CN113383360A - Content pushing method and device, server and storage medium - Google Patents

Content pushing method and device, server and storage medium Download PDF

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CN113383360A
CN113383360A CN201980091185.2A CN201980091185A CN113383360A CN 113383360 A CN113383360 A CN 113383360A CN 201980091185 A CN201980091185 A CN 201980091185A CN 113383360 A CN113383360 A CN 113383360A
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natural person
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CN113383360B (en
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郭子亮
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Guangdong Oppo Mobile Telecommunications Corp Ltd
Shenzhen Huantai Technology Co Ltd
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Guangdong Oppo Mobile Telecommunications Corp Ltd
Shenzhen Huantai Technology Co Ltd
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Abstract

The embodiment of the application discloses a content pushing method, a content pushing device, a server and a storage medium, wherein the method comprises the following steps: when target content needs to be pushed to a target user ID, current user behavior data of the target user ID is obtained; determining a target natural person ID corresponding to the target user ID, and acquiring a portrait label of the target natural person ID; calculating the current similarity of the target user ID and the target natural person ID based on the current user behavior data of the target user ID and the portrait label of the target natural person ID; and if the current similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold value, refusing to push the target content to the target user ID. The method and the device for pushing the content can improve the accuracy of pushing the content.

Description

Content pushing method and device, server and storage medium Technical Field
The present application relates to the field of communications technologies, and in particular, to a content push method, an apparatus, a server, and a storage medium.
Background
Currently, when a server (e.g., a server) pushes content to a client (e.g., an application client), the server pushes the content according to a user image of a user Identifier (ID) reported by the client. If the client is being used by others, the content pushed by the server is not desired by the client user. As can be seen, the accuracy of the content pushed by the server is low at present.
Disclosure of Invention
The embodiment of the application provides a content pushing method, a content pushing device, a server and a storage medium, and can improve the accuracy of content pushing.
In a first aspect, an embodiment of the present application provides a content push method, including:
when target content needs to be pushed to a target user ID, current user behavior data of the target user ID is obtained;
determining a target natural person ID corresponding to the target user ID, and acquiring a portrait label of the target natural person ID;
calculating the current similarity of the target user ID and the target natural person ID based on the current user behavior data of the target user ID and the portrait label of the target natural person ID;
and if the current similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold value, refusing to push the target content to the target user ID.
In a second aspect, an embodiment of the present application provides a content pushing apparatus, where the content pushing apparatus includes an obtaining unit, a determining unit, a calculating unit, and a processing unit, where:
the acquiring unit is used for acquiring the current user behavior data of the target user ID when the target content needs to be pushed to the target user ID;
the determining unit is used for determining a target natural person ID corresponding to the target user ID;
the acquisition unit is further used for acquiring a portrait label of the ID of the target natural person;
the calculation unit is used for calculating the current similarity between the target user ID and the target natural person ID based on the current user behavior data of the target user ID and the portrait label of the target natural person ID;
the processing unit is used for refusing to push the target content to the target user ID under the condition that the current similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold value.
In a third aspect, an embodiment of the present application provides a server, including a processor, and a memory, where the memory is configured to store one or more programs, where the one or more programs are configured to be executed by the processor, and where the program includes instructions for performing the steps in the first aspect of the embodiment of the present application.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program enables a computer to perform some or all of the steps described in the first aspect of the embodiment of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
It can be seen that the content push method described in the embodiment of the present application specifically includes the following steps: when target content needs to be pushed to a target user ID, current user behavior data of the target user ID is obtained; determining a target natural person ID corresponding to the target user ID, and acquiring a portrait label of the target natural person ID; calculating the current similarity of the target user ID and the target natural person ID based on the current user behavior data of the target user ID and the portrait label of the target natural person ID; and if the current similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold value, refusing to push the target content to the target user ID. By implementing the embodiment of the application, when the target content is pushed to the target user ID, the current similarity between the target user ID and the target natural person ID can be calculated based on the current user behavior data of the target user ID and the portrait label of the target natural person ID corresponding to the target user ID, when the current similarity between the target user ID and the target natural person ID is smaller than the preset similarity threshold value, it is indicated that the current actually used user of the target user ID is changed, the target content is not pushed to the target user ID, the wrong content is prevented from being pushed to the user, and therefore the accuracy of content pushing is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a content push method disclosed in an embodiment of the present application;
fig. 2 is a schematic flow chart of another content push method disclosed in the embodiments of the present application;
fig. 3 is a schematic flow chart of another content push method disclosed in the embodiments of the present application;
fig. 4 is a schematic structural diagram of a content pushing apparatus disclosed in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a server disclosed in an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The Mobile terminal according to the embodiment of the present application may include various handheld devices, vehicle-mounted devices, wearable devices, computing devices or other processing devices connected to a wireless modem, and various forms of User Equipment (UE), Mobile Stations (MS), terminal devices (terminal device), and the like. For convenience of description, the above-mentioned devices are collectively referred to as a mobile terminal.
The following describes embodiments of the present application in detail.
Referring to fig. 1, fig. 1 is a schematic flow chart of a content push method disclosed in an embodiment of the present application, and as shown in fig. 1, the content push method includes the following steps.
101, when the target content needs to be pushed to the target user ID, the server side obtains the current user behavior data of the target user ID.
In the embodiment of the application, the server serves the client, and the content of the service is such as providing resources to the client, saving client data and the like. The server is a targeted service program, and the device running the server can be called a server. The server can establish connection with a plurality of clients simultaneously and can provide services for the clients simultaneously. The service provided by the server side for the client side in the embodiment of the application mainly comprises content push service. The content push service may include: browser content push services, application content push services, game content push services, and the like. The service end can comprise an application service end, a browser service end, a game service end and the like. For example, the server may push an Application (APP) that may be of interest to the user, goods that may be of interest to the user (e.g., a brand XX, a color XX, etc.), content that may be of interest to the user (e.g., a tour route push, a gourmet push, a music push, an entertainment place push, etc.), news messages that may be of interest to the user, and the like for the client.
The user ID may include any one or more types of: single Sign On Identity (SSOID), OpenID, Integrated Circuit Card Identification (ICCID), International Mobile Equipment Identification (IMEI), telephone number (TEL), Global Unique Identifier (GUID), and the like. The SSO is that in a plurality of application systems, a user can access all mutually trusted application systems only by logging in once.
The current user behavior data for a user ID may include Application (APP) behavior data for the user ID. Such as APP currently being attended to by the user ID, currently browsed web pages, currently collected music, currently purchased goods, etc.
102, the server side determines a target natural person ID corresponding to the target user ID, and obtains the portrait label of the target natural person ID.
In the embodiment of the application, the natural person ID corresponds to a natural person. The physical person may correspond to a mobile terminal (e.g., a cell phone), at least one phone number, at least one application account number, at least one OpenID, an SSOID, at least one ICCID, and at least one IMEI. For example, if a natural person has a mobile phone, a phone number, and 5 application accounts, the IMEI, the phone number, and the 5 application accounts of the mobile phone are labeled with a natural person ID. Thus, a real natural person may have many user IDs (e.g., IMEI, phone number, 5 application accounts of a mobile phone), but only one unique natural person ID. The specific presentation form of the natural person ID may be a string of characters.
Optionally, the natural person ID may correspond to an identifier of a mobile terminal, and when the server pushes content to the natural person ID, the server may push content to the mobile terminal corresponding to the natural person ID without separately sending the pushed content to the application account, thereby improving the pushing efficiency.
The portrait label of the target natural person ID may include a base portrait label and a behavior portrait label, and the base portrait label of the target natural person ID may include at least one of gender, age, income, constellation, education level, body type, children, marital of the target natural person. The behavior portrayal tag of the target natural person ID comprises at least one of a target natural person interest preference tag (such as a consumption preference tag, an investment preference tag, a travel preference tag, a diet preference tag, a music preference tag, an entertainment preference tag, a shopping preference tag, a sports preference tag, a color preference tag, and a brand preference tag), a psychology tag of the target natural person, an active payment tag of the target natural person on each APP, and a Location Based Service (LBS) Location track tag of the target natural person.
Optionally, in step 102, the server determines a target natural person ID corresponding to the target user ID, which may specifically include the following steps:
(11) the server side obtains the corresponding relation between the user ID and the natural person ID;
(12) and the server determines a target natural person ID corresponding to the target user ID according to the corresponding relation between the user ID and the natural person ID.
In the embodiment of the application, the database of the server side can store the corresponding relation between the user ID and the natural person ID. The server can quickly determine the target natural person ID corresponding to the target user ID according to the corresponding relation between the user ID and the natural person ID. Wherein one natural person ID may correspond to a plurality of user IDs.
The correspondence between the user ID and the natural person ID stored in the database of the server may be determined based on the similarity between the historical behavior data of a plurality of user IDs. Specifically, the server may calculate similarity between historical behavior data of any two user IDs of the N user IDs by using a webpage ranking (PageRank) algorithm, a shortest path algorithm, and an Alternating Least Square (ALS) algorithm, establish a relationship pair for the user IDs with the similarity greater than a preset similarity threshold, and correspond the user IDs of the established relationship pair to the same natural person ID. For example, if it is detected that the similarity between the SSOID1 and the historical behavior data of the IMEI is greater than a preset similarity threshold, the similarity between the historical behavior data of the ope 1 and the historical behavior data of the ICCID3 is greater than a preset similarity threshold, the similarity between the historical behavior data of the SSOID2 and the historical behavior data of the TEL2 is greater than a preset similarity threshold, the similarity between the historical behavior data of the IMEI2 and the historical behavior data of the TEL3 is greater than a preset similarity threshold, the similarity between the historical behavior data of the IMEI2 and the historical behavior data of the ICCID1 is greater than a preset similarity threshold, the similarity between the historical behavior data of the SSOID2 and the historical behavior data of the IMEI1 is greater than a preset similarity threshold, and the similarity between the historical behavior data of the SSOID1 and the historical behavior data of the SSOID2 is greater than a preset similarity threshold, the service end may establish the following relationship pair:
SSOID1<->IMEI2，OPENID1<->ICCID3，SSOID2<->TEL2，IMEI2<->TEL3，IMEI2<->ICCID1，SSOID2<->OPENID1，IMEI1<->SSOID2。
then SSOID1, IMEI2, TEL3, ICCID1 correspond to one natural person ID (e.g., natural person ID1), OPENID1, ICCID3, SSOID2, TEL2, IMEI1 correspond to another natural person ID (e.g., natural person ID 2). See table 1 for details.
TABLE 1
User ID Natural person ID
SSOID1、IMEI2、TEL3、ICCID1 Natural person ID1
OPENID1、ICCID3、SSOID2、TEL2、IMEI1 Natural person ID2
Table 1 is a table of correspondence between a user ID and a natural person ID disclosed in the embodiment of the present application. As shown in table 1, the correspondence between the natural person ID1 and SSOID1, IMEI2, TEL3, and ICCID1, and the correspondence between the natural person ID2 and OPENID1, ICCID3, SSOID2, TEL2, and IMEI 1.
103, the server calculates the current similarity between the target user ID and the target natural person ID based on the current user behavior data of the target user ID and the portrait label of the target natural person ID.
And 104, if the current similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold value, the server refuses to push the target content to the target user ID.
In the embodiment of the application, the server side can extract a plurality of user characteristics from the current user behavior data of the target user ID, and the current similarity of the target natural person ID is determined based on the matching degree of the user characteristics and the portrait label of the target natural person ID. Specifically, the server may calculate the current similarity between the target user ID and the target natural person ID through any one of a cosine distance calculation formula, a hamming distance calculation formula, and an euclidean distance calculation formula.
The user characteristics extracted by the server from the current user behavior data of the target user ID may include current device characteristics, current location characteristics and current application characteristics. The current device characteristics may include the model number of the device currently in use, the identity of the device currently in use, and the like. The current Location features may include current Global Positioning System (GPS) Location information, current Location Based Service (LBS) Location tracks, and the like. The current application characteristics may include an accumulated running time of the current application, a number of times of starting the current application, a frequency and a number of times of using the current application, a function usage of the current application, and the like. Wherein the current application function usage includes the type of advertisement of interest within the current application, usage preferences of the current application, and the like.
The preset similarity threshold may be preset and stored in a memory (e.g., a non-volatile memory) of the server. The preset similarity threshold is a preset standard for judging whether the target user ID is associated with the target natural person ID, if the current similarity between the target user ID and the target natural person ID is smaller than the preset similarity threshold, it is indicated that the current user behavior of the target user ID does not accord with the target natural person ID, and the current actually used user of the target user ID is likely to change, the target content is not pushed to the target user ID, so that wrong content is prevented from being pushed to the actually used user, and the content pushing accuracy is improved. And the target content of the original user (the user corresponding to the target natural person ID) can be prevented from being pushed to the user who is actually used, so that the privacy of the original user is protected.
Optionally, if the current similarity between the target user ID and the target natural person ID is greater than or equal to the preset similarity threshold, the server pushes the target content to the target user ID.
In the embodiment of the application, if the current similarity between the target user ID and the target natural person ID is greater than or equal to the preset similarity threshold, it is indicated that the user corresponding to the target user ID is the user corresponding to the target natural person ID, the target user ID is used by the user, and the target user ID is not substituted or temporarily used by other users, so that the server can push the target content to the target user ID, and the content pushing accuracy is improved.
Optionally, 103 may specifically include the following steps:
(21) the server side extracts a plurality of user features from the current user behavior data of the target user ID, quantizes the plurality of user features of the target user ID and obtains a user feature vector of the target user ID;
(22) the server quantizes the portrait label of the target natural person ID to obtain a natural person feature vector of the target natural person ID;
(23) and the server calculates the cosine distance between the user characteristic vector of the target user ID and the natural person characteristic vector of the target natural person ID through a cosine distance calculation formula to obtain the similarity between the target user ID and the target natural person ID.
In the embodiment of the application, the server may quantize the plurality of user features of the target user ID according to the correspondence between the user features and the feature values to obtain a plurality of feature values corresponding to the plurality of user features of the target user ID, and form the user feature vector according to the plurality of feature values. The server side can quantize the plurality of portrait tags of the target natural person ID according to the corresponding relation between the portrait tags and the tag values to obtain a plurality of tag values corresponding to the plurality of portrait tags of the target natural person ID, and a natural person feature vector is formed according to the plurality of tag values. For example, for music style, the correspondence between the user features and the feature values includes the following: the feature value corresponding to the small fresh style is 0.1, the feature value corresponding to the heavy taste style is 0.9, the feature value corresponding to the elegant style is 0.2, and the feature value corresponding to the wound style is 0.4. For the playing habit of music, the corresponding relationship between the user characteristics and the characteristic values includes the following: the characteristic value corresponding to the single-song circulation music is 6, the characteristic value corresponding to the shared music is 5, the characteristic value corresponding to the music collected is 4, the characteristic value corresponding to the music actively played is 3, and the characteristic value corresponding to the music blackened is 0.
For music style, the correspondence between portrait tags and tag values includes the following: the feature value corresponding to the small fresh label is 1, the feature value corresponding to the heavy taste label is 9, the feature value corresponding to the elegant label is 2, and the feature value corresponding to the wound label is 4. For the playing habit of music, the correspondence between portrait tags and tag values includes the following: the characteristic value corresponding to the single song circulation label is 60, the characteristic value corresponding to the sharing label is 50, the characteristic value corresponding to the collection label is 40, the characteristic value corresponding to the active play label is 30, and the characteristic value corresponding to the black label is 0. If the server extracts 2 user characteristics from the current user behavior data of the target user ID, the user characteristics are respectively as follows: and if the music is in a fresh style and is collected, quantizing the 2 user characteristics of the target user ID to obtain a user characteristic vector (0.1, 4) of the target user ID. If the portrait label of the target natural person ID is a heavy taste label and a single song cycle label, the natural person feature vector of the target natural person ID is (9, 60).
For example, the server extracts a plurality of user features from the current user behavior data of the target user ID: user characteristics 1, 2, 3, 4 and 5; the portrait label of the target natural person ID includes: an image label 1, an image label 2, an image label 3, an image label 4 and an image label 5. The user characteristics 1, 2, 3, 4 and 5 are quantized and divided into: a1, a2, a3, a4 and a 5. The portrait label 1, portrait label 2, portrait label 3, portrait label 4 and portrait label 5 are quantized to: b1, b2, b3, b4 and b 5. The server calculates the cosine distance between the user characteristic vector of the target user ID and the natural person characteristic vector of the target natural person ID through the following cosine distance calculation formula to obtain the similarity between the target user ID and the target natural person ID:
Figure PCTCN2019093108-APPB-000001
wherein P is the cosine distance between the user feature vector of the target user ID and the natural person feature vector of the target natural person ID, that is, the similarity between the target user ID and the target natural person ID. The value of P is between 0 and 1, the highest similarity is shown when the value of P is 1, and the lowest similarity is shown when the value of P is 1.
Optionally, 103 may specifically include the following steps:
(31) the server side extracts a plurality of user features from the current user behavior data of the target user ID, quantizes the plurality of user features of the target user ID and obtains a user feature vector of the target user ID;
(32) the server quantizes the portrait label of the target natural person ID to obtain a natural person feature vector of the target natural person ID;
(33) and the server calculates the Euclidean distance between the user characteristic vector of the target user ID and the natural person characteristic vector of the target natural person ID through an Euclidean distance calculation formula to obtain the similarity between the target user ID and the target natural person ID.
For example, when calculating the euclidean distance between the user feature vector of the first target user ID and the natural person feature vector of the target natural person ID, the user feature vector (x) of the target user ID may be obtained1，y 1) Natural person feature vector (x) of target natural person ID2，y 2) Calculating the Euclidean distance between the user characteristic vector of the target user ID and the natural person characteristic vector of the target natural person ID according to the following Euclidean distance calculation formula:
Figure PCTCN2019093108-APPB-000002
wherein d is the calculated euclidean distance. The larger the euclidean distance is, the smaller the similarity between the target user ID and the target natural person ID is, and the smaller the euclidean distance is, the larger the similarity between the target user ID and the target natural person ID is.
Optionally, 103 may specifically include the following steps:
(41) the server side extracts a plurality of user features from the current user behavior data of the target user ID, quantizes the plurality of user features of the target user ID and obtains a user feature vector of the target user ID;
(42) the server quantizes the portrait label of the target natural person ID to obtain a natural person feature vector of the target natural person ID;
(43) and the server calculates the Hamming distance between the user characteristic vector of the target user ID and the natural person characteristic vector of the target natural person ID through a Hamming calculation formula to obtain the similarity between the target user ID and the target natural person ID.
Wherein, the smaller the calculated Hamming distance is, the higher the similarity is; the greater the calculated hamming distance, the lower its similarity.
For example, the user feature vector is 10 bits, the natural person feature vector is 10 bits, each bit of the user feature vector is compared with each bit of the natural person feature vector to determine whether the bit is the same as the bit of the natural person feature vector, if the bit is the same as the bit of the user feature vector, the hamming distance corresponding to the bit is 0, if the bit is different from the bit of the natural person feature vector, the hamming distances of all the bits are added to obtain a final hamming distance between the user feature vector and the natural person feature vector, and the final hamming distance is 0-10. The larger the final Hamming distance is, the lower the similarity is; the smaller the final hamming distance, the higher its similarity.
Optionally, after step 104 is executed, the following steps may also be executed:
the server side determines a content tag corresponding to the target user ID according to the current user behavior data of the target user ID; and the server pushes the content corresponding to the content tag to the target user ID.
In the embodiment of the application, after it is determined that the current actually-used user of the target user ID changes, related content can be pushed to the target user ID according to the current user behavior data of the target user ID, so as to meet the content acquisition requirement of the actual user.
By implementing the embodiment of the application, when the target content is pushed to the target user ID, the current similarity between the target user ID and the target natural person ID can be calculated based on the current user behavior data of the target user ID and the portrait label of the target natural person ID corresponding to the target user ID, when the current similarity between the target user ID and the target natural person ID is smaller than the preset similarity threshold value, it is indicated that the current actually used user of the target user ID is changed, the target content is not pushed to the target user ID, the wrong content is prevented from being pushed to the user, and therefore the accuracy of content pushing is improved.
Referring to fig. 2, fig. 2 is a schematic flow chart of another content pushing method disclosed in an embodiment of the present application, and fig. 2 is further optimized based on fig. 1, as shown in fig. 2, the content pushing method includes the following steps.
201, when the target content needs to be pushed to the target user ID, the server side obtains the current user behavior data of the target user ID.
202, the server side determines a target natural person ID corresponding to the target user ID, and obtains the portrait label of the target natural person ID.
The server calculates the current similarity of the target user ID and the target natural person ID based on the current user behavior data of the target user ID and the portrait label of the target natural person ID 203.
And 204, if the current similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold, the server refuses to push the target content to the target user ID.
Optionally, the specific implementation of steps 201 to 204 may refer to steps 101 to 104 shown in fig. 1, which is not described herein again.
205, the server determines that the target user ID is an abnormal use ID, and obtains historical user behavior data of the target user ID in a preset time period.
Wherein step 205 is performed after step 204.
The preset time period is a time period before the current time point, such as: 5 days, 7 days, 10 days, 15 days, 30 days, etc.
After determining that the target user ID is the abnormal use ID, the server needs to further judge whether the target user ID is a temporary transformation natural person or a permanent transformation natural person.
206, the server calculates the historical similarity between the target user ID and the target natural person ID based on the historical user behavior data of the target user ID in the preset time period and the portrait label of the target natural person ID.
And 207, if the historical similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold, the server side determines that the target user ID is a machine changing user ID.
The method for calculating the similarity in steps 206 to 207 is similar to that in steps 203 and 204, and for details, reference may be made to steps 203 and 204, which are not described herein again.
Optionally, after step 207 is executed, the following steps may also be executed:
(51) the server releases the corresponding relation between the target user ID and the target natural person ID;
(52) and the server establishes the corresponding relation between the target user ID and the new natural person ID.
In order to avoid subsequent content pushing according to the portrait label of the target natural person ID corresponding to the target user ID, the embodiment of the application may release the correspondence between the target user ID and the target natural person ID when determining that the target user ID is the machine changing user ID.
In the embodiment of the application, whether the target user ID is a machine changing user ID is determined by further detecting the similarity between the historical user behavior data of the target user ID and the portrait label of the target natural person ID within a period of time, so that a subsequent content push strategy for the target user ID is modified, and the accuracy of content push is further improved.
Referring to fig. 3, fig. 3 is a schematic flow chart of another content pushing method disclosed in an embodiment of the present application, and fig. 3 is further optimized based on fig. 2, as shown in fig. 3, the content pushing method includes the following steps.
301, when the target content needs to be pushed to the target user ID, the server obtains the current user behavior data of the target user ID.
302, the server identifies a target natural person ID corresponding to the target user ID, and obtains a portrait label of the target natural person ID.
303, the server calculates the current similarity between the target user ID and the target natural person ID based on the current user behavior data of the target user ID and the portrait label of the target natural person ID.
304, if the current similarity between the target user ID and the target natural person ID is smaller than the preset similarity threshold, the server refuses to push the target content to the target user ID.
Optionally, the specific implementation of steps 301 to 304 may refer to steps 101 to 104 shown in fig. 1, which is not described herein again.
305, the server determines that the target user ID is an abnormal use ID, and obtains historical user behavior data of the target user ID in a preset time period.
And 306, the server calculates the historical similarity between the target user ID and the target natural person ID based on the historical user behavior data of the target user ID in the preset time period and the portrait label of the target natural person ID.
307, if the historical similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold, the server side determines that the target user ID is a machine changing user ID.
Optionally, the specific implementation of steps 301 to 307 may refer to steps 201 to 207 shown in fig. 2, which is not described herein again.
308, if the historical similarity between the target user ID and the target natural person ID is greater than or equal to the preset similarity threshold, the server determines that the target user ID is a temporary user ID.
According to the method and the device, whether the target user ID is a temporary user ID or not can be determined according to the similarity between the historical user behavior data of the target user ID and the portrait label of the target natural person ID in a period of time, so that a content push strategy of the target user ID can be modified temporarily, and the content push accuracy is further improved.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that the server side includes hardware structures and/or software modules for performing the functions in order to realize the functions. Those of skill in the art will readily appreciate that the present invention can be implemented in hardware or a combination of hardware and computer software, with the exemplary elements and algorithm steps described in connection with the embodiments disclosed herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiment of the present application, the server may be divided into the functional units according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a content pushing device disclosed in the embodiment of the present application. As shown in fig. 4, the content push apparatus 400 includes an obtaining unit 401, a determining unit 402, a calculating unit 403, and a processing unit 404, where:
the obtaining unit 401 is configured to obtain current user behavior data of a target user ID when target content needs to be pushed to the target user ID;
the determining unit 402 is configured to determine a target natural person ID corresponding to the target user ID;
the obtaining unit 401 is further configured to obtain a portrait label of the target natural person ID;
the calculating unit 403 is configured to calculate a current similarity between the target user ID and the target natural person ID based on the current user behavior data of the target user ID and the portrait label of the target natural person ID;
the processing unit 404 is configured to refuse to push the target content to the target user ID when the current similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold.
Optionally, the calculating unit 403 calculates the current similarity between the target user ID and the target natural person ID based on the current user behavior data of the target user ID and the portrait label of the target natural person ID, specifically: extracting a plurality of user features from the current user behavior data of the target user ID, and quantizing the plurality of user features of the target user ID to obtain a user feature vector of the target user ID; quantizing the portrait label of the target natural person ID to obtain a natural person feature vector of the target natural person ID; and calculating the cosine distance between the user characteristic vector of the target user ID and the natural person characteristic vector of the target natural person ID through a cosine distance calculation formula to obtain the similarity between the target user ID and the target natural person ID.
Optionally, the determining unit 402 determines a target natural person ID corresponding to the target user ID, specifically: acquiring a corresponding relation between a user ID and a natural person ID; and determining a target natural person ID corresponding to the target user ID according to the corresponding relation between the user ID and the natural person ID.
Optionally, the determining unit 402 is further configured to determine that the target user ID is an abnormal usage ID after the processing unit 404 rejects to push the target content to the target user ID;
the obtaining unit 401 is further configured to obtain historical user behavior data of the target user ID in a preset time period;
the calculating unit 403 is further configured to calculate historical similarity between the target user ID and the target natural person ID based on historical user behavior data of the target user ID within a preset time period and a portrait label of the target natural person ID;
the determining unit 402 is further configured to determine that the target user ID is a switch user ID when the historical similarity between the target user ID and the target natural person ID is smaller than the preset similarity threshold.
Optionally, the processing unit 404 is further configured to, after the determining unit 402 determines that the target user ID is a switch user ID, release the corresponding relationship between the target user ID and the target natural person ID; and establishing a corresponding relation between the target user ID and the new natural person ID.
Optionally, the determining unit 402 is further configured to determine that the target user ID is a temporary user ID when the historical similarity between the target user ID and the target natural person ID is greater than or equal to the preset similarity threshold.
Optionally, the determining unit 402 is further configured to determine, after the processing unit 404 rejects to push the target content to the target user ID, a content tag corresponding to the target user ID according to current user behavior data of the target user ID;
the processing unit 404 is further configured to push the content corresponding to the content tag to the target user ID.
The acquiring unit 401, the determining unit 402, the calculating unit 403, and the processing unit 404 in fig. 4 may be processors.
With the content push device shown in fig. 4, when pushing a target content to a target user ID, the current similarity between the target user ID and the target natural person ID may be calculated based on the current user behavior data of the target user ID and the portrait label of the target natural person ID corresponding to the target user ID, and when the current similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold, it indicates that the currently actually used user of the target user ID has changed, and the target content is not pushed to the target user ID, so that the content push error is avoided from being pushed to the user, and the content push accuracy is improved.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a server according to an embodiment of the present disclosure. As shown in fig. 5, the server 500 includes a processor 501 and a memory 502, wherein the server 500 may further include a bus 503, the processor 501 and the memory 502 may be connected to each other through the bus 503, and the bus 503 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus 503 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus. The server 500 may further include a communication interface 504, and the communication interface 504 is used for communicating with the client. Memory 502 is used to store one or more programs containing instructions; processor 501 is configured to call instructions stored in memory 502 to perform some or all of the method steps described above with respect to fig. 1-3.
By implementing the server shown in fig. 5, when pushing a target content to a target user ID, the current similarity between the target user ID and the target natural person ID may be calculated based on the current user behavior data of the target user ID and the pictorial label of the target natural person ID corresponding to the target user ID, and when the current similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold, it indicates that the currently actually used user of the target user ID has changed, and the target content is not pushed to the target user ID, so that the content pushing error is avoided for the user, and thus the accuracy of content pushing is improved.
Embodiments of the present application also provide a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, and the computer program makes a computer execute part or all of the steps of any one of the content push methods described in the above method embodiments.
Embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any one of the content push methods as described in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing embodiments of the present invention have been described in detail, and the principles and embodiments of the present invention are explained herein by using specific examples, which are only used to help understand the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

  1. A method for pushing content, comprising:
    when target content needs to be pushed to a target user ID, current user behavior data of the target user ID is obtained;
    determining a target natural person ID corresponding to the target user ID, and acquiring a portrait label of the target natural person ID;
    calculating the current similarity of the target user ID and the target natural person ID based on the current user behavior data of the target user ID and the portrait label of the target natural person ID;
    and if the current similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold value, refusing to push the target content to the target user ID.
  2. The method of claim 1, wherein calculating the similarity of the target user ID to the target natural person ID based on current user behavior data of the target user ID and the portrait label of the target natural person ID comprises:
    extracting a plurality of user features from the current user behavior data of the target user ID, and quantizing the plurality of user features of the target user ID to obtain a user feature vector of the target user ID;
    quantizing the portrait label of the target natural person ID to obtain a natural person feature vector of the target natural person ID;
    and calculating the cosine distance between the user characteristic vector of the target user ID and the natural person characteristic vector of the target natural person ID through a cosine distance calculation formula to obtain the similarity between the target user ID and the target natural person ID.
  3. The method of claim 1 or 2, wherein the determining a target natural person ID corresponding to the target user ID comprises:
    acquiring a corresponding relation between a user ID and a natural person ID;
    and determining a target natural person ID corresponding to the target user ID according to the corresponding relation between the user ID and the natural person ID.
  4. The method of claim 3, wherein after rejecting the push of the targeted content to the targeted user ID, the method further comprises:
    determining that the target user ID is an abnormal use ID, and acquiring historical user behavior data of the target user ID in a preset time period;
    calculating historical similarity of the target user ID and the target natural person ID based on historical user behavior data of the target user ID in a preset time period and the portrait label of the target natural person ID;
    and if the historical similarity between the target user ID and the target natural person ID is smaller than the preset similarity threshold, determining that the target user ID is a machine changing user ID.
  5. The method of claim 4, wherein after determining that the target user ID is a switch user ID, the method further comprises:
    releasing the corresponding relation between the target user ID and the target natural person ID;
    and establishing a corresponding relation between the target user ID and the new natural person ID.
  6. The method of claim 4, further comprising:
    and if the historical similarity between the target user ID and the target natural person ID is greater than or equal to the preset similarity threshold, determining that the target user ID is a temporary user ID.
  7. The method of claim 3, wherein after rejecting the push of the targeted content to the targeted user ID, the method further comprises:
    determining a content tag corresponding to the target user ID according to the current user behavior data of the target user ID;
    and pushing the content corresponding to the content tag to the target user ID.
  8. A content push apparatus, characterized in that the content push apparatus includes an acquisition unit, a determination unit, a calculation unit, and a processing unit, wherein:
    the acquiring unit is used for acquiring the current user behavior data of the target user ID when the target content needs to be pushed to the target user ID;
    the determining unit is used for determining a target natural person ID corresponding to the target user ID;
    the acquisition unit is further used for acquiring a portrait label of the ID of the target natural person;
    the calculation unit is used for calculating the current similarity between the target user ID and the target natural person ID based on the current user behavior data of the target user ID and the portrait label of the target natural person ID;
    the processing unit is used for refusing to push the target content to the target user ID under the condition that the current similarity between the target user ID and the target natural person ID is smaller than a preset similarity threshold value.
  9. A server comprising a processor and a memory for storing one or more programs configured for execution by the processor, the programs comprising instructions for performing the method of any of claims 1-7.
  10. A computer-readable storage medium for storing a computer program for electronic data exchange, wherein the computer program causes a computer to perform the method according to any one of claims 1 to 7.
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