CN104657477A - Social contact recommendation method and device - Google Patents
Social contact recommendation method and device Download PDFInfo
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- CN104657477A CN104657477A CN201510084990.6A CN201510084990A CN104657477A CN 104657477 A CN104657477 A CN 104657477A CN 201510084990 A CN201510084990 A CN 201510084990A CN 104657477 A CN104657477 A CN 104657477A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
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- G06F16/9535—Search customisation based on user profiles and personalisation
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Abstract
The invention provides a social contact recommendation method and device. The social contact recommendation method comprises the following steps of acquiring recommendation information which comprises a recommendation index, wherein the recommendation index is determined according to pre-collected data and predetermined algorithms; showing the recommendation information to users. By utilizing the method, the precision of social contact recommendation can be improved, and the time spent on making friends is reduced.
Description
Technical field
The present invention relates to Internet technical field, particularly relate to a kind of social recommendation method and apparatus.
Background technology
Mobile social activity becomes the indispensable society of people and affection need day by day, and with micro-letter, the social application programs of large quantities of movement (app) such as footpath between fields, footpath between fields are main client software, has captured the aspect such as acquaintance's social activity and stranger's social activity respectively well.
But there is certain blindness in current social recommendation mode, the time loss that adding users is made friends.
Summary of the invention
The present invention is intended to solve one of technical matters in correlation technique at least to a certain extent.
For this reason, one object of the present invention is to propose a kind of social recommendation method, and the method can improve the precision of social recommendation, reduces the time loss of making friends.
Another object of the present invention is to propose a kind of social recommendation device.
For achieving the above object, the social recommendation method that first aspect present invention embodiment proposes, comprising: obtain recommendation information, described recommendation information comprises recommendation index, and described recommendation index determines according to the data of collecting in advance and preset algorithm; Described recommendation information is shown to user.
The social recommendation method that first aspect present invention embodiment proposes, by obtaining and showing recommendation information, recommendation information comprises recommendation index, determine according to large data owing to recommending index, user, according to the precision that can improve social recommendation when recommending index to select, reduces the time loss of making friends.
For achieving the above object, the social recommendation device that second aspect present invention embodiment proposes, comprising: acquisition module, for obtaining recommendation information, described recommendation information comprises recommendation index, and described recommendation index determines according to the data of collecting in advance and preset algorithm; First display module, for showing described recommendation information to user.
The social recommendation device that second aspect present invention embodiment proposes, by obtaining and showing recommendation information, recommendation information comprises recommendation index, determine according to large data owing to recommending index, user, according to the precision that can improve social recommendation when recommending index to select, reduces the time loss of making friends.
The aspect that the present invention adds and advantage will part provide in the following description, and part will become obvious from the following description, or be recognized by practice of the present invention.
Accompanying drawing explanation
The present invention above-mentioned and/or additional aspect and advantage will become obvious and easy understand from the following description of the accompanying drawings of embodiments, wherein:
Fig. 1 is the schematic flow sheet of the social recommendation method that one embodiment of the invention proposes;
Fig. 2 is client and the mutual schematic diagram of service end in the embodiment of the present invention;
Fig. 3 is the schematic flow sheet of the social recommendation method that another embodiment of the present invention proposes;
Fig. 4 is the schematic diagram at the main interface of social application program in the embodiment of the present invention;
Fig. 5 is the displaying schematic diagram after clicking a kind of button on main interface in the embodiment of the present invention;
Fig. 6 is the displaying schematic diagram after clicking the another kind of button on main interface in the embodiment of the present invention;
Fig. 7 is the displaying schematic diagram after clicking the another kind of button on main interface in the embodiment of the present invention;
Fig. 8 is the displaying schematic diagram after clicking the another kind of button on main interface in the embodiment of the present invention;
Fig. 9 is the structural representation of the neural network adopted in the embodiment of the present invention;
Figure 10 is the structural representation of the social recommendation device that another embodiment of the present invention proposes;
Figure 11 is the structural representation of the social recommendation device that another embodiment of the present invention proposes.
Embodiment
Be described below in detail embodiments of the invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar module or has module that is identical or similar functions from start to finish.Being exemplary below by the embodiment be described with reference to the drawings, only for explaining the present invention, and can not limitation of the present invention being interpreted as.On the contrary, embodiments of the invention comprise fall into attached claims spirit and intension within the scope of all changes, amendment and equivalent.
Fig. 1 is the schematic flow sheet of the social recommendation method that one embodiment of the invention proposes, and the method comprises:
S11: obtain recommendation information, described recommendation information comprises recommendation index, described recommendation index determines according to the data of collecting in advance and preset algorithm.
Wherein, user is after opening social application program, and user can trigger social application program by the pre-set button clicked in social application program and obtain recommendation information from cloud server.
As shown in Figure 2, social application program can be arranged on Intelligent mobile equipment 21, and Intelligent mobile equipment is Intelligent bracelet such as, smart mobile phone, Intelligent flat computer, or, intelligent watch etc.
Social application program can collect the data of own user, and send the data to cloud server 22, recommendation information according to the data genaration recommendation information of different user, can be sent to the social application program on Intelligent mobile equipment 21 by cloud server 22 after cloud server 22 generating recommendations information.
Cloud server is when generating recommendations information, if recommendation information comprises recommendation index, cloud server can according to collect mass data (large data can be called), and preset algorithm determine recommend index, preset algorithm is such as neural network algorithm.Concrete, cloud server according to the data of the data of active user and user to be recommended or group, can adopt neural network algorithm, calculates similarity between the two, similarity be defined as recommending index.Concrete computation process can see subsequent embodiment.
S12: show described recommendation information to user.
Social application program obtains recommendation information from cloud server, can show recommendation information.
Wherein, the different buttons of corresponding social application program, can show different recommendation informations.
In the present embodiment, by obtaining and showing recommendation information, recommendation information comprises recommendation index, determine according to large data owing to recommending index, user is according to the precision that can improve social recommendation when recommending index to select, reduce the time loss of making friends, thus realize the conversion from " love is blindly " to " love has Data support ".
Fig. 3 is the schematic flow sheet of the social recommendation method that another embodiment of the present invention proposes, and the method comprises:
S31: the main interface showing social application program to user.
Such as, after user opens social application program, show main interface as shown in Figure 4.
See Fig. 4, the below at main interface comprises " near, find, message, recommend " these four buttons, above main interface, the left side is that " individual " information is arranged, and is " contact person " button on the right of top, display " contact person/group " list after click.
Different recommendation informations shown by the different buttons that user can click below main interface.
Wherein, the button shown for trigger recommendation index can comprise: for searching for the button of nearby users, and, for the button recommended, in the diagram, these two buttons respectively with " near " and " recommendation " represent, be understandable that, the title of above-mentioned button is a kind of example, also can adopt other titles.
Concrete, see Fig. 5, when user click " near " after button, can according to the list of distance display people, in list, the information of each correspondence user to be recommended, such as, can comprise photo, the pet name, signature, especially, also can comprise recommendation index in the present embodiment.
See Fig. 6, after user clicks " discovery " button, according to the message of people near distance display, can serve with city and promote, neighbouring activity etc.
See Fig. 7, after user clicks " message " button, its message received can be shown to user, message can from the user in this user contact person or group, and message can have picture, word, sound, one or more forms in video etc., also support URL(uniform resource locator) (Uniform Resource Locator, URL) form.
See Fig. 8, after user clicks " recommendation " button, user to be recommended or group can be shown to user.
With usually recommend in prior art other people's (also can be called user) unlike, not only can referrer in the present embodiment, can also group be recommended.
The social activity of people from line under line, always have certain sense of insecurity.And many times, the line of direct two people is linked up, effect might not be got well, and namely one can be difficult to " social " of understanding another one people all sidedly.The present embodiment is by recommending group, and the group that user can be allowed first to add people to be recommended enliven, can understand the other side better.Be even that two people recommend a new group simultaneously, like this after two people enter a group, by everybody information feed back and the crosscheck of information, the possibility that the mutual understanding that can improve two people from social angle is better understood mutually.
Click " recommendation " button for user, the social recommendation method of the present embodiment can also comprise:
S32: after user clicks the described button for recommending, show recommendation items to described user, described recommendation items comprises user and group.
Such as, after user clicks " recommendation " button, as shown in Figure 8, can show recommendation items to user, described recommendation items comprises user's (in Fig. 8, employment represents) and group.
S33: obtain recommendation information, and show described recommendation information to user.
Such as, after user clicks " recommendation " button, social application program can obtain corresponding recommendation information from cloud server, and shows recommendation information.
Optionally, describedly show described recommendation information to user, comprising:
Under default conditions, show the recommendation information that the group of the recommendation information that the user of recommendation is corresponding and recommendation is corresponding.
Such as, see Fig. 8, the recommendation information of displaying can comprise the recommendation information of user and group, such as, comprises " people/group's pet name ".Or,
When after the user that described user clicks in described recommendation items, only show the recommendation information that the user of recommendation is corresponding, or, when after the group that described user clicks in described recommendation items, a recommendation information that the group that displaying is recommended is corresponding.
Such as, when after " people " that user clicks on " recommendation " button, then the recommendation information of user is only shown, such as, each in recommendation information only includes " picture, people's pet name; recommend index ", and does not comprise other information corresponding to " group's pet name " and group.
The present embodiment, by showing different recommendation items, can allow user select according to actual needs to show people and/or group.
In addition, as shown in Figure 8, recommendation information not only comprises picture, the pet name, especially, can also comprise recommendation index.
As shown in Fig. 5 or Fig. 8, the present embodiment can show recommendation index to user, facilitates user according to recommendation Index selection, thus user can be made to choose self more interested user and/or group.
Recommend index can be specifically that cloud server is determined according to large data and neural network algorithm.
Optionally, described preset algorithm is neural network algorithm, and described recommendation index is the similarity between described user and user to be recommended or group, and described cloud server determines described recommendation index in the following way:
From the data of collecting in advance, obtain the first data and the second data, described first data are data of described user, and described second data are data of described user to be recommended or group;
Being primary vector by described first data-mapping, is secondary vector by described second data-mapping;
Calculate the similarity of described primary vector and described secondary vector, described similarity is defined as described recommendation index.
The structure of neural network algorithm can as shown in Figure 9, and x represents the data treating recommended active user, and y represents a user to be recommended or the data of a group.See Fig. 9, data can comprise w1 ..., wn parameter, parameter is such as range information, hobby, sex, and user makes a speech keyword etc.E1 ..., em is hidden layer space, represents the concrete explainable various parameters having concrete meaning user, is mapped to a low level spatially dense.Suppose that two vectors after being mapped to hidden layer space represent with v1 and v2 respectively, then can calculate the cos function between v1 and v2, the cosine value calculated is defined as similarity.
Concrete, by w1 ..., wn is mapped as e1 ..., em can be expressed as: A:x → v1, B:y → v2, and wherein, A and B determines in the training process.
Training process can comprise:
Select candidate's training sample, wherein, training data can be met pre-conditioned training sample and be defined as candidate's training sample, pre-conditionedly such as to comprise: the interior daily routines scope of Preset Time window (such as month) is less than preset range, specify the opposite sex, and, the number with same interest hobby is greater than 1, wherein, time is time when collecting data, scope of activities can be carried out detection to the positional information of user and be obtained, gender information when specifying the opposite sex and hobby can register according to user and hobby acquisition of information;
Positive sample and negative sample is determined in candidate's training sample; Wherein, the determination principle of positive sample and negative sample also can pre-set, and such as, recommend user 3 candidate/groups, and user only clicks wherein 1.Then clicked one is " positive sample ", and not having clicked is " negative sample ".Or user clicks, but final only and one of them maintain further contact, be a positive sample too, two negative samples.
Train according to positive sample and negative sample, obtain A and B.
Such as, Target Acquisition A and B can be minimised as with following parameter:
L(A,B；x,y,y')＝max(0,Φ(v1,v2')-Φ(v1,v2)+δ)
Wherein, <x, y> are positive samples, and <x, y'> are negative samples, Φ (v1, v2)=cos (v1, v2);
Such as stochastic gradient descent algorithm can be adopted when minimizing.
Be understandable that, the training process of the positive sample of above-mentioned basis and negative sample is just a kind of simplifies example, the concrete training algorithm that can adopt conventional neural network, such as, and propagated forward algorithm or Back Propagation Algorithm etc.
The present embodiment is by calculating similarity, and similarity height then recommends index high, then can recommend and other users or the group self with higher similarity for user.
Optionally, can also comprise in recommendation information: rationale for the recommendation.Rationale for the recommendation is the parameter with higher similarity, and such as, course of growth, the daily schedule, hobby, hobby is such as moved, body-building, film, reads.
Rationale for the recommendation oppositely can be derived according to two vector similarity result of calculations, obtains the weight of each parameter, using parameter large for weight as rationale for the recommendation.Such as, when two vectorial courses of growth are identical corresponding similarity be greater than two vectorial daily schedules identical time corresponding similarity, then the weight of course of growth is greater than the weight of daily schedule.
In the present embodiment, by obtaining and showing recommendation information, recommendation information comprises recommendation index, determine according to large data owing to recommending index, user is according to the precision that can improve social recommendation when recommending index to select, reduce the time loss of making friends, thus realize the conversion from " love is blindly " to " love has Data support "; The present embodiment, by recommending group, can facilitate understanding the other side that user changes, and improves security; The present embodiment, by showing people and group when recommending, can select referrer and/or group according to user, for user provides multiple choices; The present embodiment, by showing rationale for the recommendation, more convenient user can be selected, is promoted Consumer's Experience.
Figure 10 is the structural representation of the social recommendation device that another embodiment of the present invention proposes, and this device specifically can be positioned on Intelligent mobile equipment, and this device 100 comprises acquisition module 101 and the first display module 102.
Acquisition module 101, for obtaining recommendation information, described recommendation information comprises recommendation index, and described recommendation index determines according to the data of collecting in advance and preset algorithm;
Wherein, user is after opening social application program, and user can trigger social application program by the pre-set button clicked in social application program and obtain recommendation information from cloud server.
Social application program can be arranged on Intelligent mobile equipment, and Intelligent mobile equipment is Intelligent bracelet such as, smart mobile phone, Intelligent flat computer, or, intelligent watch etc.
As shown in Figure 2, social application program can collect the data of own user, and send the data to cloud server, recommendation information according to the data genaration recommendation information of different user, can be sent to the social application program on Intelligent mobile equipment by cloud server after cloud server generating recommendations information.
Cloud server is when generating recommendations information, if recommendation information comprises recommendation index, cloud server can according to collect mass data (large data can be called), and preset algorithm determine recommend index, preset algorithm is such as neural network algorithm.Concrete, cloud server according to the data of the data of active user and user to be recommended or group, can adopt neural network algorithm, calculates similarity between the two, similarity be defined as recommending index.
First display module 102, for showing described recommendation information to user.
Social application program obtains recommendation information from cloud server, can show recommendation information.
Wherein, the different buttons of corresponding social application program, can show different recommendation informations.
See Figure 11, this device 100 also comprises:
Second display module 103, for showing the main interface of social application program to described user;
Such as, after user opens social application program, show main interface as shown in Figure 4.
See Fig. 4, the below at main interface comprises " near, find, message, recommend " these four buttons, above main interface, the left side is that " individual " information is arranged, and is " contact person " button on the right of top, display " contact person/group " list after click.
Described acquisition module 101 specifically for: after the pre-set button that described user clicks in described main interface, from cloud server obtain recommendation information.
Wherein, the button shown for trigger recommendation index can comprise: for searching for the button of nearby users, and, for the button recommended, in the diagram, these two buttons respectively with " near " and " recommendation " represent, be understandable that, the title of above-mentioned button is a kind of example, also can adopt other titles.
Click different buttons and can obtain different recommendation informations, specifically see the associated description in above-described embodiment, can not repeat them here.
Described preset algorithm is neural network algorithm, and described recommendation index is the similarity between described user and user to be recommended or group, and described cloud server determines described recommendation index in the following way:
From the data of collecting in advance, obtain the first data and the second data, described first data are data of described user, and described second data are data of described user to be recommended or group;
Being primary vector by described first data-mapping, is secondary vector by described second data-mapping;
Calculate the similarity of described primary vector and described secondary vector, described similarity is defined as described recommendation index.
The mode of concrete calculated recommendation index see the associated description in above-described embodiment, can not repeat them here.
See Figure 11, described pre-set button comprises the button for recommending, and described device 100 also comprises:
3rd display module 104, for click the described button for recommending as user after, show recommendation items to described user, described recommendation items comprises user and group.
Such as, after user clicks " recommendation " button, as shown in Figure 8, can show recommendation items to user, described recommendation items comprises user's (in Fig. 8, employment represents) and group.
Described first display module 102 specifically for:
Under default conditions, show the recommendation information that the group of the recommendation information that the user of recommendation is corresponding and recommendation is corresponding;
Such as, see Fig. 8, the recommendation information of displaying can comprise the recommendation information of user and group, such as, comprises " people/group's pet name ".Or,
When after the user that described user clicks in described recommendation items, only show the recommendation information that the user of recommendation is corresponding, or, when after the group that described user clicks in described recommendation items, a recommendation information that the group that displaying is recommended is corresponding.
Such as, when after " people " that user clicks on " recommendation " button, then the recommendation information of user is only shown, such as, each in recommendation information only includes " picture, people's pet name; recommend index ", and does not comprise other information corresponding to " group's pet name " and group.
Optionally, can also comprise in recommendation information: rationale for the recommendation.Rationale for the recommendation is the parameter with higher similarity, and such as, course of growth, the daily schedule, hobby, hobby is such as moved, body-building, film, reads.
Rationale for the recommendation oppositely can be derived according to two vector similarity result of calculations, obtains the weight of each parameter, using parameter large for weight as rationale for the recommendation.Such as, when two vectorial courses of growth are identical corresponding similarity be greater than two vectorial daily schedules identical time corresponding similarity, then the weight of course of growth is greater than the weight of daily schedule.
In the present embodiment, by obtaining and showing recommendation information, recommendation information comprises recommendation index, determine according to large data owing to recommending index, user is according to the precision that can improve social recommendation when recommending index to select, reduce the time loss of making friends, thus realize the conversion from " love is blindly " to " love has Data support ".
It should be noted that, in describing the invention, term " first ", " second " etc. only for describing object, and can not be interpreted as instruction or hint relative importance.In addition, in describing the invention, except as otherwise noted, the implication of " multiple " is two or more.
Describe and can be understood in process flow diagram or in this any process otherwise described or method, represent and comprise one or more for realizing the module of the code of the executable instruction of the step of specific logical function or process, fragment or part, and the scope of the preferred embodiment of the present invention comprises other realization, wherein can not according to order that is shown or that discuss, comprise according to involved function by the mode while of basic or by contrary order, carry out n-back test, this should understand by embodiments of the invention person of ordinary skill in the field.
Should be appreciated that each several part of the present invention can realize with hardware, software, firmware or their combination.In the above-described embodiment, multiple step or method can with to store in memory and the software performed by suitable instruction execution system or firmware realize.Such as, if realized with hardware, the same in another embodiment, can realize by any one in following technology well known in the art or their combination: the discrete logic with the logic gates for realizing logic function to data-signal, there is the special IC of suitable combinational logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA) etc.
Those skilled in the art are appreciated that realizing all or part of step that above-described embodiment method carries is that the hardware that can carry out instruction relevant by program completes, described program can be stored in a kind of computer-readable recording medium, this program perform time, step comprising embodiment of the method one or a combination set of.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing module, also can be that the independent physics of unit exists, also can be integrated in a module by two or more unit.Above-mentioned integrated module both can adopt the form of hardware to realize, and the form of software function module also can be adopted to realize.If described integrated module using the form of software function module realize and as independently production marketing or use time, also can be stored in a computer read/write memory medium.
The above-mentioned storage medium mentioned can be ROM (read-only memory), disk or CD etc.
In the description of this instructions, specific features, structure, material or feature that the description of reference term " embodiment ", " some embodiments ", " example ", " concrete example " or " some examples " etc. means to describe in conjunction with this embodiment or example are contained at least one embodiment of the present invention or example.In this manual, identical embodiment or example are not necessarily referred to the schematic representation of above-mentioned term.And the specific features of description, structure, material or feature can combine in an appropriate manner in any one or more embodiment or example.
Although illustrate and describe embodiments of the invention above, be understandable that, above-described embodiment is exemplary, can not be interpreted as limitation of the present invention, and those of ordinary skill in the art can change above-described embodiment within the scope of the invention, revises, replace and modification.
Claims (12)
1. a social recommendation method, is characterized in that, comprising:
Obtain recommendation information, described recommendation information comprises recommendation index, and described recommendation index determines according to the data of collecting in advance and preset algorithm;
Described recommendation information is shown to user.
2. method according to claim 1, is characterized in that, also comprises:
The main interface of social application program is shown to described user;
Described acquisition recommendation information, comprising:
After the pre-set button that described user clicks in described main interface, obtain recommendation information from cloud server.
3. method according to claim 2, it is characterized in that, described preset algorithm is neural network algorithm, and described recommendation index is the similarity between described user and user to be recommended or group, and described cloud server determines described recommendation index in the following way:
From the data of collecting in advance, obtain the first data and the second data, described first data are data of described user, and described second data are data of described user to be recommended or group;
Being primary vector by described first data-mapping, is secondary vector by described second data-mapping;
Calculate the similarity of described primary vector and described secondary vector, described similarity is defined as described recommendation index.
4. method according to claim 2, is characterized in that, described pre-set button comprises the button for recommending, and described method also comprises:
After user clicks the described button for recommending, show recommendation items to described user, described recommendation items comprises user and group.
5. method according to claim 4, is characterized in that, describedly shows described recommendation information to user, comprising:
Under default conditions, show the recommendation information that the group of the recommendation information that the user of recommendation is corresponding and recommendation is corresponding; Or,
When after the user that described user clicks in described recommendation items, only show the recommendation information that the user of recommendation is corresponding, or, when after the group that described user clicks in described recommendation items, a recommendation information that the group that displaying is recommended is corresponding.
6. method according to claim 2, is characterized in that, described pre-set button comprises the button for searching for nearby users.
7. the method according to any one of claim 1-6, is characterized in that, described recommendation information also comprises: rationale for the recommendation.
8. a social recommendation device, is characterized in that, comprising:
Acquisition module, for obtaining recommendation information, described recommendation information comprises recommendation index, and described recommendation index determines according to the data of collecting in advance and preset algorithm;
First display module, for showing described recommendation information to user.
9. device according to claim 8, is characterized in that, also comprises:
Second display module, for showing the main interface of social application program to described user;
Described acquisition module specifically for: after the pre-set button that described user clicks in described main interface, from cloud server obtain recommendation information.
10. device according to claim 9, it is characterized in that, described preset algorithm is neural network algorithm, and described recommendation index is the similarity between described user and user to be recommended or group, and described cloud server determines described recommendation index in the following way:
From the data of collecting in advance, obtain the first data and the second data, described first data are data of described user, and described second data are data of described user to be recommended or group;
Being primary vector by described first data-mapping, is secondary vector by described second data-mapping;
Calculate the similarity of described primary vector and described secondary vector, described similarity is defined as described recommendation index.
11. devices according to claim 9, it is characterized in that, described pre-set button comprises the button for recommending, described device also comprises:
3rd display module, for click the described button for recommending as user after, show recommendation items to described user, described recommendation items comprises user and group.
12. devices according to claim 11, is characterized in that, described first display module specifically for:
Under default conditions, show the recommendation information that the group of the recommendation information that the user of recommendation is corresponding and recommendation is corresponding; Or,
When after the user that described user clicks in described recommendation items, only show the recommendation information that the user of recommendation is corresponding, or, when after the group that described user clicks in described recommendation items, a recommendation information that the group that displaying is recommended is corresponding.
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