CN109886824B - Friend-making recommendation method, friend-making recommendation device, friend-making recommendation server, express cabinet and friend-making recommendation storage medium - Google Patents
Friend-making recommendation method, friend-making recommendation device, friend-making recommendation server, express cabinet and friend-making recommendation storage medium Download PDFInfo
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- CN109886824B CN109886824B CN201910138194.4A CN201910138194A CN109886824B CN 109886824 B CN109886824 B CN 109886824B CN 201910138194 A CN201910138194 A CN 201910138194A CN 109886824 B CN109886824 B CN 109886824B
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The embodiment of the invention discloses a friend-making recommendation method, a friend-making recommendation device, a server, an express cabinet and a storage medium. The method comprises the following steps: responding to a use request of a user for an express cabinet, extracting friend making data of the user, wherein the friend making data comprises dynamic operation behavior data associated with the express cabinet; determining information of friends to be recommended, which are matched with the friend making data; and sending the information of the friends to be recommended to the express cabinet and/or the user terminal. According to the technical scheme, the friend making data of the user are automatically acquired, friends to be recommended are intelligently matched for the user, and accuracy and satisfaction of friend matching of the user are improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of express delivery, in particular to a friend-making recommendation method, a friend-making recommendation device, a server, an express cabinet and a storage medium.
Background
With the rapid development of electronic commerce and the acceleration of the life rhythm of people, people have a strong desire to make friends, and at present, people make friends mainly through friend making software.
In the prior art, friend making software needs users to actively fill in friend making information related to interests, hobbies and the like; and if the friend making information of the user changes, the user is required to dynamically update the friend making information on the friend making software, so that the user is unwilling to take time to fill in the friend making information or the user fills in the friend making information at will.
In the prior art, users are not willing to spend time to fill in friend making information in friend making software or fill in friend making information in the friend making software at will, which causes distortion of the friend making information of the users, leads to incorrect recommendation of friends of the users by the friend making software and reduces accuracy and satisfaction of friend matching of the users.
Disclosure of Invention
The embodiment of the invention provides a friend-making recommendation method, a friend-making recommendation device, a server, an express cabinet and a storage medium, which are used for automatically extracting friend-making data of a user, intelligently matching friends to be recommended for the user and improving accuracy and satisfaction of friend matching of the user.
In a first aspect, an embodiment of the present invention provides a friend-making recommendation method, including:
responding to a use request of a user for an express cabinet, extracting friend making data of the user, wherein the friend making data comprises dynamic operation behavior data associated with the express cabinet;
Determining information of friends to be recommended, which are matched with the friend making data;
and sending the information of the friends to be recommended to the express cabinet and/or the user terminal.
Optionally, the friend making data further includes: user base data;
accordingly, the extracting the friend-making data of the user includes:
responding to a use request of a user for an express cabinet, and acquiring a unique identifier of the user from the use request;
and acquiring user basic data of the user and dynamic operation behavior data associated with the express cabinet according to the unique identification.
Optionally, the determining the information of the friends to be recommended, which are matched with the friend making data, includes:
acquiring an alternative friend information set bound with the express cabinet;
inputting the candidate friend information set and the friend making data into a preset matching model to obtain matching similarity of each candidate friend information output by the matching model and the friend making data;
selecting candidate friend information corresponding to the matching similarity greater than or equal to the similarity threshold, or selecting preset number of candidate friend information as friend information to be recommended;
the matching similarity corresponding to the preset number of candidate friend information is larger than the matching similarity corresponding to other candidate friend information in the candidate friend information set.
Optionally, after sending the information of the friend to be recommended to the express cabinet and/or the user terminal, the method further includes:
and if the moment that the user and the friend to be recommended appear in the preset range of the express cabinet is detected to be in the preset duration, controlling the user and the client of the friend to be recommended to send out preset prompt tones.
In a second aspect, the embodiment of the present invention further provides a friend-making recommendation method, including:
receiving information of friends to be recommended, which are sent by a server, wherein the information of the friends to be recommended is matched with the friend making data, the friend making data are extracted by the server, and the friend making data comprise dynamic operation behavior data associated with the express cabinet;
and prompting the information of the friends to be recommended to the user.
In a third aspect, an embodiment of the present invention further provides a friend-making recommendation apparatus, which is applied to a server, including:
the friend making data extraction module is used for responding to a use request of a user on the express cabinet and extracting friend making data of the user, wherein the friend making data comprise dynamic operation behavior data associated with the express cabinet;
the recommendation information determining module is used for determining information of friends to be recommended, which are matched with the friend making data;
And the recommendation execution module is used for sending the information of the friends to be recommended to the express cabinet and/or the user terminal.
In a fourth aspect, an embodiment of the present invention further provides a friend-making recommendation device, which is applied to an express cabinet, including:
the recommendation information receiving module is used for receiving information of friends to be recommended, which are sent by the server, wherein the information of the friends to be recommended is matched with the friend making data, the friend making data are extracted by the server, and the friend making data comprise dynamic operation behavior data associated with the express cabinet;
and the friend recommending module is used for prompting the information of the friends to be recommended to the user.
In a fifth aspect, an embodiment of the present invention further provides a server, where the server includes:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the friend-making recommendation method applied to the server as provided by any embodiment of the present invention.
In a sixth aspect, an embodiment of the present invention further provides an express cabinet, including:
a cabinet body;
a memory mounted on the cabinet, a processor, and a computer program stored on the memory and executable on the processor;
When the processor executes the program, the friend-making recommendation method applied to the express cabinet provided by any embodiment of the invention is realized.
In a seventh aspect, the embodiment of the present invention further provides a computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the friend-making recommendation method provided by any embodiment of the present invention.
The method and the device are applied to a scene of making friends by using the express cabinet, and dynamic operation behavior data associated with the express cabinet of a user is automatically extracted by responding to a use request of the user for the express cabinet, and the dynamic operation behavior data can embody the use behavior of the user based on the express cabinet, so that the data has enough authenticity and instantaneity; and then, determining information of friends to be recommended, which are matched with the user, according to the dynamic operation behavior data, namely determining friends, which are matched with the behavior of the user using the express cabinet, and sending the information of the friends to be recommended to the express cabinet and/or the user terminal so as to automatically recommend the friends to the user. Therefore, in the process of using the express cabinet by the user, the embodiment of the invention automatically extracts dynamic operation behavior data with authenticity and instantaneity in a software and hardware combined mode, and recommends matched friends according to the dynamic operation behavior data, so that the user does not need to manually fill in friend making information, and the efficiency, accuracy and satisfaction of friend matching of the user in a friend making scene by using the express cabinet are improved.
Drawings
FIG. 1 is a flow chart of a friend-making recommendation method in accordance with a first embodiment of the present invention;
FIG. 2 is a flow chart of a friend-making recommendation method in a second embodiment of the present invention;
FIG. 3 is a flowchart of an implementation of an application scenario to which embodiments of the present invention are applicable;
fig. 4 is a schematic structural diagram of a friend-making recommendation device in a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a friend-making recommendation device in a fifth embodiment of the present invention;
fig. 6 is a schematic structural diagram of a server in a sixth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of a friend-making recommendation method in a first embodiment of the present invention, where the method may be applied to a case of recommending matched friends for a user in a scenario of making friends by using an express cabinet, and the method may be performed by a friend-making recommendation device, which may be implemented by software and/or hardware, and may be generally integrated in various servers providing friend-making recommendation services. Referring to fig. 1, the method specifically includes the following steps:
Step 110, responding to a user's use request of the express cabinet, extracting friend making data of the user, wherein the friend making data comprises dynamic operation behavior data associated with the express cabinet.
Typically, the user's request for use of the express cabinet may include: the user sends a request for sending a piece, taking a piece and temporarily storing an article on the express cabinet. Optionally, after receiving the use request of the express cabinet input by the user, the server extracts friend making data of the user from the database, so as to match suitable friends for the user. Wherein the dating data includes dynamic operational behavior data associated with the express cabinet, the dynamic operational behavior data including: after sending a use request for the express cabinet, the user dynamically operates behavior data of the express cabinet; on the basis, the method can further comprise dynamic operation behavior data of the user on the express cabinet at historical time.
In particular, the dynamic operational behavior data may include: the user sends a piece on the express delivery cabinet, gets the piece and uses the data of the express delivery cabinet temporary storage article, gets the data of the piece appreciation and appreciation express delivery person on the express delivery cabinet, and purchases on other application programs and sends at least one of the data of the express delivery of taking or temporary storage with the express delivery cabinet.
For example, the pickup data of the user on the express cabinet may specifically include: get a time, get a mode, get a person's name, get a person's phone number, get a express delivery cabinet number, express delivery cabinet type, express delivery cabinet place administrative area, get express delivery whether to pay for, pay for amount, get a time consuming, whether to appreciate, to appreciate amount, pay mode, whether to detain express delivery, detain duration, whether to allow to deliver express delivery into cabinet etc..
Optionally, the friend-making data includes, in addition to dynamic operational behavior data associated with the express cabinet: user base data. For example, the WeChat account number and the mobile phone number bound on the express cabinet by the user, the binding time of the WeChat account number, real-name authentication data, face recognition data, gender, address and other user real information. The user basic data includes, in addition to user real information obtained from the user registration information by the server, presumption information presumed by the server according to dynamic operation behavior data of the user, for example, presuming that the user is female according to multiple purchases of female cosmetics by the user.
Optionally, responding to the request of the user for using the express cabinet, extracting friend making data of the user may include: after a user inputs a use request for the express cabinet on the express cabinet, the server responds to the use request for the express cabinet by the user and obtains a unique identifier of the user from the use request, wherein the unique identifier can be information such as a mobile phone number or a WeChat account number bound by the user on the express cabinet, and the server extracts user basic data of the user and dynamic operation behavior data associated with the express cabinet from data associated with the express cabinet according to the unique identifier.
Optionally, after the server obtains the user friend-making data, a corresponding label is added to the friend-making data, where the label may include attributes such as a category, a state, a value range, and a threshold range. Specifically, the label types may include a fuzzy label for marking data with non-verified authenticity and a non-fuzzy label for marking data with confirmed authenticity, and may also include a characteristic label for classifying dynamic operation behavior data associated with the express cabinet, for example, the number of times of taking the package is 5-20 times, the time of taking the package is less than 10 minutes, the mother and infant goods, the consumption amount is 500-1000 yuan, and the like. The state of the tag may include: in use, unused, discarded, etc. For the value range, the values of the label are 5-20 times and 500-1000 yuan. The threshold range is the upper limit and the lower limit of the value range of the tag, the data corresponding to the tag with the value range exceeding the threshold range is invalid data and cannot be used as friend making data of a user, the server screens the acquired data according to the threshold range, and the threshold range can be adjusted according to requirements.
Optionally, in order to ensure the authenticity of the data contained in the user friend-making data, the server acquires third party verification data bound with the user from the third party platform according to the obtained user authenticity information, so as to verify and update the user friend-making data. In particular, the third party may include public security systems, shopping applications, and the like; the server can acquire real personal information of the user, such as information of name, gender, age, address and the like, from the public security system according to the identification card number filled when the user registers on the express cabinet, and can also acquire shopping records of the user on the corresponding shopping application program, such as information of shopping times, consumption amount, types of purchased goods and the like of the user according to account information of the shopping application program bound on the express cabinet.
Optionally, after the server obtains the third party verification data, verifying the data with the fuzzy label in the friend-making data according to the third party verification data, and replacing the data with the fuzzy label inconsistent with the third party verification data with the corresponding data in the third party verification data, thereby obtaining effective friend-making data and improving the accuracy of the user friend-making data.
And 120, determining information of friends to be recommended, which are matched with the friend making data.
Optionally, after the server obtains the friend making data, the server obtains the candidate friend information set bound with the express cabinet, and selects the candidate friend information with the highest similarity with the user friend making data from the candidate friend information set as the friend information to be recommended. The candidate friend information set comprises friend making information of other users bound with the express cabinet.
Optionally, selecting the candidate friend information matched with the user from the candidate friend information set by adopting a preset matching model. In order to match friends for users from multiple aspects and improve matching accuracy, a preset number of matching dimensions are preset in a matching model, data corresponding to each matching dimension is selected from friend making data of the users and candidate friend information sets, similarity between the users and the candidate friends in each matching dimension is calculated, matching similarity between the users and the candidate friends is calculated according to weight occupied by each matching dimension, and friends to be recommended matched with the users are selected according to the size of the matching similarity. The preset number can be three, five or other numbers; in the working period of the matching model, in order to improve the matching accuracy, the number of matching dimensions can be increased and decreased at any time; after the friends are recommended to the user, the matching model can be optimized and adjusted according to the interaction effect between the user and the recommended friends.
Optionally, after the server obtains the friend making data of the user and the candidate friend making information set bound with the express cabinet, inputting the candidate friend making information set and the friend making data into a preset matching model to obtain matching similarity of each candidate friend making information output by the matching model and the friend making data, and selecting candidate friend making information corresponding to the matching similarity greater than or equal to a similarity threshold, or selecting a preset number of candidate friend making information as friend information to be recommended, wherein the matching similarity corresponding to the preset number of candidate friend making information is greater than the matching similarity corresponding to other candidate friend making information in the candidate friend making information set.
For example, six matching dimensions of gender, age, user address, picking time, consumption level and interest may be preset in the matching model, after user friend making data and candidate friend information sets are obtained, corresponding data are selected according to the six matching dimensions, and similarity between the user and all candidate friends in each matching dimension is calculated, for example, similarity between candidate friends with opposite gender of the user is recorded as 1 in the gender dimension, the similarity between candidate friends with the same gender is recorded as 0 in the picking time dimension, and similarity between candidate friends with the same picking time of the user is recorded as 1 in the non-uniform friend is recorded as 0 in the picking time dimension. And calculating the matching similarity of each candidate friend and the user according to the weight occupied by each matching dimension, wherein the weight of the sex dimension is 0.2, the weight of the age dimension is 0.2, and the dimensions of the user address, the picking time, the consumption level, the interest and hobbies are respectively 0.15, and multiplying and then summing the similarity under each dimension with the weight corresponding to the dimension for each candidate friend information to obtain the matching similarity of the candidate friend information and the user friend making data. The server selects the matching similarity greater than or equal to the similarity threshold from all the matching similarities, and the similarity threshold can be 5, and the candidate friend information corresponding to the selected matching similarity is used as friend information to be recommended.
Optionally, after obtaining the matching similarity between each piece of candidate friend information and the user friend making data, the server may select a preset number of pieces of candidate friend information as friend information to be recommended, where the matching similarity corresponding to the preset number of pieces of candidate friend information is greater than the matching similarity corresponding to other pieces of candidate friend information, that is, the preset number of pieces of candidate friend information with the highest matching similarity with the user friend making data is selected from all pieces of candidate friend information. The server may sort all the matching similarities in descending order, and select a preset number of matching similarities from the first one, or select a preset number of matching similarities with higher similarity from all the matching similarities according to other algorithms, without sorting all the matching similarities.
Preferably, in order to improve the accuracy of making friends, invalid friend making data is removed from the friend making data, valid friend making data is reserved, and information of friends to be recommended, which are matched with the valid friend making data, is further determined. The information of the friends to be recommended, which are determined to be matched with the effective friend making data, is the same as the above-described information method for determining the friends to be recommended, which are determined to be matched with the friend making data, and the difference is only that the friend making data are optimized to be the effective friend making data, which is not described herein.
And 130, sending the information of the friends to be recommended to an express cabinet and/or a user terminal.
Optionally, after obtaining the information of the friends to be recommended, the server sends the information to the express cabinet, or sends the information to the mobile terminal of the user, or simultaneously recommends the information of the friends to be recommended to the user through the express cabinet and the mobile terminal. Specifically, the server recommending the matched friend-making information to the user may include: the method comprises the steps of sending a voice prompt friend-making information instruction to an express cabinet to control the express cabinet to provide friend-making prompt information for a user through voice playing equipment on the express cabinet so as to remind the user to timely check recommended friend-making information and avoid missing the friend-making information, wherein the voice broadcasting equipment can be a microphone. Or the server respectively sends the instruction of recommending the friend information to the user at the express cabinet and the client, namely the server controls the express cabinet to display the friend making link corresponding to the friend information to be recommended on a display screen of the express cabinet, or the friend making link corresponding to the friend information to be recommended is pushed through an application program bound with the express cabinet, so that the friend information matched with the user is recommended to the user from multiple aspects.
Optionally, after the server sends the information of the friends to be recommended to the express cabinet and/or the user terminal, the method further includes: if the moment that the user and the friends to be recommended appear in the preset range of the express cabinet is detected to be within the preset duration, the user and the client of the friends to be recommended are controlled to send out preset prompt tones. Specifically, according to global positioning system (Global Positioning System, GPS) data in the express cabinet and the mobile terminal of the user, whether the moment when the user and the friend to be recommended appear in the preset range of the express cabinet is within the preset duration is judged, if so, the server controls the user and the client of the friend to be recommended to send out preset prompt tones. The preset duration may be 20 seconds, 30 seconds or other durations, the preset range may be a range of 10 meters, 15 meters of the square circle of the express cabinet or a range beside the express cabinet, and the preset prompt tone may be a marked audio, for example, a dripping sound, a beeping sound, or a user preset audio.
The method and the device are applied to a scene of making friends by using the express cabinet, and dynamic operation behavior data associated with the express cabinet of a user is automatically extracted by responding to a use request of the user for the express cabinet, and the dynamic operation behavior data can embody the use behavior of the user based on the express cabinet, so that the data has enough authenticity and instantaneity; and then, determining information of friends to be recommended, which are matched with the user, according to the dynamic operation behavior data, namely determining friends, which are matched with the behavior of the user using the express cabinet, and sending the information of the friends to be recommended to the express cabinet and/or the user terminal so as to automatically recommend the friends to the user. Therefore, in the process of using the express cabinet by the user, the embodiment of the invention automatically extracts dynamic operation behavior data with authenticity and instantaneity in a software and hardware combined mode, and recommends matched friends according to the dynamic operation behavior data, so that the user does not need to manually fill in friend making information, and the efficiency, accuracy and satisfaction of friend matching of the user in a friend making scene by using the express cabinet are improved.
Example two
Fig. 2 is a flowchart of a friend-making recommendation method in a second embodiment of the present invention, where the method may be applied to a case where a matching friend is recommended to a user based on an express cabinet, and the method may be performed by a friend-making recommendation device, and the device may be implemented by software and/or hardware, and may be generally integrated in various express cabinets that provide friend-making recommendation services. Referring to fig. 2, the method specifically includes the following steps:
Step 210, receiving information of friends to be recommended sent by a server, wherein the information of friends to be recommended is matched with friend making data, the friend making data is extracted by the server, and the friend making data comprises dynamic operation behavior data associated with an express cabinet.
Optionally, the express cabinet receives information of friends to be recommended, which are matched with the user and are sent by the server, and an instruction for recommending the information of the friends to be recommended to the user. The friend making data of the user is extracted by the server according to the unique identification of the user, and comprises dynamic operation behavior data associated with the express cabinet, such as data of a user sending a piece on the express cabinet, taking the piece and temporarily storing articles by using the express cabinet, data of a person who takes the piece to be rewarded and rewarded on the express cabinet, and data of express purchased on other application programs and sent to be taken or temporarily stored by using the express cabinet; the dating data also includes user base data including personal information of the user.
Step 220, information of friends to be recommended is prompted to the user.
Optionally, after receiving an instruction of recommending the information of the friend to be recommended to the user sent by the server, the express cabinet provides friend-making prompt information for the user through the voice playing device, for example, a microphone is used for carrying out voice prompt on the user: the user can pay attention to friends at the mobile phone end by taking a friend to wait for the inquiry. The express cabinet can also display friend making links to the user through the display screen, the friend making links correspond to the information of friends to be recommended, which are sent by the server, the user can check the friend information matched with the server by clicking the friend making links, and online interactive communication is carried out on the friends to be recommended by paying attention to the friends.
Optionally, after the express cabinet prompts the information of the friend to be recommended to the user, if the user sends a request for using the express cabinet to the express cabinet again, whether the user and the moment when the friend to be recommended appears in the preset range of the express cabinet are within preset duration is judged according to the positioning data in the express cabinet and the user mobile terminal, if so, the server controls the user and the client of the friend to be recommended to send preset prompt tones, and the user and the friend to be recommended meet and interact under the development line of the friend to realize face-to-face communication between the user and the friend.
According to the technical scheme, the express cabinet can receive information of friends to be recommended, which are sent by the server, the server automatically extracts dynamic operation behavior data with authenticity and instantaneity in a software and hardware combined mode in the process of using the express cabinet by the server, and accordingly matching is recommended, the user does not need to fill friend making information manually, and accordingly the express cabinet receives information of friends to be recommended, which is high in matching efficiency, accuracy and satisfaction; by prompting the information of the friends to be recommended to the user, the user automatically receives the information of the friends to be recommended on the using site of the express cabinet, friend making experience of the user is improved, and meanwhile friend making efficiency, accuracy and satisfaction are improved.
Example III
Fig. 3 is a flowchart of an implementation of an application scenario to which the embodiment of the present invention is applicable, and this embodiment may be combined with each of the alternatives in the foregoing embodiment. Specifically, referring to fig. 3, the method may include the steps of:
firstly, a user sends a request for using the express cabinet to a server.
Optionally, the user goes to the express delivery cabinet, selects corresponding operation page from the service of picking up, sending or temporarily storing articles on the display screen of the express delivery cabinet according to own needs, sends the request for using the express delivery cabinet to the server by scanning the two-dimensional code in the operation page, or sends the request for using the express delivery cabinet to the server by inputting the corresponding number in the edit box of the operation page, so that the server controls the express delivery cabinet to open the corresponding gate according to the request for using by the user, thereby facilitating the user to send, pick up or temporarily store articles.
And then, after the server determines that the user is bound with the express cabinet, the server acquires friend making data of the user.
Optionally, after receiving a use request sent by a user, the server determines whether the user binds with the express cabinet, for example, whether the user binds a mobile phone number or a WeChat account with the express cabinet, if so, identifies use information corresponding to the mobile phone number or the WeChat account, and displays the use information to the user through a display screen of the express cabinet; if not, prompting the user to bind with the express cabinet.
Optionally, after the server guides the user to click the information to be fetched in the fetch list, a door opening instruction is sent to the express cabinet, the express cabinet is controlled to open the corresponding grid door, the display screen displays that the cabinet door is opened, the user takes out the express and then closes the door, and therefore the user takes away the express and closes the grid door.
Optionally, after the server controls the express cabinet to open the gate, the friend-making data of the user is extracted from the database according to the mobile phone number or WeChat account bound by the user, and a corresponding label is added to the friend-making data, so that verification and update of data which is not verified to be true in the friend-making data can be conveniently performed by using third party verification data, invalid data with smaller influence factors is removed according to requirements, and only the valid data is reserved as the friend-making data of the user, thereby improving the accuracy of the friend-making data of the user.
And then, the server obtains friend information to be recommended, which is matched with the friend making data, and sends the friend information to the express cabinet.
Optionally, after the server selects effective friend making data, acquiring friend making information of other users bound with the express cabinet as an alternative friend making information set, inputting the alternative friend making information set and the friend making data into a preset matching model, obtaining matching similarity of each piece of alternative friend making information and the friend making data through calculation, selecting alternative friend making information corresponding to the matching similarity greater than or equal to a similarity threshold, or selecting preset number of pieces of alternative friend making information as friend making information to be recommended, wherein the matching similarity corresponding to the preset number of pieces of alternative friend making information is greater than the matching similarity corresponding to other pieces of alternative friend making information.
Optionally, after detecting that the user has used the express cabinet to close the cabinet door corresponding to the corresponding grid, the server sends an instruction of prompting friend-making information to the express cabinet by voice reminding, so as to control the express cabinet to provide friend-making prompting information for the user through voice playing equipment on the express cabinet, for example, a friend waits for receiving, and the user can pay attention to the friend at the mobile phone end.
And recommending the friend information to be recommended to the user by the express cabinet.
Optionally, after receiving the instruction sent by the server to recommend the information of the friend to be recommended to the user, the express cabinet displays a friend making link to the user through the display screen, the friend making link corresponds to the information of the friend to be recommended sent by the server, and the user can check the friend information matched with the server by clicking the friend making link and carry out online interactive communication with the friend to be recommended by paying attention to the friend to be recommended.
Optionally, after the express cabinet provides the information of the friend to be recommended for the user, if the user and the friend to be recommended are in the preset range of the express cabinet within the preset duration, the server controls the user and the client of the friend to be recommended to send out preset prompt tone, and the user and the friend to be recommended are interacted with each other under the development line of the friend to realize face-to-face communication between the user and the friend.
Example IV
Fig. 4 is a schematic structural diagram of a friend-making recommendation device in a fourth embodiment of the present invention. The method and the device are suitable for recommending the matched friends for the user. As shown in fig. 4, the friend-making recommendation device is applied to a server, and includes:
a friend-making data extraction module 410, configured to extract friend-making data of a user in response to a user's request for use of the express cabinet, where the friend-making data includes dynamic operation behavior data associated with the express cabinet;
the recommendation information determining module 420 is configured to determine information of friends to be recommended, which are matched with the friend making data;
and the recommendation execution module 430 is configured to send information of friends to be recommended to the express cabinet and/or the user terminal.
The method and the device are applied to a scene of making friends by using the express cabinet, and dynamic operation behavior data associated with the express cabinet of a user is automatically extracted by responding to a use request of the user for the express cabinet, and the dynamic operation behavior data can embody the use behavior of the user based on the express cabinet, so that the data has enough authenticity and instantaneity; and then, determining information of friends to be recommended, which are matched with the user, according to the dynamic operation behavior data, namely determining friends, which are matched with the behavior of the user using the express cabinet, and sending the information of the friends to be recommended to the express cabinet and/or the user terminal so as to automatically recommend the friends to the user. Therefore, in the process of using the express cabinet by the user, the embodiment of the invention automatically extracts dynamic operation behavior data with authenticity and instantaneity in a software and hardware combined mode, and recommends matched friends according to the dynamic operation behavior data, so that the user does not need to manually fill in friend making information, and the efficiency, accuracy and satisfaction of friend matching of the user in a friend making scene by using the express cabinet are improved.
Further, the friend-making data further includes: user base data;
accordingly, the dating data extraction module 410 includes: the identification acquisition unit is used for responding to a use request of the user for the express cabinet and acquiring the unique identification of the user from the use request; the data acquisition unit is used for acquiring user basic data of the user and dynamic operation behavior data associated with the express cabinet according to the unique identification.
Further, the recommendation information determining module 420 includes: the information acquisition unit is used for acquiring an alternative friend information set bound with the express cabinet; the similarity acquisition unit is used for inputting the candidate friend information set and the friend making data into a preset matching model to obtain the matching similarity of each candidate friend information output by the matching model and the friend making data; the friend information to be recommended acquisition unit is used for selecting candidate friend information corresponding to the matching similarity which is greater than or equal to the similarity threshold, or selecting a preset number of candidate friend information as friend information to be recommended; the matching similarity corresponding to the preset number of candidate friend information is larger than the matching similarity corresponding to other candidate friend information in the candidate friend information set.
Further, the recommendation execution module 430 further includes: and the off-line prompting unit is used for controlling the user and the client of the friend to be recommended to send out preset prompting sound if the moment that the user and the friend to be recommended appear in the preset range of the express cabinet is detected to be in the preset duration after the information of the friend to be recommended is sent to the express cabinet and/or the user terminal.
The friend-making recommendation device provided by the embodiment of the invention can execute the friend-making recommendation method applied to the server provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example five
Fig. 5 is a schematic structural diagram of a friend-making recommendation device in a fifth embodiment of the present invention. The method and the device are suitable for recommending matched friends to the user based on the express cabinet. As shown in fig. 5, the friend-making recommendation device is applied to an express cabinet, and includes:
the recommendation information receiving module 510 is configured to receive information of friends to be recommended sent by the server, where the information of friends to be recommended is matched with friend making data, the friend making data is extracted by the server, and the friend making data includes dynamic operation behavior data associated with the express cabinet;
and the friend recommending module 520 is used for prompting the information of friends to be recommended to the user.
According to the technical scheme, the express cabinet can receive information of friends to be recommended, which are sent by the server, the server automatically extracts dynamic operation behavior data with authenticity and instantaneity in a software and hardware combined mode in the process of using the express cabinet by the server, and accordingly matching is recommended, the user does not need to fill friend making information manually, and accordingly the express cabinet receives information of friends to be recommended, which is high in matching efficiency, accuracy and satisfaction; by prompting the information of the friends to be recommended to the user, the user automatically receives the information of the friends to be recommended on the using site of the express cabinet, friend making experience of the user is improved, and meanwhile friend making efficiency, accuracy and satisfaction are improved.
The friend-making recommendation device provided by the embodiment of the invention can execute the friend-making recommendation method applied to the express cabinet provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example six
Referring to fig. 6, fig. 6 is a schematic structural diagram of a server in a sixth embodiment of the present invention, and as shown in fig. 6, the server includes a processor 610, a memory 620, an input device 630, and an output device 640; the number of processors 610 in the server may be one or more, one processor 610 being taken as an example in fig. 6; the processor 610, memory 620, input device 630, and output device 640 in the server may be connected by a bus or other means, for example in fig. 6.
The memory 620 serves as a computer-readable storage medium, and may be used to store software programs, computer-executable programs, and modules, such as program instructions/modules (e.g., the friend-making data extraction module 410, the recommendation information determination module 420, and the recommendation execution module 430 in the friend-making recommendation device) corresponding to the friend-making recommendation method in the embodiment of the present invention. The processor 610 executes various functional applications of the server and data processing by running software programs, instructions and modules stored in the memory 620, i.e., implements the friend-making recommendation method described above.
Memory 620 may include primarily a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required for functionality; the storage data area may store data created according to the use of the terminal, etc. In addition, memory 620 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, memory 620 may further include memory remotely located with respect to processor 610, which may be connected to the server via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 630 may be used to receive input numeric or character information, such as a request for use of the till, and to generate key signal inputs related to user settings and function control of the server. The output device 640 may include a display server such as a display screen.
Example seven
The seventh embodiment of the invention provides an express cabinet, which comprises: a cabinet body; a memory mounted on the cabinet, a processor, and a computer program stored on the memory and executable on the processor; when the processor executes the program, the friend-making recommendation method applied to the express cabinet provided by any embodiment of the invention is realized.
In the embodiment of the invention, one cabinet body can comprise a plurality of grid openings with different specifications, so that different requirements of users on the size of the grid openings can be met, and more grid openings can be set in a limited cabinet body space to serve more users. Each express cabinet can comprise a cabinet body and a plurality of cabinet bodies, and the number of the cabinet bodies contained in the express cabinet can be changed according to business requirements.
The memory, as a computer-readable storage medium, may be used to store a software program, a computer-executable program, and modules, such as program instructions/modules (e.g., the recommendation information receiving module 510 and the friend recommendation module 520 in the friend-making recommendation device) corresponding to the friend-making recommendation method in the embodiment of the present invention. The processor executes various functional applications and data processing of the express cabinet by running software programs, instructions and modules stored in the memory, namely, the friend-making recommendation method is realized.
The memory may mainly include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, the memory may further include memory remotely located with respect to the processor, the remote memory being connectable to the courier cabinet through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Example eight
An eighth embodiment of the present invention provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement any one of the friend-making recommendation methods provided in the above embodiments, wherein the friend-making recommendation method includes:
responding to a use request of a user for an express cabinet, extracting friend making data of the user, wherein the friend making data comprises dynamic operation behavior data associated with the express cabinet;
Determining information of friends to be recommended, which are matched with the friend making data;
and sending the information of the friends to be recommended to the express cabinet and/or the user terminal.
Alternatively, another friend-making recommendation method includes:
receiving information of friends to be recommended, which are sent by a server, wherein the information of the friends to be recommended is matched with the friend making data, the friend making data are extracted by the server, and the friend making data comprise dynamic operation behavior data associated with the express cabinet;
and prompting the information of the friends to be recommended to the user.
Of course, the computer-readable storage medium provided by the embodiments of the present invention may have computer instructions capable of executing related operations in the friend-making recommendation method provided by any of the embodiments of the present invention, not limited to the method operations described above.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present invention.
It should be noted that, in the above embodiment of the friend-making recommendation device, each unit and module included are only divided according to the functional logic, but not limited to the above division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (9)
1. The friend making recommendation method is characterized by being applied to friend making by using an express cabinet and comprising the following steps:
responding to a use request of a user for the express cabinet, extracting friend making data of the user, wherein the friend making data comprises dynamic operation behavior data associated with the express cabinet, real basic data of the user and speculation data obtained by speculation according to the dynamic operation behavior data of the user; wherein the dynamic operational behavior data comprises: the user sends and fetches a piece on the express cabinet and uses the data of the temporary storage article of the express cabinet, and at least one of the data of the express player for picking up and rewarding the piece on the express cabinet and the data of the express purchased on other application programs and sent or temporarily stored by the express cabinet;
Adding a label to the friend-making data; wherein the label comprises a category, a state, a value range and a threshold range; the label types comprise fuzzy labels for marking data without verification of authenticity, non-fuzzy labels for marking the authenticity of the confirmed data and characteristic labels for classifying dynamic operation behavior data associated with the express cabinet;
according to the obtained real basic data of the user, third party verification data bound with the user is obtained from a third party platform, data with fuzzy labels in the friend making data are verified according to the third party verification data, and data with fuzzy labels inconsistent with the third party verification data are replaced with corresponding data in the third party verification data, so that effective friend making data are obtained;
determining information of friends to be recommended, which are matched with the effective friend making data;
the information of the friends to be recommended is sent to the express cabinet and/or the user terminal;
controlling the express cabinet and/or the user terminal to prompt the information of the friends to be recommended to the user in a voice broadcasting mode or a friend making link mode corresponding to the information of the friends to be recommended;
And if the moment that the user and the friend to be recommended appear in the preset range of the express cabinet is detected to be in the preset duration, controlling the user and the client of the friend to be recommended to send out preset prompt tones.
2. The method of claim 1, wherein the extracting the friend-making data of the user comprises:
responding to a use request of a user for the express cabinet, and acquiring a unique identifier of the user from the use request;
and acquiring user basic data of the user and dynamic operation behavior data associated with the express cabinet according to the unique identification.
3. The method of claim 1, wherein the determining information of friends to be recommended that match the effective dating data comprises:
acquiring an alternative friend information set bound with the express cabinet;
inputting the candidate friend information set and the effective friend making data into a preset matching model to obtain matching similarity of each candidate friend information output by the matching model and the effective friend making data;
selecting candidate friend information corresponding to the matching similarity greater than or equal to the similarity threshold, or selecting preset number of candidate friend information as friend information to be recommended;
The matching similarity corresponding to the preset number of candidate friend information is larger than the matching similarity corresponding to other candidate friend information in the candidate friend information set.
4. The friend making recommendation method is characterized by being applied to friend making by using an express cabinet and comprising the following steps:
receiving information of friends to be recommended sent by a server, wherein the information of the friends to be recommended is matched with effective friend making data;
the effective friend making data is obtained in the following way: the server adds a label to the friend-making data; wherein the label comprises a category, a state, a value range and a threshold range; the label types comprise fuzzy labels for marking data without verification of authenticity, non-fuzzy labels for marking the authenticity of the confirmed data and characteristic labels for classifying dynamic operation behavior data associated with the express cabinet; the server acquires third party verification data bound with the user from a third party platform according to the acquired user real basic data, verifies the data with the fuzzy label in the friend making data according to the third party verification data, and replaces the data with the fuzzy label inconsistent with the third party verification data with corresponding data in the third party verification data to acquire the effective friend making data;
The friend making data comprise dynamic operation behavior data associated with the express cabinet, real basic data of the user and speculative data obtained by speculation according to the dynamic operation behavior data of the user;
wherein the dynamic operational behavior data comprises: the user sends and fetches a piece on the express cabinet and uses the data of the temporary storage article of the express cabinet, and at least one of the data of the express player for picking up and rewarding the piece on the express cabinet and the data of the express purchased on other application programs and sent or temporarily stored by the express cabinet;
prompting the information of the friends to be recommended to the user by a voice broadcasting mode or a friend making link mode corresponding to the information of the friends to be recommended;
and if the moment that the user and the friend to be recommended appear in the preset range of the express cabinet is detected to be within the preset duration, the server controls the user and the client of the friend to be recommended to send out preset prompt tones.
5. A friend-making recommendation device is characterized by being applied to a server and an express cabinet
Making friends, including:
the friend making data extraction module is used for responding to a user's use request of the express cabinet and extracting friend making data of the user, wherein the friend making data comprises dynamic operation behavior data associated with the express cabinet, real basic data of the user and speculation data obtained by speculation the dynamic operation behavior data of the user; wherein the dynamic operational behavior data comprises: the user sends and fetches a piece on the express cabinet and uses the data of the temporary storage article of the express cabinet, and at least one of the data of the express player for picking up and rewarding the piece on the express cabinet and the data of the express purchased on other application programs and sent or temporarily stored by the express cabinet;
Adding a label to the friend-making data; wherein the label comprises a category, a state, a value range and a threshold range; the label types comprise fuzzy labels for marking data without verification of authenticity, non-fuzzy labels for marking the authenticity of the confirmed data and characteristic labels for classifying dynamic operation behavior data associated with the express cabinet;
according to the obtained real basic data of the user, third party verification data bound with the user is obtained from a third party platform, data with fuzzy labels in the friend making data are verified according to the third party verification data, and data with fuzzy labels inconsistent with the third party verification data are replaced with corresponding data in the third party verification data, so that effective friend making data are obtained;
the recommendation information determining module is used for determining information of friends to be recommended, which are matched with the effective friend making data;
the recommendation execution module is used for sending the information of the friends to be recommended to the express cabinet and/or the user terminal;
controlling the express cabinet and/or the user terminal to prompt the information of the friends to be recommended to the user in a voice broadcasting mode or a friend making link mode corresponding to the information of the friends to be recommended;
The recommendation execution module includes: and the off-line prompting unit is used for controlling the user and the client of the friend to be recommended to send out preset prompting sounds if the moment that the user and the friend to be recommended appear in the preset range of the express cabinet is detected to be within the preset duration after the information of the friend to be recommended is sent to the express cabinet and/or the user terminal.
6. The utility model provides a recommendation device makes friends, its characterized in that is applied to the express delivery cabinet, is applied to and uses the express delivery cabinet to make friends, includes:
the recommendation information receiving module is used for receiving information of friends to be recommended, which are sent by the server, wherein the information of the friends to be recommended is matched with the effective friend making data;
the effective friend making data is obtained in the following way: the server adds a label to the friend-making data; wherein the label comprises a category, a state, a value range and a threshold range; the label types comprise fuzzy labels for marking data without verification of authenticity, non-fuzzy labels for marking the authenticity of the confirmed data and characteristic labels for classifying dynamic operation behavior data associated with the express cabinet; the server acquires third party verification data bound with the user from a third party platform according to the acquired user real basic data, verifies the data with the fuzzy label in the friend making data according to the third party verification data, and replaces the data with the fuzzy label inconsistent with the third party verification data with corresponding data in the third party verification data to acquire the effective friend making data;
The friend making data comprise dynamic operation behavior data associated with the express cabinet, real basic data of the user and speculative data obtained by speculation according to the dynamic operation behavior data of the user;
wherein the dynamic operational behavior data comprises: the user sends and fetches a piece on the express cabinet and uses the data of the temporary storage article of the express cabinet, and at least one of the data of the express player for picking up and rewarding the piece on the express cabinet and the data of the express purchased on other application programs and sent or temporarily stored by the express cabinet;
the friend recommending module is used for prompting the information of the friends to be recommended to the user in a voice broadcasting mode or a friend making link mode corresponding to the information of the friends to be recommended;
and if the moment that the user and the friend to be recommended appear in the preset range of the express cabinet is detected to be within the preset duration, the server controls the user and the client of the friend to be recommended to send out preset prompt tones.
7. A server, the server comprising:
one or more processors;
storage means for storing one or more programs,
The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the friend-making recommendation method of any one of claims 1-3.
8. Express delivery cabinet, its characterized in that includes:
a cabinet body;
a memory mounted on the cabinet, a processor, and a computer program stored on the memory and executable on the processor;
the processor, when executing the program, implements the friend-making recommendation method according to claim 4.
9. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the friend-making recommendation method according to any one of claims 1 to 3, or the friend-making recommendation method according to claim 4.
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| CN110390856B (en) * | 2019-07-23 | 2022-02-18 | 深圳市丰巢科技有限公司 | Express cabinet operation guiding method and device, express cabinet and storage medium |
| CN111753211A (en) * | 2020-06-01 | 2020-10-09 | 深圳市智莱科技股份有限公司 | Friend-making recommendation method, express delivery cabinet and storage medium |
| CN113691442B (en) * | 2021-08-16 | 2023-02-28 | 北京百度网讯科技有限公司 | Friend recommendation method, device, equipment, storage medium and program product |
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| CN103684994A (en) * | 2013-12-19 | 2014-03-26 | 宇龙计算机通信科技(深圳)有限公司 | Method and system for recommending friends for instant messaging users |
| CN104408105A (en) * | 2014-11-20 | 2015-03-11 | 四川长虹电器股份有限公司 | Friend recommendation method applicable for intelligent TV (Television) users |
| CN104462308A (en) * | 2014-11-27 | 2015-03-25 | 广东小天才科技有限公司 | Social network friend recommending method and system |
| CN107123057A (en) * | 2017-03-22 | 2017-09-01 | 阿里巴巴集团控股有限公司 | User recommends method and device |
| CN107248116A (en) * | 2017-06-07 | 2017-10-13 | 维沃移动通信有限公司 | A kind of activity recommendation method and mobile terminal |
| CN107995099A (en) * | 2017-11-24 | 2018-05-04 | 广东欧珀移动通信有限公司 | Friend recommendation method and device |
| CN108647273B (en) * | 2018-04-28 | 2021-08-10 | 北京百度网讯科技有限公司 | Friend-making recommendation method and device |
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