Information processing method and device, electronic equipment and storage medium

Abstract

The invention provides an information processing method and device, electronic equipment and a storage medium. The invention relates to the technical field of information processing. The method can comprise the following steps: acquiring subject attribute data of a plurality of promotion subjects and user data of a user; according to the subject attribute data of the plurality of promotion subjects and the user data, determining association information between the plurality of promotion subjects and the user; and according to the association information between the user and the plurality of promotion subjects, updating a preset keyword library. According to the method and the device, the keyword library can be dynamically updated, so that the setting that the keywords in the keyword library are fixed keywords is broken, the keywords can be updated in real time, the timeliness and the accuracy of the keywords in the keyword library are improved, and the accuracy and the efficiency of information popularization based on the keyword library are favorably improved.

Description

Technical Field

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

[0002] With the development of Internet technology, more and more users can receive promotional information from promotion entities quickly and conveniently.

[0003] Usually, after a user enters a search term, the data stored in the database used to match the search term is relatively fixed. In this way, because the data in the database are all pre-set promotion information, the background needs to traverse each promotion information and select the appropriate promotion information to push to the user. Summary of the invention

[0004] In view of this, the purpose of the present disclosure is to propose an information processing method, device, electronic device and storage medium, which can solve the existing problems in a targeted manner.

[0005] Based on the above-mentioned purpose, in the first aspect, the present disclosure proposes a method for processing information, including: obtaining subject attribute data of multiple promotion subjects and user data of users; determining association information between the multiple promotion subjects and the users based on the subject attribute data of the multiple promotion subjects and the user data; and updating a preset keyword library based on the association information between the users and the multiple promotion subjects.

[0006] In the second aspect, an information processing device is also provided, including: an acquisition unit, configured to acquire subject attribute data of multiple promotion subjects and user data of users; a determination unit, configured to determine association information between the multiple promotion subjects and the users based on the subject attribute data of the multiple promotion subjects and the user data; an update unit, configured to update a preset keyword library based on the association information between the users and the multiple promotion subjects.

[0007] In a third aspect, an electronic device is also provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect.

[0008] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the program is executed by a processor to implement any method described in the first aspect.

[0009] In general, the present disclosure has at least the following beneficial effects: the keyword library can be updated dynamically, thereby breaking the setting that the keywords in the keyword library are fixed keywords, allowing the keywords to be updated in real time, improving the timeliness and accuracy of the keywords in the keyword library, and helping to improve the accuracy and efficiency of information promotion based on the keyword library. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in accordance with the present disclosure and should not be regarded as limiting the scope of the present disclosure.

[0011] Figure 1 A flowchart showing a method for processing information according to an embodiment of the present disclosure is shown;

[0012] Figure 2 Another flow chart of the information processing method according to the embodiment of the present disclosure is shown;

[0013] Figure 3 A schematic diagram showing an information processing device according to an embodiment of the present disclosure;

[0014] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure is shown;

[0015] Figure 5 A schematic diagram of a storage medium provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0016] The present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0017] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0018] Figure 1 The method for processing information disclosed in the present invention is shown. In an embodiment of the present invention, the method comprises the following steps:

[0019] Step S101, obtaining subject attribute data of multiple promotion subjects and user data of users.

[0020] In this embodiment, the execution subject of the information processing method can obtain the subject attribute data of multiple promotion subjects, and the execution subject can also obtain the user data of the user. The promotion subject can be, for example, an advertiser.

[0021] Subject attribute data can reflect the attributes of the promotion subject. For example, if the promotion subject is a medical institution, the subject attribute data can include medical projects, hospital facilities, doctors, etc. User data can include user attribute data and behavior data related to the promotion subject. For example, user attribute data can include basic user data such as gender and age. Behavior data can include search history, browsing history, and comments, etc.

[0022] Specifically, the users here may correspond to the same login account.

[0023] Step S102: determining association information between the multiple promotion entities and the user according to the entity attribute data of the multiple promotion entities and the user data.

[0024] In this embodiment, the execution subject may determine the association information between the multiple promotion subjects and the user according to the subject attribute data of the multiple promotion subjects and the user data in various ways. For example, the execution subject may input the subject attribute data and the user data of the multiple promotion subjects into a preset model, thereby obtaining the association information output from the model. Specifically, the association information may be various information that can characterize the association relationship between the promotion subject and the user, such as information that characterizes association or non-association.

[0025] Step S103: updating a preset keyword library according to the association information between the user and the multiple promotion entities.

[0026] In this embodiment, the execution subject may adopt various methods to update the preset keyword library according to the association information between the user and the multiple promotion subjects. For example, the execution subject may update the preset keyword library when the association information indicates an association.

[0027] This embodiment can dynamically update the keyword library, thereby breaking the setting that the keywords in the keyword library are fixed keywords, allowing the keywords to be updated in real time, improving the timeliness and accuracy of the keywords in the keyword library, and helping to improve the accuracy and efficiency of information promotion based on the keyword library.

[0028] Figure 2 The method for processing information according to the embodiment of the present disclosure is shown. Figure 2 As shown, the method includes:

[0029] Step S201, obtaining subject attribute data of multiple promotion subjects and user data of users.

[0030] Step S202: extract keywords from the subject attribute data of the multiple promotion subjects and the user data to obtain multiple subject keywords and multiple user keywords.

[0031] In this embodiment, the execution subject may extract keywords from the subject attribute data of the multiple promotion subjects and the user data in various ways. The extraction results are multiple subject keywords and multiple user keywords. For example, the execution subject may extract keywords using any of the following algorithms: statistical algorithms (e.g., term frequency–inverse document frequency, TF-IDF), semantic analysis algorithms (e.g., Latent Dirichlet Allocation, LDA), and machine learning algorithms (keyword extraction algorithm TextRank).

[0032] Step S203, determining the associated subject keywords and user keywords, and determining the associated probability of the association, wherein at least one user keyword of each user is associated with at least one subject keyword, and the at least one subject keyword corresponds to the same promotion subject.

[0033] In this embodiment, the execution subject may determine the subject keywords and user keywords that are associated with any promotion subject and any user, and may also determine the association probability of the association.

[0034] The association may be a word match that exists, and specifically, the word match may include a complete match, a partial match, or a synonym match, etc. For example, if "rhinoplasty" and "nose plastic surgery" exist in the user's search history X, and "rhinoplasty" exists in the product A of the promotion subject, then the user data including X and the subject attribute data including A may be associated, and the association probability is 0.86.

[0035] Specifically, the execution subject may determine the subject keywords and user keywords that are associated in various ways. For example, the execution subject may determine by using a machine learning algorithm. The machine learning algorithm may be of various types, such as a filtering collaborative algorithm or a convolutional neural network, etc. Specifically, the input to the machine learning algorithm may be the keyword itself or the feature corresponding to the keyword. In the case of a feature, the execution subject may generate features corresponding to the user keyword and features corresponding to the subject keyword. If there are at least two user keywords corresponding to the user, the features corresponding to the user may be represented as a matrix, i.e., a user feature matrix. If there are at least two subject keywords corresponding to the promotion subject, the features corresponding to any promotion subject may be represented as a matrix, i.e., a subject feature matrix. In addition, the execution subject may also use a corresponding relationship table to determine the subject keywords and user keywords that are associated. Specifically, the corresponding relationship table may record whether there is an association between the subject keywords and the user keywords and the probability of association. The execution subject may determine the subject keywords and user keywords that are associated among the extracted keywords, and the association is the association recorded in the corresponding relationship table.

[0036] Step S204: taking the association probability, and the subject keyword and user keyword associated with the association probability as association information between the user corresponding to the association probability and the corresponding promotion subject.

[0037] In this embodiment, the association information may include the association probability. In addition, the association information may also include the associated subject keywords and user keywords. That is, in the keyword library, there may be not only keywords but also associations between keywords.

[0038] Step S205: updating a preset keyword library according to the association information between the user and the multiple promotion entities.

[0039] This embodiment can help to quickly determine the promotion subject through the keyword library by storing the associated subject keywords and user keywords and the association probability as the association information in the keyword library, so as to efficiently push the promotion information.

[0040] In some optional implementations of the present embodiment, the association information includes an association probability, and updating a preset keyword library based on the association information between the user and the multiple promotion entities includes: determining an association probability greater than a preset threshold, wherein the association probability greater than the preset threshold is an association probability to be added; and adding the association information between the user corresponding to the association probability to be added and the corresponding promotion entity to the keyword library.

[0041] In these implementations, the execution subject may only add the associated information with a greater associated probability to the keyword library. Specifically, the preset threshold may be any value in the interval [0,1), such as 0 or 0.2.

[0042] The execution subject may store the associated information in the keyword library when the associated information associated with the associated probability does not exist in the keyword library. Alternatively, in other application scenarios, the execution subject may directly store the associated information in the keyword library. In this scenario, the keyword library may regularly perform deduplication of associated information.

[0043] In these implementations, the above-mentioned execution entity can add the subject keywords and user keywords to the keyword library in an associated manner, so that after the user enters a search term for retrieval, the promotion entity corresponding to the search term can be efficiently determined by matching the search term with the keywords in the keyword library.

[0044] In some optional implementations of any embodiment of the present disclosure, the method further includes: in response to receiving a search request from a client corresponding to the user, determining a promotion entity associated with the user in a keyword library based on the associated information; and pushing the promotion information of the promotion entity associated with the user to the user.

[0045] In these optional implementations, the execution entity may determine the promotion entity associated with the user in the keyword library according to the associated information when receiving the search request. Afterwards, the execution entity may push the promotion information of the determined promotion entity to the user.

[0046] The execution subject may determine the promotion subject associated with the user in the keyword library according to the association information in various ways. For example, when the association information is an association probability, the execution subject may determine a specified number of promotion subjects with the highest association probability with the requested user as the promotion subjects associated with the user. In the keyword library, the association probability of the specified number of promotion subjects with the requested user is greater than or equal to the association probability of other promotion subjects with the requested user.

[0047] These optional implementations can utilize the updated keyword library to quickly and accurately determine promotional information associated with the user when the user conducts a search.

[0048] In some optional application scenarios of these implementations, the association information includes an association probability; the response to receiving a search request from a client corresponding to the user, determining a promotion entity associated with the user in the keyword library according to the association information, includes: in response to receiving a search request from the client, determining candidate promotion entities associated with the user in the keyword library according to the search terms included in the search request and the user attribute data of the user; and determining a preset number of promotion entities associated with the user from each candidate promotion entity in order of the association probabilities corresponding to the candidate promotion entities from large to small.

[0049] In these application scenarios, after receiving the user's search request, the execution entity may determine the promotion entity as a candidate promotion entity in the keyword library according to the search term input by the user. Afterwards, the execution entity may select a preset number of promotion entities as promotion entities associated with the user according to the magnitude of the association probability.

[0050] Specifically, the execution subject may adopt various methods to determine the promotion subject as a candidate promotion subject in the keyword library according to the search term input by the user. For example, the execution subject may input the search term and each promotion subject in the keyword library into a preset model, and obtain the candidate promotion subject output from the preset model. The preset model may be a pre-trained deep neural network, which is used to determine the candidate promotion subject among the various promotion subjects using the search term and each promotion subject.

[0051] These application scenarios can use search terms to quickly match candidate promotion entities in the keyword library, and can also determine the promotion entity that will ultimately be promoted to the user based on the size of the association probability.

[0052] In some optional cases of these application scenarios, determining the candidate promotion entities associated with the user in the keyword library based on the search terms included in the search request includes: searching for keywords that have a word match with the search terms in the keyword library, the keywords including subject keywords and user keywords; and determining the candidate promotion entities associated with the user based on the search results.

[0053] In these optional cases, the execution subject may search for keywords that match the search terms in the keyword library to obtain search results. Then, the execution subject may determine candidate promotion entities associated with the user based on the search results. For example, the execution subject may input the search results and the promotion entities recorded in the keyword library into a specified model, and obtain candidate promotion entities output from the specified model.

[0054] In these cases, the executing entity can prioritize accurate matching of search terms in the keyword library, thereby helping to improve the accuracy of determining candidate promotion entities.

[0055] Optionally, determining the candidate promotion entities associated with the user based on the search results includes: if the search result indicates that a keyword that matches the search term is found, then determining the promotion entity corresponding to the keyword, and the promotion entity corresponding to the keyword is the preliminary promotion entity; if the search result indicates that no keyword that matches the search term is found, then determining the product category corresponding to the search term, and the promotion entity corresponding to the product category is the preliminary promotion entity; among each preliminary promotion entity, determining the promotion entity whose corresponding population portrait matches the user attribute data as the candidate promotion entity.

[0056] The above-mentioned execution entities may give priority to accurately matching the search terms with the keywords in the keyword library, thereby screening the promotion entities. If the accurate matching is unsuccessful, the product category may be determined, and the promotion entity corresponding to the product category may be determined as the preliminary promotion entity. Regardless of whether the accurate matching is successful, the above-mentioned execution entities may match the user attribute data of the search user with the crowd portrait, thereby further narrowing the scope of the promotion entity.

[0057] Promotional information is product information used to promote products. The keyword library includes keywords. Keywords are subject keywords or user keywords that indicate product information. Each product belongs to a product category, and the promotion subject has a corresponding population portrait.

[0058] Specifically, if the search result indicates that a keyword matching the search term is found in the keyword library, the execution subject may determine the promotion subject corresponding to the keyword. For example, the keyword includes "rhinoplasty", which may be a subject keyword or a keyword. If the search result indicates that a keyword matching the search term is not found, the product category corresponding to the search term may be determined, and the promotion subject of the product corresponding to the product category may be determined.

[0059] Products can be classified by keyword features, for example, keywords can include: eyes, plastic surgery, injection, filling, wrinkle removal, etc. Or, they can also be classified by service items, for example, product categories can include eye plastic surgery, micro plastic surgery, facial care, skin whitening, nose repair, etc. For example, if the search term includes "eyes", the above execution entity can determine that the product category corresponding to the search term is "eye plastic surgery".

[0060] In these optional cases, the above-mentioned execution entity may give priority to accurate matching of products through keywords. If there are no keywords, category matching may be performed through product categories to ensure the smooth push of promotional information.

[0061] In some optional implementations of any embodiment of the present disclosure, the step of obtaining user data includes: obtaining real user data of the user, the real user data including historical behavior data related to the promotion subject, and real user attributes of the user; determining a data item corresponding to the real user data, the data item being a target data item; if there are other data items other than the target data item in the preset data item set, adding user data of similar users in the other data items to the user data of the user, the user and the similar users having at least one real user data of the same type, wherein the similar users refer to users whose user data similarity is higher than a similarity threshold.

[0062] In these implementations, if a user, such as a target user, has some missing data, that is, the target data items corresponding to the user's real data cannot cover the complete set of data items, then the user data of other users can be used to supplement the user data of the target user.

[0063] Specifically, similar users may refer to users whose similarity of at least one of user behavior data and user attribute data is higher than a similarity threshold.

[0064] These implementations can supplement user data with data of other users, thereby ensuring complete user data.

[0065] In some optional implementations of any embodiment of the present disclosure, the association information includes an association probability; the method further includes: in response to receiving a promotion request from a target promotion entity, extracting a content field to be promoted contained in the promotion request; determining, from the user data in the user set, at least one user data having a field overlap with the content field to be promoted, and the user corresponding to the at least one user data is the target user; updating the association probability between the target promotion entity and the target user, wherein the updated association probability is greater than the updated association probability; determining, from the multiple push entities, a push entity associated with the target user according to the updated association probability; and pushing the promotion information of the promotion entity associated with the target user to the target user.

[0066] In these optional implementations, the target promotion subject may send a promotion request, which includes a field for content to be promoted, and the request is used to indicate that the target promotion subject has a need to push promotion information. The execution subject may determine which users' user data overlaps with the field for content to be promoted included in the promotion request, and use these users as target users. Afterwards, the execution subject may update the association probability between the target promotion subject and the target user, thereby increasing the probability of the target promotion subject pushing promotion information to the target user by increasing the association probability.

[0067] These implementation methods can help the promotion entity find the corresponding user group through targeted promotion requests, thereby improving the targeting of pushed promotion information.

[0068] The present disclosure provides an information processing device, which is used to execute the information processing method described in the above embodiment. Figure 3 As shown, the device may include: an acquisition unit 301, configured to acquire subject attribute data of multiple promotion subjects and user data of users; a determination unit 302, configured to determine association information between the multiple promotion subjects and the users based on the subject attribute data of the multiple promotion subjects and the user data; an update unit 303, configured to update a preset keyword library based on the association information between the users and the multiple promotion subjects.

[0069] Optionally, the determination unit is further configured to perform the determination of the association information between the multiple promotion entities and the users based on the entity attribute data of the multiple promotion entities and the user data in the following manner: extract keywords from the entity attribute data of the multiple promotion entities and the user data to obtain multiple entity keywords and multiple user keywords; determine the associated entity keywords and user keywords, and determine the association probability of the association, wherein at least one user keyword of each user is associated with at least one entity keyword, and the at least one entity keyword corresponds to the same promotion entity; use the association probability, and the entity keyword and user keyword associated with the association probability, as the association information between the user corresponding to the association probability and the corresponding promotion entity.

[0070] Optionally, the association information includes an association probability, and the updating unit is further configured to execute the updating of the preset keyword library according to the association information between the user and the multiple promotion entities in the following manner: determining an association probability greater than a preset threshold, wherein the association probability greater than the preset threshold is an association probability to be added; and adding the association information between the user corresponding to the association probability to be added and the corresponding promotion entity to the keyword library.

[0071] Optionally, the device also includes: an associating entity unit, configured to determine a promotion entity associated with the user in the keyword library according to the associated information in response to receiving a search request from the client corresponding to the user; and a pushing unit, configured to push the promotion information of the promotion entity associated with the user to the user.

[0072] Optionally, the association information includes an association probability; the association subject unit is further configured to execute the following method in response to receiving a search request from the client corresponding to the user, and determining the promotion subject associated with the user in the keyword library according to the association information: in response to receiving a search request from the client, determining candidate promotion subjects associated with the user in the keyword library according to the search terms included in the search request; and determining a preset number of promotion subjects associated with the user from each candidate promotion subject in order of the association probabilities corresponding to the candidate promotion subjects from large to small.

[0073] Optionally, the associating subject unit is further configured to execute the determination of candidate promotion subjects associated with the user in the keyword library based on the search terms included in the search request in the following manner: searching for keywords that have a word match with the search terms in the keyword library, the keywords including subject keywords and user keywords; and determining candidate promotion subjects associated with the user based on the search results.

[0074] Optionally, the associating entity unit is further configured to perform the determination of candidate promotion entities associated with the user based on the search results in the following manner: if the search result indicates that a keyword that matches the search term is found, then the promotion entity corresponding to the keyword is determined, and the promotion entity corresponding to the keyword is the preliminary promotion entity; if the search result indicates that a keyword that matches the search term is not found, then the product category corresponding to the search term is determined, and the promotion entity corresponding to the product category is the preliminary promotion entity; among each preliminary promotion entity, the promotion entity whose corresponding population portrait matches the user attribute data is determined as the candidate promotion entity.

[0075] Optionally, the step of acquiring the user data includes: acquiring the real user data of the user, the real user data including historical behavior data related to the promotion subject and the real user attributes of the user; determining a data item corresponding to the real user data, the data item being a target data item; if there are other data items other than the target data item in the preset data item set, adding user data of similar users in the other data items to the user data of the user, the user and the similar users having at least one real user data of the same type, wherein the similar users refer to users whose user data similarity is higher than a similarity threshold.

[0076] Optionally, the association information includes an association probability; the device is further configured to: in response to receiving a promotion request from a target promotion entity, extract a content field to be promoted contained in the promotion request; determine, in the user data of the user set, at least one user data that has a field overlap with the content field to be promoted, and the user corresponding to the at least one user data is the target user; update the association probability between the target promotion entity and the target user, wherein the updated association probability is greater than the association probability before the update; according to the updated association probability, determine a push entity associated with the target user from the multiple push entities; and push the promotion information of the promotion entity associated with the target user to the target user.

[0077] The information processing device provided by the above-mentioned embodiment of the present disclosure and the information processing method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0078] The embodiments of the present disclosure also provide an electronic device corresponding to the information processing method provided in the above embodiments, so as to execute the above information processing method. The embodiments of the present disclosure are not limited.

[0079] Please refer to Figure 4 , which shows a schematic diagram of an electronic device provided by some embodiments of the present disclosure. Figure 4 As shown, the electronic device 40 includes: a processor 400, a memory 401, a bus 402 and a communication interface 403, and the processor 400, the communication interface 403 and the memory 401 are connected via the bus 402; the memory 401 stores a computer program that can be run on the processor 400, and the processor 400 executes the method provided in any of the aforementioned embodiments of the present disclosure when running the computer program.

[0080] The memory 401 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 403 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0081] The bus 402 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 401 is used to store programs, and the processor 400 executes the programs after receiving the execution instruction. The information processing method disclosed in any implementation of the aforementioned embodiment of the present disclosure may be applied to the processor 400, or implemented by the processor 400.

[0082] The processor 400 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 400 or the instruction in the form of software. The above processor 400 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 401, and the processor 400 reads the information in the memory 401 and completes the steps of the above method in combination with its hardware.

[0083] The electronic device provided by the embodiment of the present disclosure and the information processing method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented therein.

[0084] The present disclosure also provides a computer-readable storage medium corresponding to the information processing method provided in the above-mentioned embodiment. Figure 5 The computer-readable storage medium shown is a CD 50 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the information processing method provided by any of the aforementioned embodiments will be executed.

[0085] It should be noted that examples of the computer-readable storage medium may also 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 optical or magnetic storage media, which are not listed here one by one.

[0086] The computer-readable storage medium provided by the above-mentioned embodiments of the present disclosure and the information processing method provided by the embodiments of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0087] It should be noted that:

[0088] In the above text, the term "comprises", "comprising" or any other variant thereof is 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 "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be noted that the scope of the method and device in the embodiments of the present disclosure is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0089] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present disclosure.

[0090] The embodiments of the present disclosure are described above in conjunction with the accompanying drawings, which are only specific implementation modes of the present disclosure. However, the present disclosure is not limited to the above-mentioned specific implementation modes. The above-mentioned specific implementation modes are merely illustrative and not restrictive. Under the guidance of the present disclosure, ordinary technicians in this field can also make many forms without departing from the purpose of the present disclosure and the scope of protection of the claims, all of which are within the protection of the present disclosure.

Claims

1. A method for processing information, characterized in that: include: Obtaining subject attribute data of multiple promotion subjects and user data of users; Determining association information between the multiple promotion entities and the user according to the subject attribute data of the multiple promotion entities and the user data; The preset keyword library is updated according to the association information between the user and the multiple promotion entities.

2. The method according to claim 1, characterized in that The determining, based on the subject attribute data of the multiple promotion subjects and the user data, the association information between the multiple promotion subjects and the user includes: Extracting keywords from the subject attribute data of the plurality of promotion subjects and the user data to obtain a plurality of subject keywords and a plurality of user keywords; Determining associated subject keywords and user keywords, and determining an associated probability of the association, wherein at least one user keyword of each user is associated with at least one subject keyword, and the at least one subject keyword corresponds to the same promotion subject; The association probability, and the subject keyword and user keyword associated with the association probability are used as association information between the user corresponding to the association probability and the corresponding promotion subject.

3. The method according to claim 2, characterized in that The association information includes an association probability, and the updating of a preset keyword library according to the association information between the user and the plurality of promotion entities includes: Determine an association probability greater than a preset threshold, wherein the association probability greater than the preset threshold is the association probability to be added; The association information between the user corresponding to the association probability to be added and the corresponding promotion subject is added to the keyword library.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: In response to receiving a search request from a client corresponding to the user, determining a promotion subject associated with the user in the keyword library according to the association information; The promotion information of the promotion entity associated with the user is pushed to the user.

5. The method according to claim 4, characterized in that The association information includes an association probability; The step of determining, in response to receiving a search request from a client corresponding to the user, a promotion subject associated with the user in the keyword library according to the association information, includes: In response to receiving a search request from the client, determining candidate promotion entities associated with the user in the keyword library according to search terms included in the search request; According to the descending order of the association probabilities corresponding to the candidate promotion entities, a preset number of promotion entities associated with the user are determined from each candidate promotion entity.

6. The method according to claim 5, characterized in that The determining, in the keyword library, a candidate promotion subject associated with the user according to the search term included in the search request includes: In the keyword library, searching for keywords that have a word match with the search term, the keywords include subject keywords and user keywords; Based on the search result, a candidate promotion entity associated with the user is determined.

7. The method according to claim 6, characterized in that The step of determining a candidate promotion entity associated with the user based on the search result includes: If the search result indicates that a keyword matching the search term is found, then the promotion subject corresponding to the keyword is determined, and the promotion subject corresponding to the keyword is the preliminary promotion subject; if the search result indicates that no keyword matching the search term is found, then the product category corresponding to the search term is determined, and the promotion subject corresponding to the product category is the preliminary promotion subject; Among the various initially selected promotion entities, the promotion entity whose corresponding population portrait matches the user attribute data is determined as a candidate promotion entity.

8. The method according to claim 1, characterized in that: The step of obtaining the user data includes: Acquire the real user data of the user, wherein the real user data includes historical behavior data related to the promotion subject and the real user attributes of the user; Determine a data item corresponding to the real user data, the data item being a target data item; If there are other data items other than the target data item in the preset data item set, the user data of similar users in the other data items are added to the user data of the user, and the user and the similar user have at least one real user data of the same type, wherein the similar user refers to a user whose user data similarity is higher than a similarity threshold.

9. The method according to any one of claims 1 to 8, characterized in that The association information includes an association probability; and the method further includes: In response to receiving a promotion request from a target promotion subject, extracting a field of content to be promoted contained in the promotion request; Determine, from the user data in the user set, at least one piece of user data having a field overlapped with the field of the content to be promoted, and the user corresponding to the at least one piece of user data is the target user; Updating the association probability between the target promotion subject and the target user, wherein the updated association probability is greater than the association probability before the update; According to the updated association probability, determining a push subject associated with the target user from among the multiple push subjects; The promotion information of the promotion entity associated with the target user is pushed to the target user.

10. An information processing device, characterized in that: include: An acquisition unit configured to acquire subject attribute data of a plurality of promotion subjects and user data of users; a determining unit configured to determine association information between the plurality of promotion entities and the user according to the subject attribute data of the plurality of promotion entities and the user data; The updating unit is configured to update the preset keyword library according to the association information between the user and the multiple promotion entities.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor runs the computer program to implement the method according to any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method according to any one of claims 1 to 9.

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