WO2020257993A1 - Procédé et appareil de poussée de contenu, serveur et support d'informations - Google Patents

Procédé et appareil de poussée de contenu, serveur et support d'informations Download PDF

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Publication number
WO2020257993A1
WO2020257993A1 PCT/CN2019/092594 CN2019092594W WO2020257993A1 WO 2020257993 A1 WO2020257993 A1 WO 2020257993A1 CN 2019092594 W CN2019092594 W CN 2019092594W WO 2020257993 A1 WO2020257993 A1 WO 2020257993A1
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WIPO (PCT)
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target
natural person
real
time
user
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PCT/CN2019/092594
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English (en)
Chinese (zh)
Inventor
吴旭镇
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深圳市欢太科技有限公司
Oppo广东移动通信有限公司
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Application filed by 深圳市欢太科技有限公司, Oppo广东移动通信有限公司 filed Critical 深圳市欢太科技有限公司
Priority to CN201980091615.0A priority Critical patent/CN113412608B/zh
Priority to PCT/CN2019/092594 priority patent/WO2020257993A1/fr
Publication of WO2020257993A1 publication Critical patent/WO2020257993A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/40—Network security protocols

Definitions

  • This application relates to the field of communication technology, and specifically relates to a content push method, device, server and storage medium.
  • the server pushes content to the client (for example, the application client)
  • the server pushes the content according to the user portrait of the user identity (ID) reported by the client. Since the accuracy of the user portrait of a single user ID is not high, the accuracy of the content pushed by the server is low.
  • the embodiments of the present application provide a content pushing method, device, server, and storage medium, which can improve the accuracy of content pushing.
  • an embodiment of the present application provides a content pushing method, including:
  • an embodiment of the present application provides a content pushing device, the content pushing device includes an acquiring unit, a calculating unit, a determining unit, and a pushing unit, wherein:
  • the acquiring unit is configured to acquire real-time user characteristics and real-time characteristic parameters of multiple user IDs corresponding to the target natural person identification ID, and generate the real-time user characteristics of the target natural person identification ID based on the real-time user characteristics of the multiple user IDs,
  • the real-time characteristic parameter includes the current time point;
  • the calculation unit is configured to calculate the similarity between the real-time user feature of the target natural person ID and at least one historical feature of the target historical time period of the target natural person ID, and the current time point is the same as the target historical time period Corresponding;
  • the determining unit is configured to determine the first target historical feature that has the highest similarity with the real-time user feature of the target natural person identification ID among the at least one historical feature, and determine the first target corresponding to the first target historical feature Content label
  • the pushing unit is configured to push the content corresponding to the first target content tag to the target natural person ID.
  • an embodiment of the present application provides a server, including a processor and a memory, the memory is used to store one or more programs, and the one or more programs are configured to be executed by the processor.
  • the program includes instructions for executing the steps in the first aspect of the embodiments of the present application.
  • an embodiment of the present application provides a computer-readable storage medium, wherein the foregoing computer-readable storage medium stores a computer program for electronic data exchange, wherein the foregoing computer program enables a computer to execute Some or all of the steps described in one aspect.
  • embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute Example part or all of the steps described in the first aspect.
  • the computer program product may be a software installation package.
  • the content pushing method described in the embodiment of this application specifically includes the following steps: acquiring real-time user characteristics and real-time characteristic parameters of multiple user IDs corresponding to the target natural person identification ID, and real-time user characteristics based on multiple user IDs Generate the real-time user characteristics of the target natural person ID, the real-time characteristic parameters include the current time point; calculate the similarity between the real-time user characteristics of the target natural person ID and the target historical time period of the target natural person ID, the current time point and the target Corresponding to the historical time period; determine the first target historical feature that has the highest similarity with the real-time user feature of the target natural person identification ID among at least one historical feature, and determine the first target content label corresponding to the first target historical feature; send the target natural person ID Push the content corresponding to the first target content tag.
  • the content to be pushed to the target natural person ID when pushing content to the target natural person ID, can be determined based on the real-time user characteristics of the target natural person ID and the similarity between the real-time feature parameters and the historical characteristics of the target natural person ID And push the content corresponding to the label to the target natural person ID. Since the historical features used for comparison are obtained based on multiple user IDs corresponding to the natural person ID, compared with the historical features using a single user ID, the historical features are greatly enriched, and the accuracy of content tags is improved, thereby Can improve the accuracy of content push.
  • FIG. 1 is a schematic flowchart of a content pushing method disclosed in an embodiment of the present application
  • FIG. 2 is a schematic flowchart of another content pushing method disclosed in an embodiment of the present application.
  • Figure 3 is a schematic structural diagram of a content pushing device disclosed in an embodiment of the present application.
  • Fig. 4 is a schematic structural diagram of a server disclosed in an embodiment of the present application.
  • the mobile terminals involved in the embodiments of this application may include various handheld devices with wireless communication functions, vehicle-mounted devices, wearable devices, computing devices or other processing devices connected to wireless modems, as well as various forms of user equipment (User Equipment, UE), mobile station (Mobile Station, MS), terminal device (terminal device), etc.
  • UE User Equipment
  • MS Mobile Station
  • terminal device terminal device
  • FIG. 1 is a schematic flowchart of a content pushing method disclosed in an embodiment of the present application. As shown in FIG. 1, the content pushing method includes the following steps.
  • the server acquires real-time user characteristics and real-time characteristic parameters of multiple user IDs corresponding to the target natural person identification ID, and generates real-time user characteristics of the target natural person identification ID based on the real-time user characteristics of the multiple user IDs, and the real-time characteristic parameters include the current Point in time.
  • the server serves the client, and the content of the service includes providing resources to the client and storing client data.
  • the server is a targeted service program, and the device running the server can be called a server.
  • the server can establish connections with multiple clients at the same time, and can provide services to multiple clients at the same time.
  • the services provided by the server for the client in the embodiments of the present application mainly include content push services.
  • the content push service may include: an advertisement content push service.
  • the advertisement content push service may include: browser content push service, application content push service, game content push service, etc.
  • the server can include application server, browser server, game server, etc.
  • User ID can include any one or more of the following types: single sign on identity (SSOID), OpenID, integrated circuit card identity (ICCID), international mobile device identity (International Mobile) Equipment Identity, IMEI), telephone number (telephone, TEL), Globally Unique Identifier (GUID), etc.
  • SSO is in multiple application systems. Users only need to log in once to access all mutually trusted application systems.
  • the natural person ID in the embodiment of this application corresponds to a natural person.
  • This natural person may correspond to a mobile terminal (for example, a mobile phone), at least one phone number, at least one application account, at least one OpenID, one SSOID, at least one ICCID, and at least one IMEI.
  • a mobile terminal for example, a mobile phone
  • the IMEI, phone number, and 5 application accounts of the mobile phone are labeled with a natural person ID.
  • the user behavior data corresponding to these 5 application accounts all belong to the user behavior data of this natural person ID.
  • a real natural person can have many user IDs (for example, the IMEI of a mobile phone, a phone number, and 5 application accounts), but only one unique natural person ID is corresponding.
  • the specific presentation form of the natural person ID can be a string of characters.
  • the natural person ID can correspond to an identification of a mobile terminal.
  • the user characteristics of the user ID may include device characteristics, positioning characteristics, and application (APP) characteristics.
  • Device characteristics may include the model of the device, the identification of the device, and so on.
  • the positioning features may include global positioning system (Global Positioning System, GPS) positioning information, mobile location-based service (Location Based Service, LBS) location trajectory, etc.
  • the application program characteristics may include the cumulative running time of the application program, the number of startup times of the application program, the usage frequency and times of the application program, and the usage of the application program function. Among them, the application function usage includes the types of advertisements that are paid attention to in the application, the application preference, etc.
  • the real-time user characteristics of the user ID are the user characteristics of the user ID collected at the current point in time.
  • the server can generate the real-time user characteristics of the target natural person identification ID based on the real-time user characteristics of multiple user IDs. For example, if there are 5 user IDs corresponding to the target natural person identification ID, the server can obtain 5 real-time user characteristics of these 5 user IDs, and the server can integrate the 5 real-time user characteristics of these 5 user IDs , Get the real-time user characteristics of the target natural person ID.
  • the 5 real-time user characteristics include: the advertisement A followed by the first APP, the login information of the second APP (not logged in), the login information of the third APP (not logged in), and the login information of the fourth APP ( Not logged in), the login information of the fifth APP (not logged in), then only one user feature (ad A followed by the first APP) among these 5 real-time user features is a useful feature, then the ads followed by the first APP A is the real-time user characteristic of the target natural person ID.
  • the real-time user features of multiple user IDs at a point in time there is generally only one user feature with at most one user ID as a useful feature.
  • the user may open multiple APPs at the same time.
  • multiple user ID characteristics may be useful characteristics.
  • multiple user ID characteristics need to be integrated. For another example, if 5 real-time user characteristics include: Advertisement A followed by the first APP, Advertisement B followed by the second APP, Advertisement C followed by the third APP, login information of the fourth APP (not logged in), and the fifth APP Login information (not logged in).
  • Advertisement A, Advertisement B, and Advertisement C are determined in, if the types of Advertisement A, Advertisement B, and Advertisement C are the same, and they are all type XX advertisements, it can be determined that the real-time user characteristics of the natural person ID are type XX advertisements.
  • step 101 the following steps may be performed:
  • the server obtains the correspondence between the natural person ID and the user ID;
  • the server determines multiple user IDs corresponding to the target natural person ID according to the corresponding relationship between the natural person ID and the user ID.
  • the corresponding relationship between the natural person ID and the user ID can be stored in the database of the server.
  • the server can quickly determine multiple user IDs corresponding to the target natural person ID according to the correspondence between the natural person ID and the user ID.
  • step (11) before performing step (11), the following steps can also be performed:
  • the server obtains the historical behavior data of N user IDs reported by multiple clients, and calculates the similarity between the historical behavior data of N user IDs, where N is a positive integer;
  • the server constructs a relationship pair between user IDs based on the similarity between the historical behavior data of N user IDs;
  • the server constructs the corresponding relationship between the natural person ID and the user ID according to the relationship between the user IDs.
  • one natural person ID corresponds to at least two user IDs.
  • the historical behavior data may include: historical device characteristics, historical positioning characteristics, and historical application (Application, APP) characteristics of N user IDs.
  • Historical device characteristics can include the model of the device, the identification of the device, and the usage habits of the device (for example, the backlight brightness of the device, the volume of the device, the holding posture of the device, the average use time of the device, the boot time of the device, and the Shutdown time, etc.).
  • Historical positioning features may include global positioning system (Global Positioning System, GPS) positioning information, mobile location-based service (Location Based Service, LBS) location trajectory, etc.
  • Historical application features can include application setting parameters (for example, application brightness, application volume, application refresh frequency), application opening time, application closing time, application function usage, application Program continuous running time, cumulative application running time, application installation data, application uninstallation data, etc.
  • the server can use the PageRank algorithm, the shortest path algorithm, and the Alternating Least Squares (ALS) algorithm to calculate the similarity between the historical behavior data of N user IDs.
  • a relationship pair is established for user IDs whose similarity is greater than a preset similarity threshold. For example, SSOID1 ⁇ ->IMEI2, OPENID1 ⁇ ->ICCID3, SSOID2 ⁇ ->TEL2, IMEI2 ⁇ ->TEL3, IMEI2 ⁇ ->ICCID1, SSOID2 ⁇ ->OPENID1, IMEI1 ⁇ ->SSOID2.
  • SSOID1, IMEI2, TEL3, and ICCID1 correspond to one natural person ID (for example, natural person ID1)
  • OPENID1, ICCID3, SSOID2, TEL2, and IMEI1 correspond to another natural person ID (for example, natural person ID2). See Table 1 for details.
  • Table 1 is a table of correspondence between user IDs and natural person IDs disclosed in the embodiments of the present application. As shown in Table 1, the correspondence between natural person ID1 and SSOID1, IMEI2, TEL3, and ICCID1, and the correspondence between natural person ID2 and OPENID1, ICCID3, SSOID2, TEL2, and IMEI1.
  • N is greater than a preset number threshold.
  • the server calculates the similarity between the historical behavior data of N user IDs, specifically:
  • the server uses a locally sensitive hash algorithm to calculate the similarity between the historical behavior data of N user IDs.
  • the number of N is large, the number of user IDs is very large, and if all the user IDs of the N user IDs are calculated one by one, the amount of calculation is very large.
  • Using local sensitive hashing algorithm to calculate the similarity between massive data can reduce the complexity of user similarity calculation.
  • the hash-sensitive algorithm can construct a hash function that puts user IDs with the same or similar characteristics into the same hash bucket, and then calculates the similarity of the user IDs in the hash bucket.
  • the local sensitive hash algorithm of this application can hash user IDs with the same or similar characteristics into the same hash bucket for similarity calculation, so that similar users can be assigned to the same hash bucket with a greater probability , Only need to calculate the similarity between user IDs in the bucket, thereby reducing the complexity of similarity calculation.
  • the same or similar features can be location features, and user IDs with similar latitude and longitude can be hashed into the same bucket.
  • the Euclidean distance calculation formula can be used to determine whether two user IDs have the same or similar location characteristics.
  • the location feature of the first user ID (longitude x 1 , latitude y 1 ) and the location of the second user ID can be obtained Features (longitude is x 2 , latitude y 2 ), calculate the position similarity between the first user ID and the second user ID:
  • d is less than or equal to the preset threshold, it indicates that the first user ID and the second user ID have the same or similar location characteristics, and the first user ID and the second user ID are placed in the same hash bucket. If d is greater than the preset threshold, it indicates that the first user ID and the second user ID do not have the same or similar location features.
  • the server can analyze the user behavior data of the newly registered user ID, analyze the user behavior data of the newly registered user ID and the user behavior data of all natural person IDs that have been stored, if the above has been stored.
  • the similarity of the natural person ID with the greatest similarity to the newly registered user ID among all the natural person IDs is greater than the preset similarity threshold, and the corresponding relationship between the natural person ID with the greatest similarity and the newly registered user ID is established.
  • the server calculates the similarity between the real-time user characteristic of the target natural person ID and at least one historical characteristic of the target historical time period of the target natural person ID, and the current time point corresponds to the target historical time period.
  • the historical feature of the target natural person ID is obtained by integrating the historical characteristics of multiple user IDs corresponding to the target natural person ID.
  • the server can obtain the user characteristics of multiple user IDs corresponding to the target natural person ID at various time points in the past month, and classify the user characteristics at each time point according to the time period to obtain the target natural person ID at each time. At least one historical feature for each time period. Specifically, the server can classify the user characteristics of multiple user IDs corresponding to the target natural person ID at 6-9 a.m.
  • the server can assign the target natural person
  • the user characteristics of the multiple user IDs corresponding to the ID in the past month from 9-11 a.m. are included in the historical characteristics of the second time period of the target natural person ID; the server can assign multiple user IDs corresponding to the target natural person ID in the past The user characteristics at 11-14 o'clock every day within a month are classified as the historical characteristics of the third time period of the target natural person ID; the server can assign multiple user IDs corresponding to the target natural person ID at 14-17 o'clock every day in the past month
  • the user characteristics are classified into the historical characteristics of the fourth time period of the target natural person ID; the server can include the user characteristics of multiple user IDs corresponding to the target natural person ID at 17-20 o'clock every day in the past month into the target natural person ID
  • the historical characteristics of the fifth time period; the server can classify the user characteristics of multiple user IDs corresponding to the target natural person ID at 20-24 o'clock every day in the
  • the current time point is the time point at which the server obtains the real-time user characteristics of the multiple user IDs corresponding to the target natural person identification ID. If the current time point is 7 am, the current time point corresponds to the first time period (6-9 o'clock), and the server calculates at least one of the real-time user characteristics of the target natural person ID and the first time period of the target natural person ID Similarity of historical features.
  • the server can obtain the content label corresponding to each historical feature in each time period.
  • the corresponding content tag may be advertisement A; if the historical feature is the focus on the first APP, the corresponding content tag may be the first APP.
  • the server obtains the content label corresponding to each historical feature in each time period, specifically:
  • the server acquires the first historical feature of the first time period, where the first historical feature is any one of all historical features of the first time period;
  • the server determines the group of natural persons with the first historical characteristic
  • the content label corresponding to the first historical feature in the first time period is the label corresponding to the natural person group.
  • the group of natural persons with the first historical characteristic indicates that the group of natural persons has the first historical characteristic, indicating that the target natural person ID also belongs to the group of natural persons and has the corresponding characteristics of the group of natural persons.
  • the target natural person ID pushes the content corresponding to the tag corresponding to the natural person group, thereby improving the accuracy of content pushing.
  • a group of natural persons refers to a collection of IDs of a type of natural persons with at least one common feature.
  • the server calculates the similarity between the real-time user characteristic of the target natural person ID and at least one historical characteristic of the target historical time period of the target natural person ID, specifically:
  • the server extracts the target digital parameter in the real-time user characteristics of the target natural person ID, and extracts at least one historical digital parameter of at least one historical characteristic of the target historical time period of the target natural person ID;
  • At least one Euclidean distance between the target digital parameter and the at least one historical digital parameter is calculated by using the Euclidean distance calculation formula.
  • the server calculates the similarity between the real-time user characteristic of the target natural person ID and at least one historical characteristic of the target historical time period of the target natural person ID, specifically:
  • the server extracts the target vector in the real-time user characteristics of the target natural person ID, and extracts at least one historical vector of at least one historical feature of the target historical time period of the target natural person ID;
  • the Hamming distance between the target vector and the at least one historical vector is calculated by a Hamming distance calculation formula.
  • the target vector is 10 bits
  • at least one history vector is 10 bits.
  • each bit of the target vector is the same. If they are the same, it indicates that the corresponding Hamming The distance is 0. If it is different, it means that the Hamming distance corresponding to the bit is 1.
  • the server determines the first target historical feature that has the highest similarity with the real-time user feature of the target natural person identification ID among the at least one historical feature, and determines the first target content tag corresponding to the first target historical feature.
  • the server pushes the content corresponding to the first target content tag to the target natural person ID.
  • each historical feature has at least one content tag corresponding to it.
  • the server After calculating the similarity between the real-time user feature of the target natural person ID and at least one historical feature of the target historical time period of the target natural person ID, the server determines the highest similarity between the at least one historical feature and the real-time user feature of the target natural person ID.
  • a target historical feature the server pushes the content corresponding to the first target content tag to the target natural person ID.
  • the first target content tag may be an advertisement content tag, and its corresponding content may be advertisement content.
  • accurate advertisement placement can be implemented in a specific time period according to the real-time characteristics of the user.
  • the real-time feature parameters also include current geographic location information.
  • the following steps may also be performed:
  • the server determines the geographic location tag corresponding to the current geographic location information.
  • Step 104 may specifically be:
  • the server pushes the content corresponding to the first target content tag and the geographic location tag to the target natural person ID.
  • the current geographic location information may include current GPS positioning information (for example, current longitude and latitude information).
  • current GPS positioning information for example, current longitude and latitude information.
  • the content to be pushed to the target natural person ID when pushing content to the target natural person ID, can be determined based on the real-time user characteristics of the target natural person ID and the similarity between the real-time feature parameters and the historical characteristics of the target natural person ID And push the content corresponding to the tag to the target natural person ID. Since the historical features used for comparison are obtained based on multiple user IDs corresponding to the natural person ID, compared with the historical features using a single user ID, the historical features are greatly enriched, and the accuracy of content tags is improved, thereby Can improve the accuracy of content push.
  • FIG. 2 is a schematic flowchart of another content pushing method disclosed in an embodiment of the present application.
  • Figure 2 is further optimized on the basis of Figure 1.
  • the content pushing method includes the following steps.
  • the server obtains real-time user characteristics and real-time characteristic parameters of multiple user IDs corresponding to the target natural person identification ID, and generates real-time user characteristics of the target natural person identification ID based on the real-time user characteristics of the multiple user IDs, and the real-time characteristic parameters include the current time point And current geographic location information.
  • the server calculates the similarity between the real-time user characteristic of the target natural person ID and at least one historical characteristic of the target historical time period of the target natural person ID, and the current time point corresponds to the target historical time period.
  • the server determines the first target historical feature that has the highest similarity with the real-time user feature of the target natural person identification ID among the at least one historical feature, and determines the first target content tag corresponding to the first target historical feature.
  • step 201 to step 203 can refer to step 101 to step 103 shown in FIG. 1, which will not be repeated here.
  • the server calculates the similarity between the first target content tag and the current geographic location information.
  • the server pushes the content corresponding to the first target content tag to the target natural person ID.
  • the current geographic location information may include current GPS positioning information (for example, current longitude and latitude information).
  • the server calculates the similarity between the first target content tag and the current geographic location information, specifically: the server extracts the location subtag in the first target content tag, and calculates the location subtag in the first target content tag and the current geographic location information The similarity.
  • the server can calculate the similarity between the location subtag in the first target content tag and the current geographic location information through the Euclidean distance calculation formula.
  • the method shown in FIG. 2 may further include the following steps:
  • the server determines the second target with the second highest similarity to the real-time user feature of the target natural person ID in at least one historical feature Historical characteristics, determine the second target content label corresponding to the second target historical characteristics;
  • the server calculates the similarity between the second target content label and the current geographic location information
  • the server pushes the content corresponding to the second target content tag to the target natural person ID.
  • the similarity between the first target content tag and the current geographic location information is less than the preset similarity threshold, it indicates that the current geographic location is not suitable for pushing content corresponding to the first target content tag. For example, if the first target content tag is a travel-related tag, and the current geographic location information is located in a hospital, the similarity between the two is small.
  • the server determines the second highest similarity between the at least one historical feature and the real-time user feature of the target natural person ID.
  • the second target historical feature determines the second target content tag corresponding to the second target historical feature. If the similarity between the second target content tag and the current geographic location information is greater than or equal to the preset similarity threshold, the server sends the target natural person ID Push the content corresponding to the second target content tag. If the similarity between the second target content label and the current geographic location information is less than the preset similarity threshold, step 201 is executed again.
  • the server includes hardware structures and/or software modules corresponding to each function.
  • the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or computer software-driven hardware depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered as going beyond the scope of the present invention.
  • the embodiment of the present application may divide the server side into functional units according to the foregoing method examples.
  • each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical function division, and there may be other division methods in actual implementation.
  • FIG. 3 is a schematic structural diagram of a content pushing device disclosed in an embodiment of the present application.
  • the content pushing device 300 includes an acquiring unit 301, a calculating unit 302, a determining unit 303, and a pushing unit 304, wherein:
  • the acquiring unit 301 is configured to acquire real-time user characteristics and real-time characteristic parameters of multiple user IDs corresponding to a target natural person identification ID, and generate real-time user characteristics of the target natural person identification ID based on the real-time user characteristics of the multiple user IDs ,
  • the real-time characteristic parameter includes the current time point;
  • the calculation unit 302 is configured to calculate the similarity between the real-time user characteristic of the target natural person ID and at least one historical characteristic of the target historical time period of the target natural person ID, and the current time point is the same as the target historical time Segment corresponding
  • the determining unit 303 is configured to determine the first target historical feature with the highest similarity to the real-time user feature of the target natural person identification ID among the at least one historical feature, and determine the first target historical feature corresponding to the first target historical feature.
  • Target content label
  • the pushing unit 304 is configured to push the content corresponding to the first target content tag to the target natural person ID.
  • the real-time characteristic parameter also includes current geographic location information.
  • the determining unit 303 is further configured to determine a geographic location tag corresponding to the current geographic location information
  • the pushing unit 304 pushes the content corresponding to the first target content tag to the target natural person ID, specifically: pushing the content corresponding to the first target content tag and the geographic location tag to the target natural person ID content.
  • the real-time characteristic parameters also include current geographic location information
  • the calculation unit 302 is further configured to calculate the similarity between the first target content tag and the current geographic location information
  • the pushing unit 304 is further configured to push the target natural person ID to the target natural person ID when the similarity between the first target content tag and the current geographic location information is greater than or equal to a preset similarity threshold. Content corresponding to a target content tag.
  • the determining unit 303 is further configured to determine the at least one historical feature when the similarity between the first target content label and the current geographic location information is less than the preset similarity threshold Determining the second target content tag corresponding to the second target historical feature in the second target historical feature with the second highest similarity to the real-time user characteristics of the target natural person identification ID;
  • the calculation unit 302 is further configured to calculate the similarity between the second target content tag and the current geographic location information
  • the pushing unit 304 is further configured to push the target natural person ID to the target natural person ID when the similarity between the second target content tag and the current geographic location information is greater than or equal to a preset similarity threshold. 2. Content corresponding to the target content tag.
  • the acquiring unit 301 is further configured to acquire the corresponding relationship between the natural person ID and the user ID before acquiring the real-time user characteristics and real-time characteristic parameters of multiple user IDs corresponding to the target natural person identification ID;
  • the determining unit 303 is further configured to determine multiple user IDs corresponding to the target natural person ID according to the corresponding relationship between the natural person ID and the user ID.
  • the content pushing apparatus 300 may further include a processing unit 305.
  • the obtaining unit 301 is further configured to obtain historical behavior data of N user IDs reported by multiple clients before obtaining the corresponding relationship between the natural person ID and the user ID;
  • the calculation unit 302 is also used to calculate the similarity between the historical behavior data of the N user IDs, where N is a positive integer;
  • the processing unit 305 is configured to construct a relationship pair between user IDs based on the similarity between the historical behavior data of the N user IDs; construct a pair of natural person IDs and user IDs based on the relationship between the user IDs Correspondence, in the correspondence between the natural person ID and the user ID, one natural person ID corresponds to at least two user IDs.
  • the calculation unit 302 calculates the similarity between the historical behavior data of the N user IDs, specifically: using a local sensitive hash algorithm to calculate the number of the N user IDs The similarity between historical behavior data.
  • the acquiring unit 301, the calculating unit 302, the determining unit 303, the pushing unit 304, and the processing unit 305 in FIG. 3 may be processors.
  • the target natural person ID when pushing content to the target natural person ID, can be determined based on the real-time user characteristics of the target natural person ID and the similarity between the real-time feature parameters and the historical characteristics of the target natural person ID.
  • the tag of the content pushed by the ID, and the content corresponding to the tag is pushed to the target natural person ID. Since the historical features used for comparison are obtained based on multiple user IDs corresponding to the natural person ID, compared with the historical features using a single user ID, the historical features are greatly enriched, and the accuracy of content tags is improved, thereby Can improve the accuracy of content push.
  • FIG. 4 is a schematic structural diagram of a server disclosed in an embodiment of the present application.
  • the server 400 includes a processor 401 and a memory 402.
  • the server 400 may also include a bus 403.
  • the processor 401 and the memory 402 may be connected to each other through the bus 403.
  • the bus 403 may be a peripheral component. Connect the standard (Peripheral Component Interconnect, referred to as PCI) bus or extended industry standard architecture (Extended Industry Standard Architecture, referred to as EISA) bus, etc.
  • PCI Peripheral Component Interconnect
  • EISA Extended Industry Standard Architecture
  • the bus 403 can be divided into an address bus, a data bus, a control bus, and so on. For ease of presentation, only one thick line is used to represent in FIG.
  • the server 400 may also include an input and output device 404.
  • the memory 402 is used to store one or more programs containing instructions; the processor 401 is used to call the instructions stored in the memory 402 to execute some or all of the method steps in FIGS. 1 to 2.
  • the target natural person ID when pushing content to the target natural person ID, can be determined based on the real-time user characteristics of the target natural person ID and the similarity between the real-time feature parameters and the historical characteristics of the target natural person ID.
  • the tag of the pushed content, and the content corresponding to the tag is pushed to the target natural person ID. Since the historical features used for comparison are obtained based on multiple user IDs corresponding to the natural person ID, compared with the historical features using a single user ID, the historical features are greatly enriched, and the accuracy of content tags is improved, thereby Can improve the accuracy of content push.
  • the embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program causes the computer to execute any part of the content push method recorded in the above method embodiment Or all steps.
  • the embodiments of the present application also provide a computer program product.
  • the computer program product includes a non-transitory computer-readable storage medium storing a computer program.
  • the computer program is operable to cause a computer to execute any of the methods described in the foregoing method embodiments. Part or all of the steps of a content push method.
  • the disclosed device may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or may be Integrate into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • the functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
  • the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable memory.
  • the technical solution of the present invention essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, A number of instructions are included to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the present invention.
  • the aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other various media that can store program codes.
  • the program can be stored in a computer-readable memory, and the memory can include: flash disk , Read-only memory (English: Read-Only Memory, abbreviated as: ROM), random access device (English: Random Access Memory, abbreviated as: RAM), magnetic disk or optical disc, etc.

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  • Engineering & Computer Science (AREA)
  • Computer Security & Cryptography (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Information Transfer Between Computers (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Des modes de réalisation de la présente invention concernent un procédé et un appareil de poussée de contenu, un serveur et un support d'informations. Le procédé consiste à : obtenir des caractéristiques d'utilisateur en temps réel d'une pluralité d'ID d'utilisateur correspondant à un identifiant ID de personne naturelle cible et des paramètres de caractéristique en temps réel, et générer une caractéristique d'utilisateur en temps réel de l'identifiant ID de personne naturelle cible sur la base des caractéristiques d'utilisateur en temps réel de la pluralité d'ID d'utilisateur, les paramètres caractéristiques en temps réel comprenant un point temporel actuel ; calculer une similarité entre la caractéristique d'utilisateur en temps réel de l'identifiant ID de personne naturelle cible et au moins une caractéristique historique d'une période de temps historique cible de l'ID de personne naturelle cible, le point temporel actuel correspondant à la période de temps historique cible ; déterminer une première caractéristique historique cible ayant la similarité la plus élevée par rapport à la caractéristique d'utilisateur en temps réel de l'identifiant ID de personne naturelle cible parmi la ou les caractéristiques historiques, et déterminer une première étiquette de contenu cible correspondant à la première caractéristique historique cible ; et pousser un contenu correspondant à la première étiquette de contenu cible vers l'ID de personne naturelle cible. Les modes de réalisation de la présente invention peuvent améliorer la précision de poussée de contenu.
PCT/CN2019/092594 2019-06-24 2019-06-24 Procédé et appareil de poussée de contenu, serveur et support d'informations WO2020257993A1 (fr)

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