CN106550004B - Service object recommendation method and device - Google Patents
Service object recommendation method and device Download PDFInfo
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- CN106550004B CN106550004B CN201510614129.6A CN201510614129A CN106550004B CN 106550004 B CN106550004 B CN 106550004B CN 201510614129 A CN201510614129 A CN 201510614129A CN 106550004 B CN106550004 B CN 106550004B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/55—Push-based network services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
- H04L51/52—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail for supporting social networking services
Abstract
The application provides a method and a device for recommending a business object, wherein the method comprises the following steps: receiving a business object recommendation request from a current login user, wherein the business object recommendation request comprises a recommended business object and a recommendation target user; selecting associated users which have an association relationship with the current login user and the recommendation target user simultaneously from historical users of the recommended business object; and sending a recommendation message to the recommendation target user, wherein the recommendation message comprises the description information of the recommended service object and the introduction information of the historical user of which the associated user serves as the recommended service object. By the technical scheme, the incidence relation among the users can be used for improving the recommendation effect of the business object.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a method and an apparatus for recommending a service object.
background
Through mutual recommendation among users, the rapid popularization of the business objects can be realized. In the related art, mutual recommendation among users often adopts the following way: and sending a recommendation message to the user B by the user A, and informing the user B of the description information of the recommended service object.
however, the user B can only know the identity of the user a to the business object and cannot know the identity of the business object at other users, so that the user B often abandons the attempt to the business object.
Disclosure of Invention
In view of this, the present application provides a method and an apparatus for recommending a service object, which can use an association relationship between users to improve a recommendation effect on the service object.
In order to achieve the above purpose, the present application provides the following technical solutions:
According to a first aspect of the present application, a method for recommending a business object is provided, including:
receiving a business object recommendation request from a current login user, wherein the business object recommendation request comprises a recommended business object and a recommendation target user;
Selecting associated users which have an association relationship with the current login user and the recommendation target user simultaneously from historical users of the recommended business object;
and sending a recommendation message to the recommendation target user, wherein the recommendation message comprises the description information of the recommended service object and the introduction information of the historical user of which the associated user serves as the recommended service object.
according to a second aspect of the present application, a recommendation apparatus for a business object is provided, including:
the system comprises a receiving unit, a recommending unit and a recommending unit, wherein the receiving unit receives a business object recommending request from a current login user, and the business object recommending request comprises a recommended business object and a recommending target user;
The selecting unit is used for selecting associated users which have an association relationship with the current login user and the recommendation target user simultaneously from historical users of the recommended business object;
And the sending unit is used for sending a recommendation message to the recommendation target user, wherein the recommendation message comprises the description information of the recommended service object and the introduction information of the historical user taking the associated user as the recommended service object.
according to the technical scheme, the associated users which have an association relation with the current login user and the recommended target user at the same time are selected, the introduction information of the historical users of the recommended service object is sent to the recommended target user, so that the recommended target user can know the recognition conditions of other users except the current login user on the service object, the recognition of the recommended service object by the recommended target user is strengthened, and the attempt probability of the recommended target user on the service object is improved.
Drawings
FIG. 1A is a schematic view of a terminal interface of a current login user in the related art;
FIG. 1B is a schematic diagram of a terminal interface of a recommendation target user in the related art;
FIG. 2 is a flowchart of a method for recommending business objects according to an exemplary embodiment of the present application;
FIG. 3 is a flowchart of another method for recommending business objects provided by an exemplary embodiment of the present application;
FIG. 4 is a schematic diagram of a user relationship provided by an exemplary embodiment of the present application;
FIG. 5 is a schematic diagram of a terminal interface for recommending a target user according to an exemplary embodiment of the present application;
FIG. 6 is a schematic diagram of a terminal interface of a currently logged-in user according to an exemplary embodiment of the present application;
FIG. 7 is a schematic diagram of another terminal interface for recommending target users provided by an exemplary embodiment of the present application;
FIG. 8 is a flowchart of a method for recommending business objects according to an exemplary embodiment of the present application;
FIG. 9 is a schematic diagram of another terminal interface of a currently logged-in user provided by an exemplary embodiment of the present application;
FIG. 10 is a schematic illustration of a terminal interface of a recommendation target user according to an exemplary embodiment of the present application;
FIG. 11 is a schematic illustration of a terminal interface of a recommendation target user according to an exemplary embodiment of the present application;
Fig. 12 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present application;
Fig. 13 is a block diagram of a service object recommendation apparatus according to an exemplary embodiment of the present application.
Detailed Description
fig. 1A is a schematic terminal interface diagram of a current login user in the related art, and as shown in fig. 1A, the current login user "white" may select any service object for recommendation, for example, the service object in fig. 1A is an enterprise instant messaging application "nailing", and a recommendation message for the selected service object may be sent to each recommendation target user by specifying a recommendation target user such as "black and small" shown in fig. 1A.
Accordingly, the recommendation message of "small white" from the current login user can be received as "small black" of the recommendation target user, as shown in fig. 1B, the recommendation target user "small black" can know that the current message is the recommendation message according to the characteristics of the word of "nail" at the lower right corner of the recommendation message, and the like.
Through the manner in the related art, the current login user 'pinwhite' completes recommendation of 'nailing', so that the recommendation target user 'pinwhite' knows that the 'pinwhite' recommends an application program 'nailing' to me.
however, in the above solutions of the related art, the "small black" only knows that the "small white" recommends a certain service object to the service object, which indicates that the service object is recognized by the "small white", and the "small black" does not know whether other friends of the "small black" also recognize the service object, so that the "small black" has higher trial cost and risk, and is likely not to respond to the recommendation, resulting in a "although the recommendation message is successfully delivered, the recommendation effect is poor".
therefore, the present application improves the recommendation method for the business object to solve the above technical problems in the related art. For further explanation of the present application, the following examples are provided:
Fig. 2 is a flowchart of a method for recommending a business object according to an exemplary embodiment of the present application, and as shown in fig. 2, the method applied in a server may include the following steps:
step 202, receiving a service object recommendation request from a currently logged-in user, where the service object recommendation request includes a recommended service object and a recommendation target user.
in this embodiment, for the currently logged-in user, it is still possible to specify the recommended service object in the "recommendation content" and specify the "recommendation target user" in the manner shown in fig. 1A; correspondingly, the server can execute the technical scheme based on the application according to the received service object recommendation request so as to realize a more optimized service object recommendation scheme.
step 204, selecting an associated user having an association relationship with the current login user and the recommendation target user at the same time from the historical users of the recommended business object.
in this embodiment, in one case, address book data of the current login user and the address book data of the recommendation target user, which are respectively obtained in advance, may be called; and when a common contact of the current login user and the recommendation target user exists and the common contact is a historical user of the recommended business object, taking the common contact as the associated user. In this embodiment, by acquiring the address book data, the server may determine the associated user according to a matching condition between the current login user and the address book data of the recommendation target user, and by combining the historical user of the recommended service object. Because the address book data is actively created by the current login user and the recommendation target user, the accuracy of the final result is ensured.
In this embodiment, in another case, social relationship data of the current login user and the recommendation target user, which are respectively obtained in advance, may be retrieved, where the social relationship data is obtained by analyzing historical behavior data of a corresponding user; and when any user has a preset social relationship with the current login user and the recommendation target user at the same time and is a historical user of the recommended business object, taking the any user as the associated user. In this embodiment, according to the historical behavior data of each user, the social relationship data of each user can be analyzed more accurately based on the actual behavior of the user, so as to determine the associated user.
step 206, sending a recommendation message to the recommendation target user, where the recommendation message includes description information of the recommended service object and introduction information of a historical user using the associated user as the recommended service object.
in this embodiment, the recommendation message may be sent to the recommendation target user in various manners such as an instant messaging message, a short message, and an email; or the recommendation message and the information of the current login user can be pushed to the recommendation target user, so that the recommendation message is displayed in the social timeline of the recommendation target user as a social message sent by the current login user.
according to the technical scheme, the associated users which have an association relation with the current login user and the recommended target user at the same time are selected, the introduction information of the historical users of the recommended service object is sent to the recommended target user, so that the recommended target user can know the recognition conditions of other users except the current login user on the service object, the recognition of the recommended service object by the recommended target user is strengthened, and the attempt probability of the recommended target user on the service object is improved.
The following describes the technical solution of the present application in detail with reference to fig. 3 in conjunction with the data interaction situation between the current login user, the server, and the recommendation target user. Fig. 3 is a flowchart of another method for recommending a business object according to an exemplary embodiment of the present application, and as shown in fig. 3, the method applied in a server may include the following steps:
Step 302, the server obtains the address book data from the small white and the small black respectively.
In the present embodiment, "small white" and "small black" have not been classified as "current login user" or "recommendation target user"; in fact, any user may act as a "current login user" or a "recommendation target user", depending on the role that the user plays in the overall solution. For example, when the small white initiates a service recommendation request for the small black to the server, the small white is used as the current login user and the small black is used as the recommendation target user in the recommendation process; correspondingly, when the small black initiates a service recommendation request for the small white to the server, the small black is used as the current login user and the small white is used as the recommendation target user in the recommendation process.
In this embodiment, the address book may be a mobile phone address book of each user, or an address book of each user on other platforms such as an instant messaging application, which is not limited in this application. If the address book can be located locally in the mobile phone of the user, the data needs to be uploaded to the server in step 302; or the address book can be located at the cloud end, and the server acquires address book data from the cloud end after requesting the user and obtaining the related authority.
of course, it should be understood that the server may obtain the address book data of all users separately, rather than obtaining only the address book data of "small white" and "small black", and herein, only the emphasis is given because "small white" and "small black" are used for illustration, and the server may obtain the address book data of any other users.
step 304, the user's ' small white ' generates a service recommendation request on the terminal, and the server receives the service recommendation request initiated by the ' small white '.
in this embodiment, "small white" may specify a recommended business object, such as "nailing" of the enterprise instant messaging application, in "recommended content" through a terminal interface as shown in fig. 1A; meanwhile, a recommendation target, such as the user "little black", is also specified in the "recommendation target user". Accordingly, the generated service recommendation request respectively contains the description information for the "nail" and the information of "small black" as the recommendation target user (such as the mobile phone number, the instant messaging account number, etc.).
meanwhile, for the server, since the service recommendation request from the "whitelet" is received, the identity of the "whitelet" is determined as the "current login user".
Step 306, determining a recommendation target user.
in this embodiment, the server may obtain the description information of the recommended service object and the information of the recommendation target user by analyzing the service recommendation request. For example, assume that the recommendation target user included in the service recommendation request is "black and light".
Step 308, the server respectively extracts the address book data of the preset 'small white' and 'small black' according to the determined current login user and the recommendation target user; meanwhile, historical user information of the recommended business object, namely registered user data of the nail is called, so that the address book data of the small white, the address book data of the small black and the registered user data of the nail are compared.
It should be noted that: since the server has previously obtained the address book of each user, the server may perform real-time data comparison at the stage shown in fig. 3, or may perform pre-comparison and determine the correlation between the users, which is not limited in the present application.
And 310, selecting the associated user according to the comparison result, and generating a recommendation message for a recommendation target user.
In this embodiment, fig. 4 shows the user relationship under an exemplary embodiment, as shown in fig. 4: contacts such as 'small A', 'small B' and 'small C' exist in the 'small white' address book, and contacts such as 'small B', 'small C' and 'small D' exist in the 'small black' address book, so that common friends of the contacts are 'small B' and 'small C'; meanwhile, among registered users of the nail, only registration information of 'small A', 'small C' and 'small D' is recorded; thus, user "Small C" satisfies: the method belongs to registered users of the nail and common friends of the small white and the small black, and therefore the small C is taken as a selected associated user.
In step 312, the server sends a recommendation message to "little black".
in this embodiment, the server actually sends the recommendation message to the account corresponding to "small black", so that the "small black" can receive the recommendation message after logging in its own account on any terminal.
And step 314, displaying the recommendation message on the terminal in the small black state.
in this embodiment, as an exemplary embodiment, the recommendation message may be sent in the form of an "instant messaging message", a "short message", or a "mail", and is displayed on the terminal interface of the recommendation target user. As shown in fig. 5, taking "instant messaging message" as an example, when the recommendation message is displayed, it is equivalent to that "black and small" receives an instant messaging message sent by "white and small", and the message includes not only the description information of "nail" for the recommended business object, such as an icon of "nail" and "nail, a new generation of team communication mode |)! "and so on introduction information, also contains introduction information of the historical user who associated user" small C "as" nail ", such as" common friends of us (i.e., 'small black' and 'small white'): small C has also been nailed to get contact with him! "and the like.
In addition, if the current login user designates a plurality of recommendation target users at the same time, the technical solution of the present application may further include: when a plurality of recommendation target users exist, determining the relationship status among the recommendation target users; when an association relationship exists between any recommendation target user and another recommendation target user, the recommendation message sent to any recommendation target user further includes: the other recommendation target user is used as introduction information of a recommendation target user of the recommended business object.
For example, as shown in fig. 6, the "small white" may specify "small black" and "white" as the recommendation target user at the same time, and when there is an association relationship between the "small black" and the "white", as in step 308, the server determines that the "white" is located in the address book of the "small black", that is, the "white" is a friend of the "small black", as shown in fig. 4, and the "white" is a friend of the "small white" and the "small black", and belongs to the "small white" and the "small black" at the same time, and the "nail" has not been registered yet. Therefore, when generating a recommendation message sent to "small black", the server also contains, in addition to the description information of "nail" and the introduction information of the historical user who is "nail" for "small C", the introduction information of "white and white" as the recommendation target user, for example, the introduction information may be "our common friend" shown in fig. 7: small B will also be added with the nail, so that "small black" not only knows the approval and recommendation of "white" to "nail", but also knows the approval of "small C" to "nail", and "small B" is also recommended and has the possibility of approving "nail", so that "small black" psychologically generates the feeling of "both agree to recommended business objects" or "both try to recommend business objects", further enhancing the approval feeling to recommended business objects, and contributing to further reducing the psychological burden of recommending target users to try.
Fig. 8 is a flowchart of another method for recommending a business object according to an exemplary embodiment of the present application, and as shown in fig. 8, the method applied in a server may include the following steps:
In step 802, the server obtains historical behavior data of "small white" and "small black".
In the present embodiment, similarly to step 302, "small white" and "small black" have not been classified as "current login user" or "recommendation target user". Meanwhile, the server may obtain the historical behavior data of all users respectively, instead of obtaining only the historical behavior data of "small white" and "small black", and here, only because the "small white" and the "small black" are emphasized for the purpose of illustration, the server may also obtain the historical behavior data of any other user actually.
and step 804, analyzing the social relationship data corresponding to each user according to the acquired historical behavior data.
In this embodiment, the historical behavior data may include at least one of: communication behaviors (including all types of communication such as telephone, short message, instant messaging, mail and the like), transfer behaviors, logistics receiving and sending behaviors (such as sending express and the like), account binding behaviors and the like. By acquiring historical behavior data, the relationship between users can be determined according to behavior analysis between the users, and corresponding social relationship data can be obtained through analysis.
It should be noted that: the server can analyze historical behavior data of each user in advance to obtain corresponding social relationship data; or, the server may also analyze the corresponding social relationship data in real time when receiving a service recommendation request initiated by a certain user. This application is not intended to be limiting.
Step 806, the user's ' small white ' generates a service recommendation request on the terminal, and the server receives the service recommendation request initiated by the ' small white '.
In this embodiment, "small white" may specify a recommended business object, such as "nailing" of the enterprise instant messaging application, in "recommended content" through a terminal interface as shown in fig. 1A; meanwhile, a recommendation target, such as the user "little black", is also specified in the "recommendation target user".
Of course, the "business object" in the present application is not limited to "enterprise instant messaging application" or "application program", but may also be a movie, an electronic device, a book, or any physical or virtual object; in fact, any object may be recommended as a business object in the present application, and the present application does not limit this.
For example, as shown in fig. 9, "white" may specify a type of headphone (i.e., "2015 latest exploded bluetooth headset and subwoofer … …" shown in fig. 9) as the recommended service object in "recommended content"; meanwhile, the user such as the user with the color of small black and the user with the color of white can be designated as the recommendation target user so as to receive the corresponding recommendation message.
In addition, besides the type of the "business object" is not limited, the application also does not limit the sending form of the recommendation message. In fact, besides the form of "instant messaging message" shown in fig. 5 or fig. 7, the sending of the recommendation message may also be implemented based on the social network platform. For example, as shown in fig. 9, "white small" may select a social network platform "work circle" in "recommended manner"; of course, on the one hand, the "work circle" may be set as a default recommendation mode without selecting "small white", and on the other hand, the "small white" may also select other recommendation modes, such as "instant messaging message", "mail", and the like.
step 808, determining a recommendation target user.
step 810, the server respectively extracts social relationship data corresponding to the small white and the small black according to the determined current login user and the recommendation target user; meanwhile, historical user information of the recommended business object, namely purchased user data of the Bluetooth headset, is called, so that the social relationship data of the small white and the social relationship data of the small black are compared with the purchased user data of the Bluetooth headset.
And step 812, selecting the associated user according to the comparison result, and generating a recommendation message for the recommendation target user.
in this embodiment, in fig. 4, replacing the "small white address book" with the "small white social relationship", replacing the "small black address book" with the "small black social relationship", and replacing the "nailed registered user" with the "purchased user of the bluetooth headset", the associated user may be determined in a manner similar to that in step 310, and details are not repeated here; for example, the associated user may be "Small C".
in step 814, the server sends a recommendation message to "little black".
step 816, displaying the recommendation message on the terminal in "small black".
In this embodiment, the server may use the current login user "small white" as a sender to push the recommendation message to the recommendation target user "small black" (and other recommendation target users; here, only "small black" is taken as an example for explanation), so that the recommendation message is shown in a social timeline (timeline) of the recommendation target user "small black" as a social message sent by the current login user "small white".
as shown in FIG. 10, "Xiao-Black" can view the recommendation message from "Xiao-Bai" in its "work circle," including descriptive information for the recommended business object "Bluetooth headset," including pictures and text "recent exploded Wireless Bluetooth headset side subwoofer … … ￥ 198" and the like, as well as introductory information for the purchased users associated with user "XiaoC" as "Bluetooth headset" our co-friends that XiaoC has also purchased 2015-to-catch up bar! ", and the like.
meanwhile, similarly to fig. 7, if "small white" selects a plurality of recommendation target users such as "small black" and "small B (i.e., white)" at the same time, as shown in fig. 11, in addition to the information shown in fig. 10, "small black" can also see "our common friends: the small B is also added with the introduction information of the Chinese character- "in.
In addition, in the technical solution of the present application, the recommendation message may further include: an acquisition link for the recommended business object. For example, as shown in fig. 5 or fig. 7, when the user "little black" clicks the received recommendation message in the form of the instant messaging message, the user may jump to the download interface of "nail" so as to facilitate the experience of direct download of "little black"; as shown in fig. 10 or fig. 11, when the user "black and small" clicks the received recommendation message in the form of social message, the user can jump to the purchase interface corresponding to the "bluetooth headset" to browse and purchase "black and small".
FIG. 12 shows a schematic block diagram of an electronic device according to an exemplary embodiment of the present application. Referring to fig. 12, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, but may also include hardware required for other services. The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the recommendation device of the business object on the logic level. Of course, besides the software implementation, the present application does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.
Referring to fig. 13, in a software implementation, the service object recommendation apparatus may include a receiving unit, a selecting unit, and a sending unit. Wherein:
the system comprises a receiving unit, a recommending unit and a recommending unit, wherein the receiving unit receives a business object recommending request from a current login user, and the business object recommending request comprises a recommended business object and a recommending target user;
The selecting unit is used for selecting associated users which have an association relationship with the current login user and the recommendation target user simultaneously from historical users of the recommended business object;
And the sending unit is used for sending a recommendation message to the recommendation target user, wherein the recommendation message comprises the description information of the recommended service object and the introduction information of the historical user taking the associated user as the recommended service object.
Optionally, the selecting unit is specifically configured to:
Calling address book data of the current login user and the recommended target user, which are respectively obtained in advance;
and when a common contact of the current login user and the recommendation target user exists and the common contact is a historical user of the recommended business object, taking the common contact as the associated user.
Optionally, the selecting unit is specifically configured to:
Calling social relationship data of the current login user and the recommendation target user, which are respectively obtained in advance, wherein the social relationship data are obtained by analyzing historical behavior data of corresponding users;
and when any user has a preset social relationship with the current login user and the recommendation target user at the same time and is a historical user of the recommended business object, taking the any user as the associated user.
Optionally, the method further includes:
A determining unit that determines a relationship status between the respective recommendation target users when there are a plurality of recommendation target users;
When an association relationship exists between any recommendation target user and another recommendation target user, the recommendation message sent to any recommendation target user further includes: the other recommendation target user is used as introduction information of a recommendation target user of the recommended business object.
Optionally, the sending unit is specifically configured to:
And pushing the recommendation message to the recommendation target user by taking the current login user as a sender so that the recommendation message is taken as a social message sent by the current login user and is displayed in a social timeline of the recommendation target user.
optionally, the recommendation message further includes: an acquisition link for the recommended business object.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
the above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (14)
1. A method for recommending a business object, comprising:
Receiving a business object recommendation request from a current login user, wherein the business object recommendation request comprises a recommended business object and a recommendation target user, and the recommended business object is any object selected by the current login user;
Selecting associated users which have an association relationship with the current login user and the recommendation target user simultaneously from historical users of the recommended business object;
And sending a recommendation message to the recommendation target user, wherein the recommendation message comprises the description information of the recommended service object and the introduction information of the historical user of which the associated user serves as the recommended service object.
2. The method of claim 1, wherein selecting, from the historical users of the recommended business object, an associated user that has an association relationship with both the current logged-in user and the recommendation target user comprises:
calling address book data of the current login user and the recommended target user, which are respectively obtained in advance;
and when a common contact of the current login user and the recommendation target user exists and the common contact is a historical user of the recommended business object, taking the common contact as the associated user.
3. The method of claim 1, wherein selecting, from the historical users of the recommended business object, an associated user that has an association relationship with both the current logged-in user and the recommendation target user comprises:
calling social relationship data of the current login user and the recommendation target user, which are respectively obtained in advance, wherein the social relationship data are obtained by analyzing historical behavior data of corresponding users;
And when any user has a preset social relationship with the current login user and the recommendation target user at the same time and is a historical user of the recommended business object, taking the any user as the associated user.
4. The method of claim 1, further comprising:
When a plurality of recommendation target users exist, determining the relationship status among the recommendation target users;
when an association relationship exists between any recommendation target user and another recommendation target user, the recommendation message sent to any recommendation target user further includes: the other recommendation target user is used as introduction information of a recommendation target user of the recommended business object.
5. The method of claim 1, wherein sending the recommendation message to the recommendation target user comprises:
And pushing the recommendation message to the recommendation target user by taking the current login user as a sender so that the recommendation message is taken as a social message sent by the current login user and is displayed in a social timeline of the recommendation target user.
6. The method of claim 1, wherein the recommendation message further comprises: an acquisition link for the recommended business object.
7. An apparatus for recommending a business object, comprising:
The system comprises a receiving unit, a recommendation unit and a recommendation unit, wherein the receiving unit receives a service object recommendation request from a current login user, the service object recommendation request comprises a recommended service object and a recommendation target user, and the recommended service object is any object selected by the current login user;
The selecting unit is used for selecting associated users which have an association relationship with the current login user and the recommendation target user simultaneously from historical users of the recommended business object;
and the sending unit is used for sending a recommendation message to the recommendation target user, wherein the recommendation message comprises the description information of the recommended service object and the introduction information of the historical user taking the associated user as the recommended service object.
8. The apparatus according to claim 7, wherein the selecting unit is specifically configured to:
calling address book data of the current login user and the recommended target user, which are respectively obtained in advance;
and when a common contact of the current login user and the recommendation target user exists and the common contact is a historical user of the recommended business object, taking the common contact as the associated user.
9. The apparatus according to claim 7, wherein the selecting unit is specifically configured to:
Calling social relationship data of the current login user and the recommendation target user, which are respectively obtained in advance, wherein the social relationship data are obtained by analyzing historical behavior data of corresponding users;
And when any user has a preset social relationship with the current login user and the recommendation target user at the same time and is a historical user of the recommended business object, taking the any user as the associated user.
10. The apparatus of claim 7, further comprising:
A determining unit that determines a relationship status between the respective recommendation target users when there are a plurality of recommendation target users;
When an association relationship exists between any recommendation target user and another recommendation target user, the recommendation message sent to any recommendation target user further includes: the other recommendation target user is used as introduction information of a recommendation target user of the recommended business object.
11. The apparatus according to claim 7, wherein the sending unit is specifically configured to:
And pushing the recommendation message to the recommendation target user by taking the current login user as a sender so that the recommendation message is taken as a social message sent by the current login user and is displayed in a social timeline of the recommendation target user.
12. the apparatus of claim 7, wherein the recommendation message further comprises: an acquisition link for the recommended business object.
13. An electronic device, comprising:
a processor;
A memory for storing processor-executable instructions;
Wherein the processor is configured with executable instructions to implement the method of any one of claims 1-6.
14. a computer-readable storage medium having stored thereon computer instructions, which, when executed by a processor, carry out the steps of the method according to any one of claims 1-6.
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Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107527274B (en) * | 2017-09-04 | 2021-09-03 | 北京京东尚科信息技术有限公司 | Information recommendation platform, device, system, method and terminal |
| CN108737506A (en) * | 2018-04-27 | 2018-11-02 | 苏州达家迎信息技术有限公司 | A kind of application method for pushing, equipment, storage medium and system |
| CN108833467B (en) * | 2018-04-27 | 2021-02-02 | 苏州达家迎信息技术有限公司 | Application pushing method, device, storage medium and system |
| CN109218429B (en) * | 2018-09-26 | 2021-05-18 | 深圳市云歌人工智能技术有限公司 | Method, apparatus and storage medium for publishing information based on target selection |
| CN109446458B (en) * | 2018-09-26 | 2021-08-03 | 深圳市云歌人工智能技术有限公司 | Method, apparatus and storage medium for distributing information and processing information |
| CN109637225B (en) * | 2018-12-20 | 2021-03-12 | 广东小天才科技有限公司 | Interactive learning method and system |
| CN110086877B (en) * | 2019-04-30 | 2022-10-21 | 上海连尚网络科技有限公司 | Application program sharing and information sharing display method, device, equipment and medium |
| CN110225122B (en) * | 2019-06-13 | 2021-11-23 | 广州酷狗计算机科技有限公司 | Message pushing method, device, equipment and storage medium |
| CN110276593A (en) * | 2019-06-19 | 2019-09-24 | 腾讯科技（深圳）有限公司 | Object recommendation method, apparatus, server and storage medium |
| CN112231567A (en) * | 2020-10-20 | 2021-01-15 | 王明烨 | Method and device for accurately pushing information |
| CN112417304B (en) * | 2020-12-10 | 2023-06-23 | 北方工业大学 | Data analysis service recommendation method and system for constructing data analysis flow |
| CN112613715B (en) * | 2020-12-16 | 2023-09-05 | 重庆电子工程职业学院 | Intelligent management system for traditional Chinese medicine diseases |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102130896A (en) * | 2010-01-14 | 2011-07-20 | 腾讯科技（深圳）有限公司 | Method and system for correlating network applications |
| CN102571971A (en) * | 2012-01-21 | 2012-07-11 | 上海傲动信息技术有限公司 | Method and system for shafting commodity information on mobile terminal |
| CN103886103A (en) * | 2014-04-10 | 2014-06-25 | 广东欧珀移动通信有限公司 | Recommendation method and system for application programs |
| CN104281599A (en) * | 2013-07-02 | 2015-01-14 | 北京千橡网景科技发展有限公司 | Method and device for recommending information to user in social network |
| CN104601641A (en) * | 2014-05-23 | 2015-05-06 | 腾讯科技（深圳）有限公司 | Application link sharing method, device and system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9111255B2 (en) * | 2010-08-31 | 2015-08-18 | Nokia Technologies Oy | Methods, apparatuses and computer program products for determining shared friends of individuals |
| US9619534B2 (en) * | 2010-09-10 | 2017-04-11 | Salesforce.Com, Inc. | Probabilistic tree-structured learning system for extracting contact data from quotes |
| CN104899196A (en) * | 2014-03-03 | 2015-09-09 | 联想(北京)有限公司 | Information processing method and apparatus |
-
2015
- 2015-09-23 CN CN201510614129.6A patent/CN106550004B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102130896A (en) * | 2010-01-14 | 2011-07-20 | 腾讯科技（深圳）有限公司 | Method and system for correlating network applications |
| CN102571971A (en) * | 2012-01-21 | 2012-07-11 | 上海傲动信息技术有限公司 | Method and system for shafting commodity information on mobile terminal |
| CN104281599A (en) * | 2013-07-02 | 2015-01-14 | 北京千橡网景科技发展有限公司 | Method and device for recommending information to user in social network |
| CN103886103A (en) * | 2014-04-10 | 2014-06-25 | 广东欧珀移动通信有限公司 | Recommendation method and system for application programs |
| CN104601641A (en) * | 2014-05-23 | 2015-05-06 | 腾讯科技（深圳）有限公司 | Application link sharing method, device and system |
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