information recommendation, and further reducing the cost of information recommendation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings:

[0043] Figure 1 A flowchart of an information recommendation method provided in this specification;

[0044] Figure 2 A schematic diagram of an information attribution process provided in this specification;

[0045] Figure 3 A schematic diagram of an information recommendation device provided in this specification;

[0046] Figure 4 A method corresponding to the Figure 1 Schematic diagram of electronic equipment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0048] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0049] Figure 1 A flowchart of an information recommendation method provided in this specification includes the following steps:

[0050] S101: In response to interactive operations performed by various users on different forms of business recommendation information of a target business to be released, interactive data of each user for each form of business recommendation information is obtained as interactive data corresponding to various forms of business recommendation information, wherein, for each form of business recommendation information, the interactive data corresponding to the form of business recommendation information includes user identification information corresponding to each user who performs interactive operations on the form of business recommendation information.

[0051] Advertising attribution is a key process in information recommendation, which aims to determine which business recommendation information or channels have an impact on users' specific behaviors (such as clicks, downloads, purchases, etc.) and quantify the extent of this impact. Advertising attribution is crucial for advertisers because it helps understand which marketing strategies are effective and which need to be improved, thereby optimizing advertising spending and improving return on investment.

[0052] Especially in the publishing industry of services such as games, before the service is officially launched, it is often widely promoted through channels such as information flow advertising and social media to encourage users to make reservations, thereby collecting interest data of potential users. When the service is officially launched, the previously collected data is used to accurately recommend the service to users. It can be seen that whether the advertising attribution can be accurate further affects the accuracy of subsequent information recommendations.

[0053] Based on this, this specification provides an information recommendation method, which collects and analyzes data before the service is released (reservation stage) and after the service is released (first release stage), so as to obtain an accurate information recommendation strategy.

[0054] In this specification, the execution subject for implementing an information recommendation method may be a designated device such as a server. For the sake of ease of description, the following will only take the server as an execution subject as an example to illustrate a service execution method provided in this specification.

[0055] Before the target service is released, the server can send the recommended information to the user. In actual applications, the server can recommend service recommendation information for the target service to be released to the user through multiple channels, and there can be at least one form of service recommendation information under each information recommendation channel.

[0056] Among them, the above-mentioned target businesses may include business procedures such as games, finance, catering, services, etc., and the information recommendation channels may include different social media platforms, news consulting platforms, video application platforms, and search engines, etc. The different forms of business recommendation information may include but are not limited to differences in content, format, picture, music, etc.

[0057] For each type of business data, when a user performs an interactive operation on the business recommendation information, the server can respond to the interactive operation performed by each user on the business recommendation information, obtain the interactive data of each user on the business recommendation information, and then obtain the interactive data of each user on each type of business recommendation information.

[0058] For each type of business recommendation information, the interaction data corresponding to the business recommendation information may include user identification information corresponding to each user who performs interactive operations on the business recommendation information. The user identification information is mainly used to determine which specific user has interacted with the business recommendation information, and may include one or more of the user's identity ID, user mobile phone number, business account number, and device ID of the terminal device used by the user.

[0059] In addition, the above-mentioned interaction data may also include: the operation type identifier of the user performing the interaction operation, the channel information of the information recommendation channel corresponding to the business recommendation information, the unique identification identifier corresponding to the business recommendation information, and the number of users who perform each interaction operation on the business recommendation information, etc.

[0060] In this specification, the interactive operations between users and business recommendation information may include browsing operations and reservation operations, wherein browsing operations mainly include the user's browsing behavior and clicking behavior on the business recommendation information, and reservation operations may include the operations performed by the user on the reservation controls (such as buttons, sliders) in the business recommendation information and the operations of the user filling in the reservation information in the reservation form of the business recommendation information.

[0061] For each type of business recommendation information, the server can determine the browsing data corresponding to the business recommendation information in response to the browsing operations performed by each user on the business recommendation information, and determine the reservation data corresponding to the business recommendation information in response to the reservation operations performed by each user on the business recommendation information. The browsing data and the reservation data together constitute the interaction data between the user and each type of business recommendation information, and the two can be distinguished by the operation type identifier of the user performing the interactive operation (such as the browsing operation identifier and the reservation operation identifier).

[0062] S102: In response to activation operations performed by each user on the target service after the target service is released, activation data corresponding to the target service is acquired, wherein the activation data includes user identification information corresponding to each user who performs the activation operation.

[0063] After the target service is released, the server can obtain activation data corresponding to the target service in response to activation operations performed by each user on the target service after the target service is released. There are many ways for a user to perform an activation operation on a target service. For example, when a user opens a service program of the target service, it can be considered that the user has performed an activation operation on the target service. For another example, when a user performs a login process registration operation on the service program, it can be considered that the user has performed an activation operation on the target service.

[0064] The activation data may include user identification information corresponding to each user who performs activation operations on the business program (such as the user's identity ID, user mobile phone number, business account number, and device ID of the terminal device used by the user). Of course, it may also include the number of users who perform activation operations on the target business within a period of time after the business is released.

[0065] After obtaining the activation data and interaction data, the server can further attribute information based on the activation data and interaction data, thereby determining information recommendation strategies for different forms of business recommendation information. For ease of understanding, this specification provides a schematic diagram of the information attribution process, such as Figure 2 shown.

[0066] Figure 2 This is a schematic diagram of the information attribution process provided in this manual.

[0067] Among them, the server can receive the user's click data on each business recommendation information in real time based on the click callback Web service of the SpringBoot scaffolding, and store it in MySQL. In addition, based on the SpringBoot scaffolding scheduled data pulling service, the xxl-job scheduler is used to pull the user's appointment data from the platform corresponding to each information recommendation channel through the HTTP interface at preset time intervals, and store it in MySQL.

[0068] Afterwards, the server can bind each user to each business recommendation information based on the user identification identifier and the identification identifier of the business recommendation information in the stored interaction data, obtain the binding result of [user / device ID=>reservation advertisement ID], and store it in the Redis cache.

[0069] After the target service is released, the server can obtain the user's activation data for the target service, and then read the previous interaction data and the above binding relationship from the Redis cache to perform subsequent information attribution.

[0070] S103: Based on the activation data and the interaction data corresponding to the various types of service recommendation information, determine information recommendation strategies for different forms of service recommendation information, and perform information recommendation according to the information recommendation strategies.

[0071] The server may determine information recommendation strategies for different forms of service recommendation information based on the activation data and interaction data corresponding to various service recommendation information, and perform information recommendation according to the information recommendation strategies.

[0072] Specifically, for each type of service recommendation information, the server may determine the contribution of the service recommendation information to the activation operation of the target service according to the activation data and the interaction data corresponding to the service recommendation information as the contribution corresponding to the service recommendation information.

[0073] In an embodiment provided in the present specification, the server can determine the number of users who perform activation operations on the target business based on the activation data as the first number of users. At the same time, for each type of business recommendation information, the server can determine the number of users who perform interactive operations on the business recommendation information and perform activation operations on the target business based on the user identification information contained in the activation data and the user identification information contained in the business recommendation information as the second number of users.

[0074] Among them, for each type of service recommendation information, when the activation data and the interaction data corresponding to the service recommendation information contain the same user identification identifier, it means that the users corresponding to these same identification identifiers have performed an activation operation on the target service when performing an interaction operation on the service recommendation information.

[0075] The server may then determine the contribution level corresponding to the service recommendation information according to the number of first users and the number of second users.

[0076] For example, the server can determine the ratio of the first user volume to the second user volume, which can further reflect the proportion of users who activate the target service under the influence of the service recommendation information, and then determine the contribution corresponding to the service recommendation information based on the ratio.

[0077] Furthermore, in order to fully consider the different contributions of browsing operations and reservation operations to subsequent target services in actual situations, the server may determine a preset weight corresponding to the browsing operation and a preset weight corresponding to the reservation operation. In actual applications, the preset weight corresponding to the browsing operation may be smaller than the preset weight corresponding to the reservation operation, and the specific sizes of the preset weight corresponding to the browsing operation and the preset weight corresponding to the reservation operation may be set according to actual conditions, and this specification does not make specific limitations on this.

[0078] For each type of business recommendation information, the server determines the number of users who perform browsing operations on this type of business information based on the browsing data contained in the interaction data corresponding to this type of business recommendation information, as the third user quantity, and determines the number of users who perform reservation operations on this type of business information based on the reservation data contained in the interaction data corresponding to this type of business recommendation information, as the fourth user quantity.

[0079] The server can perform weighted summation based on the preset weights corresponding to the third user volume and browsing operations, and the preset weights corresponding to the fourth user volume and reservation operations, to obtain the reference user volume, and then determine the ratio of the reference user volume to the second user volume, and determine the contribution corresponding to the business recommendation information based on the ratio.

[0080] In actual applications, the user's interaction data on business recommendation information can also directly reflect the accuracy of the recommendation information. For example, for each form of business recommendation information, the more times the user clicks or makes reservations on the business recommendation information, the more accurate the business recommendation information is and the greater its contribution to the activation of the target business.

[0081] Based on this, in another embodiment provided in the present specification, for each type of business recommendation information, the server can determine the contribution of the business recommendation information to the activation operation of the target business based on the activation data and the interaction data corresponding to the business recommendation information, as the first contribution corresponding to the business recommendation information (please see above for details on the method for determining the contribution).

[0082] At the same time, the server can determine the contribution of this type of business recommendation information to the activation operation of the target business based on the browsing data corresponding to this type of business recommendation information and the preset weight corresponding to the browsing data, the reservation data corresponding to this type of business recommendation information and the preset weight corresponding to the reservation data, as the second contribution corresponding to this type of business recommendation information. Afterwards, the server can determine the actual contribution corresponding to this type of business recommendation information based on the first contribution corresponding to this type of business recommendation information and the second contribution corresponding to this type of business recommendation information.

[0083] In actual applications, the server can also determine the preset weight corresponding to the first contribution and the preset weight corresponding to the second contribution, and then perform weighted summation based on the first contribution and its corresponding preset weight and the second contribution and its corresponding preset weight to obtain the actual contribution corresponding to the business information.

[0084] Among them, the preset weight corresponding to the first contribution and the preset weight corresponding to the second contribution can be set according to actual conditions. In actual applications, the preset weight corresponding to the first contribution can be greater than the preset weight corresponding to the second contribution.

[0085] The server may determine the contribution (actual contribution) corresponding to each type of business recommendation information based on any of the above methods, and then the server may determine information recommendation strategies for different forms of business recommendation information according to the contribution corresponding to each type of business recommendation information.

[0086] In actual applications, there can be many ways to determine the information recommendation strategy for different forms of business recommendation information based on the contribution degree corresponding to each type of business recommendation information. For example, for each type of business recommendation information, the server can adjust its information recommendation strategy based on the contribution degree corresponding to the business information. The higher the contribution degree corresponding to the business recommendation information, the greater the recommendation amount for the business recommendation information in subsequent information recommendations, and vice versa.

[0087] For another example, the server can determine the proportion of the contribution of different business recommendation information to the total contribution of all business recommendation information based on the contribution corresponding to each business recommendation information. For each business recommendation information, the greater the proportion of the contribution of this business recommendation information to the total contribution of all business recommendation information, the greater the recommendation amount of information recommendation for this business recommendation information after the target business is released, and vice versa.

[0088] Of course, the server can also adjust its subsequent information recommendation strategy based on the actual delivery volume of the business recommendation information before the business is released and its corresponding contribution. For each type of business recommendation information, the greater the contribution of the business recommendation information or the proportion of its contribution to the overall contribution, and the smaller the actual recommendation volume before the business is released, the greater the recommendation volume of this type of business recommendation information when recommending information after the target business is released, and vice versa.

[0089] In addition, the server may also determine the information recommendation strategy (such as the recommendation quantity) corresponding to each type of business recommendation information under different information recommendation channels through the above method, and this specification does not make specific limitations on this.

[0090] Furthermore, in order to ensure that every user interested in the target service can receive the messages released by the target service in a timely manner, for each type of service recommendation information, the server can determine the users who perform interactive operations on the service recommendation information based on the user identification information contained in the interactive data corresponding to the service recommendation information, as the interested users, and then determine the interested users who have not performed activation operations on the target service among the interested users based on the user identification information contained in the activation data, and send activation prompt information to the interested users who have not performed activation operations on the target service but have performed interactive operations on the service recommendation information.

[0091] It can be seen from the above method that we can accurately track user behavior from business reservation to the first launch, achieve high performance and low latency attribution in the first launch period, and determine the information recommendation strategy for different business recommendation information based on accurate analysis of the data obtained at different stages, thereby ensuring the accuracy of subsequent information recommendations and further reducing the cost of information recommendation.

[0092] The above are one or more implementation information recommendation methods of this specification. Based on the same idea, this specification also provides a corresponding information recommendation device, such as Figure 3 shown.

[0093] Figure 3 A schematic diagram of an information recommendation device provided in this specification includes:

[0094] The interaction module 301 is used to obtain the interaction data of each user for each type of business recommendation information in response to the interaction operations performed by each user for the business recommendation information of different forms of the target business to be released, as the interaction data corresponding to the various business recommendation information, wherein for each type of business recommendation information, the interaction data corresponding to the business recommendation information includes the user identification information corresponding to each user who performs the interaction operation on the business recommendation information;

[0095] An activation module 302, configured to obtain activation data corresponding to the target service in response to activation operations performed by each user on the target service after the target service is released, wherein the activation data includes user identification information corresponding to each user who performs the activation operation;

[0096] The recommendation module 303 is used to determine information recommendation strategies for different forms of business recommendation information based on the activation data and the interaction data corresponding to the various business recommendation information, and perform information recommendation according to the information recommendation strategies.

[0097] Optionally, the recommendation module 303 is specifically used to determine, for each type of business recommendation information, the contribution of the business recommendation information to the activation operation performed on the target business based on the activation data and the interaction data corresponding to the business recommendation information, as the contribution corresponding to the business recommendation information; and determine the information recommendation strategy for different forms of business recommendation information based on the contribution corresponding to each type of business recommendation information.

[0098] Optionally, the recommendation module 303 is specifically used to determine, based on the activation data, the number of users who perform activation operations on the target business, as the first number of users, and for each type of business recommendation information, based on the user identification information contained in the activation data and the user identification information contained in the business recommendation information, determine the number of users who perform interactive operations on the business recommendation information and perform activation operations on the target business, as the second number of users; and determine the contribution corresponding to the business recommendation information based on the first number of users and the second number of users.

[0099] Optionally, the interactive operation includes: browsing operation and reservation operation;

[0100] The acquisition module 301 is specifically used to, for each type of business recommendation information, determine the browsing data corresponding to the business recommendation information in response to the browsing operations performed by each user on the business recommendation information, and to determine the reservation data corresponding to the business recommendation information in response to the reservation operations performed by each user on the business recommendation information; and determine the interaction data corresponding to the business recommendation information based on the browsing data and the reservation data.

[0101] Optionally, the recommendation module 303 is specifically used to, for each type of business recommendation information, determine the contribution of the business recommendation information to the activation operation being executed on the target business according to the activation data and the interaction data corresponding to the business recommendation information, as the first contribution corresponding to the business recommendation information, and determine the contribution of the business recommendation information to the activation operation being executed on the target business according to the browsing data corresponding to the business recommendation information and the preset weight corresponding to the browsing data, and the reservation data corresponding to the business recommendation information and the preset weight corresponding to the reservation data, as the second contribution corresponding to the business recommendation information; determine the actual contribution corresponding to the business recommendation information according to the first contribution corresponding to the business recommendation information and the second contribution corresponding to the business recommendation information; and determine the information recommendation strategy for different forms of business recommendation information according to the actual contribution corresponding to each type of business recommendation information.

[0102] Optionally, each service recommendation information corresponding to the target service to be released includes service recommendation information under several information recommendation channels, and for each information recommendation channel, the service recommendation information under the information recommendation channel includes at least one form of service recommendation information;

[0103] Optionally, the recommendation module 303 is specifically configured to determine, based on the activation data and the interaction data corresponding to each piece of service recommendation information, an information recommendation strategy for each piece of service recommendation information under at least part of the information recommendation channels.

[0104] Optionally, the recommendation module 303 is also used to, for each type of business recommendation information, determine the users who perform interactive operations on the business recommendation information as interested users based on the user identification information contained in the interactive data corresponding to the business recommendation information; determine the interested users who have not performed the activation operation on the target business among the interested users based on the user identification information contained in the activation data, and send activation prompt information to the interested users who have not performed the activation operation on the target business.

[0105] This specification also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 An information recommendation method is provided.

[0106] This manual also provides Figure 4 The one shown corresponds to Figure 1 A schematic diagram of the electronic device. Figure 4 As mentioned above, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 Of course, in addition to the software implementation, this specification does not exclude other implementations, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0107] For the improvement of a technology, it can be clearly distinguished whether it is a hardware improvement (for example, improvement of the circuit structure of diodes, transistors, switches, etc.) or a software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements of the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be implemented with a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming themselves, without having to ask chip manufacturers to design and make dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0108] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0109] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0110] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0111] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0112] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0115] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0116] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0117] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0118] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0119] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0121] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0122] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. An information recommendation method, characterized in that: include: In response to interaction operations performed by each user on different forms of business recommendation information of a target business to be released, interaction data of each user on each form of business recommendation information is obtained as interaction data corresponding to various business recommendation information, wherein, for each form of business recommendation information, the interaction data corresponding to the form of business recommendation information includes user identification information corresponding to each user who performs interaction operations on the form of business recommendation information; In response to activation operations performed by each user on the target service after the target service is released, acquiring activation data corresponding to the target service, wherein the activation data includes user identification information corresponding to each user who performs the activation operation; Based on the activation data and the interaction data corresponding to the various types of service recommendation information, an information recommendation strategy for different forms of service recommendation information is determined, and information recommendation is performed according to the information recommendation strategy.

2. The method according to claim 1, characterized in that The step of determining the information recommendation strategy for different forms of service recommendation information based on the activation data and the interaction data corresponding to the various service recommendation information specifically includes: For each type of service recommendation information, determining, according to the activation data and the interaction data corresponding to the type of service recommendation information, a contribution of the type of service recommendation information to the activation operation of the target service, as the contribution corresponding to the type of service recommendation information; According to the contribution degree corresponding to each type of business recommendation information, an information recommendation strategy for different forms of business recommendation information is determined.

3. The method according to claim 2, characterized in that The step of determining, for each type of service recommendation information, a contribution of the type of service recommendation information to the activation operation of the target service according to the activation data and the interaction data corresponding to the type of service recommendation information, as the contribution corresponding to the type of service recommendation information, specifically includes: determining, according to the activation data, the number of users who perform activation operations on the target service as a first number of users, and For each type of service recommendation information, according to the user identification information included in the activation data and the user identification information included in the service recommendation information, determine the number of users who perform an interactive operation on the service recommendation information and perform an activation operation on the target service as the second number of users; A contribution degree corresponding to the service recommendation information is determined according to the number of the first users and the number of the second users.

4. The method according to claim 1, characterized in that The interactive operation includes: browsing operation and reservation operation; The step of obtaining, in response to the interaction operations performed by each user on the target business recommendation information of different forms to be released, the interaction data of each user on each type of business recommendation information as the interaction data corresponding to the various types of business recommendation information specifically includes: For each type of service recommendation information, in response to a browsing operation performed by each user on the service recommendation information, determining browsing data corresponding to the service recommendation information, and, in response to a reservation operation performed by each user on the service recommendation information, determining reservation data corresponding to the service recommendation information; The interactive data corresponding to the service recommendation information is determined according to the browsing data and the reservation data.

5. The method according to claim 4, characterized in that The step of determining the information recommendation strategy for different forms of service recommendation information based on the activation data and the interaction data corresponding to the various service recommendation information specifically includes: for each type of service recommendation information, determining, according to the activation data and the interaction data corresponding to the type of service recommendation information, a contribution of the type of service recommendation information to the activation operation of the target service as a first contribution corresponding to the type of service recommendation information, and Determining, according to the browsing data corresponding to the service recommendation information and the preset weight corresponding to the browsing data, the reservation data corresponding to the service recommendation information and the preset weight corresponding to the reservation data, the contribution of the service recommendation information to the activation operation of the target service as the second contribution corresponding to the service recommendation information; Determining an actual contribution degree corresponding to the type of business recommendation information according to the first contribution degree corresponding to the type of business recommendation information and the second contribution degree corresponding to the type of business recommendation information; According to the actual contribution degree corresponding to each type of business recommendation information, an information recommendation strategy for different forms of business recommendation information is determined.

6. The method according to claim 1, characterized in that Each piece of service recommendation information corresponding to the target service to be released includes service recommendation information under several information recommendation channels, and for each information recommendation channel, the service recommendation information under the information recommendation channel includes at least one form of service recommendation information; The step of determining the information recommendation strategy for different forms of service recommendation information based on the activation data and the interaction data corresponding to the various service recommendation information specifically includes: Based on the activation data and the interaction data corresponding to each piece of service recommendation information, an information recommendation strategy for each piece of service recommendation information under at least part of the information recommendation channels is determined.

7. The method according to claim 1, characterized in that The method further comprises: For each type of service recommendation information, according to the user identification information contained in the interaction data corresponding to the service recommendation information, determine the users who perform the interaction operation on the service recommendation information as the interested users; According to the user identification information included in the activation data, interested users who have not performed an activation operation on the target service are determined among the interested users, and activation prompt information is sent to the interested users who have not performed an activation operation on the target service.

8. An information recommendation device, characterized in that: include: An interaction module, configured to obtain, in response to interaction operations performed by each user on different forms of business recommendation information of a target business to be released, interaction data of each user on each form of business recommendation information as interaction data corresponding to various forms of business recommendation information, wherein, for each form of business recommendation information, the interaction data corresponding to the form of business recommendation information includes user identification information corresponding to each user who performs interaction operations on the form of business recommendation information; an activation module, configured to obtain activation data corresponding to the target service in response to activation operations performed by each user on the target service after the target service is released, wherein the activation data includes user identification information corresponding to each user who performs the activation operation; The recommendation module is used to determine information recommendation strategies for different forms of business recommendation information based on the activation data and the interaction data corresponding to the various business recommendation information, and perform information recommendation according to the information recommendation strategies.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

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