CN103020276A - Method and device for searching social contact objects - Google Patents

Abstract

The invention discloses a method and device for searching social contact objects. The method comprises the following steps: searching for the social contact objects according to a searching condition, thereby obtaining all the social contact objects matching with the searching condition; taking each of the social contact objects as a present social contact object in turn, thereby obtaining social contact objects concerned by the present social contact object and concerning the present social contact object; confirming a weight index value of each social contact object according to the social contact objects respectively concerned by each social contact object and the social contact objects respectively concerning each social contact object; and returning each social contact object according to the sequence of the weight index values from high to low. According to the scheme provided by the embodiment of the invention, when the social contact objects are searched, the consumption of the processing resource on the side of a social contact network is reduced and the consumption of the network bandwidth resource is reduced.

Description

A kind of social object search method and device

Technical field

The present invention relates to Internet technical field and field of computer technology, relate in particular to a kind of social object search method and device.

Background technology

In existing internet, applications, social networks is widely used and fast development, such as microblogging etc.In social networks, social object (being the user) can be issued the information of various media formats, such as literal, picture, video etc., also can browse the information of other social object publishings.For the ease of the interchange between each social object, can set up the relation of paying close attention to and being concerned between the social object, a social object can be paid close attention to other a plurality of social objects, also can be paid close attention to by other a plurality of social objects.

In the actual use of social networks, also often need to search for social object, existing way of search is that the object tag of search condition and social object is mated, and the social object of Satisfying Matching Conditions returned to the user as Search Results, wherein, the object tag of social object can be the interest label, the interest that represents social object, it also can be the topic label, represent the topic that social object is participated in discussion, object tag can by user oneself mark, also can be marked by in social networks the behavioural characteristic of visit information of network side according to social object.

Yet in existing social object search method, after obtaining Search Results, each the social object that directly Search Results is comprised returns according to the order of random alignment and represents to the user.Since not according to one reasonably order do not arrange, for the Search Results that obtains, the user may need operation is checked in the click that each the social object that comprises in the Search Results carries out repeatedly, the social object that can find self expectation to search.And operation is checked in user's repeatedly click, all needs network side to process accordingly operation, and returns the information that request is checked to the user, thereby has increased the consumption of social networks side processing resource, and the consumption of network bandwidth resources.

Summary of the invention

The embodiment of the invention provides a kind of social object search method and device, and is more in order to solve the social networks side that the causes processing resource consumption that exists in the prior art to social object search the time, and network bandwidth resources consumes more problem.

The embodiment of the invention provides a kind of social object search method, comprising:

Social object is searched for the social object of each that obtains being complementary with search condition according to search condition;

Successively with each social object in described each social object as current social object, obtain the social object that this current social object is paid close attention to, and the social object of paying close attention to this current social object;

The social object of paying close attention to respectively according to described each social object, and the social object of paying close attention to respectively described each social object are determined the weight index value of described each social object;

According to weight index value order from high to low, return described each social object.

The embodiment of the invention also provides a kind of social object search device, comprising:

Search unit is used for social object being searched for the social object of each that obtains being complementary with search condition according to search condition;

Acquiring unit is used for successively with described each social object of each social object obtaining the social object of this current social object concern as current social object, and the social object of paying close attention to this current social object;

The weight determining unit is used for the social object paid close attention to respectively according to described each social object, and the social object of paying close attention to respectively described each social object, determines the weight index value of described each social object;

Sequencing unit is used for according to weight index value order from high to low, returns described each social object.

Beneficial effect of the present invention comprises:

In the method that the embodiment of the invention provides, according to search condition social object is being searched for, and after each the social object that obtains being complementary with search condition, successively with each social object in each social object as current social object, obtain the social object that this current social object is paid close attention to, and the social object of paying close attention to this current social object, then the social object of paying close attention to respectively according to each social object, and the social object of paying close attention to respectively each social object, determine the weight index value of each social object, according to weight index value order from high to low, return each social object.Because the weight index value has characterized the significance level of social object, so return each social object according to weight index value order from high to low, so that can checking by click still less, the user namely finds self social object of searching of expectation, thereby reduced the user operation has been checked in the click of each social object of comprising in the Search Results, and then reduced the consumption that the social networks side is processed resource, and the consumption that reduced network bandwidth resources.

The application's further feature and advantage will be set forth in the following description, and, partly from instructions, become apparent, perhaps understand by implementing the application.The application's purpose and other advantages can realize and obtain by specifically noted structure in the instructions of writing, claims and accompanying drawing.

Description of drawings

Accompanying drawing is used to provide a further understanding of the present invention, and consists of the part of instructions, is used from explanation the present invention with the embodiment of the invention one, is not construed as limiting the invention.In the accompanying drawings:

The process flow diagram of the social object search method that Fig. 1 provides for the embodiment of the invention;

The process flow diagram of the social object search method that Fig. 2 provides for the embodiment of the invention 1;

The process flow diagram of the social object search method that Fig. 3 provides for the embodiment of the invention 2;

Fig. 4 is the structural representation that is used for carrying out the disposal system of social object search method in the embodiment of the invention;

The structural representation of the social object search device that provides in the embodiment of the invention 3 is provided Fig. 5.

Embodiment

Reduce the consumption that the social networks side is processed resource in order to be given in social object search, and the implementation that reduces the consumption of network bandwidth resources, the embodiment of the invention provides a kind of social object search method and device, below in conjunction with Figure of description the preferred embodiments of the present invention are described, be to be understood that, preferred embodiment described herein only is used for description and interpretation the present invention, is not intended to limit the present invention.And in the situation of not conflicting, embodiment and the feature among the embodiment among the application can make up mutually.

The embodiment of the invention provides a kind of social object search method, as shown in Figure 1, comprising:

Step 101, social object is searched for the social object of each that obtains being complementary with search condition according to search condition.

Step 102, successively with each social object in each social object as current social object, obtain the social object that this current social object is paid close attention to, and the social object of paying close attention to this current social object.

Step 103, the social object of paying close attention to respectively according to each social object, and the social object of paying close attention to respectively each social object are determined the weight index value of each social object.

Step 104, according to weight index value order from high to low, return each social object.

Wherein, for the concrete mode of determining the weight index value of each social object in the above-mentioned steps 103, can be as follows:

Before the weight index value of determining each social object, first successively with each social object in each social object as current social object, determine the quantity of the social object that this current social object is paid close attention to, and the quantity of paying close attention to the social object of this current social object;

Then, the quantity of the social object of paying close attention to respectively according to each social object, and the quantity of paying close attention to respectively the social object of each social object are determined the weight index value of each social object.

Understand with being convenient to for convenience of description, the quantity of the social object that a social object can be paid close attention to, be called the quantity that this social activity object is paid close attention to, and can the quantity of the social object of a social object will be paid close attention to, be called the quantity that this social activity object is concerned, the quantity of namely paying close attention to respectively according to each social object, and the quantity that is concerned respectively of each social object are determined the weight index value of each social object.

For example, it is larger to be specifically as follows the quantity that social object is concerned, and the weight index value of this social activity object is larger, the quantity that social object is paid close attention to is larger, the weight index value of this social activity object is larger, is weighted summation such as the quantity based on the quantity that is concerned and concern, obtains the weight index value;

Also can be the quantity that is concerned according to social object first, determine the weight index value, and the quantity that social object is concerned is larger, the weight index value of this social activity object is larger, in the identical situation of the quantity that is concerned, the quantity of paying close attention to according to social object is again adjusted the weight index value, and the quantity that social object is paid close attention to is larger, and the weight index value of this social activity object is larger.

Above-mentioned steps 103 can also adopt alternate manner in the embodiment of the invention except adopting above-mentioned concrete mode, below in conjunction with accompanying drawing, with specific embodiment method provided by the invention and device are described in detail.

Embodiment 1:

The process flow diagram of the social object search method that Fig. 2 provides for the embodiment of the invention 1 specifically comprises following treatment step:

Step 201, user carry search condition by the social object search request of client to the webserver transmission of social networks in the social object search request.

The webserver of step 202, social networks is searched for social object according to the search condition that carries after receiving this social activity object search request, the social object of each that obtains being complementary with search condition.

Wherein, the concrete mode according to search condition is searched for social object can adopt variety of way of the prior art.

In this step, specifically can determine the object tag that is complementary with search condition, and mark is had each social object of this object tag as Search Results;

Can also further mark there be social object that the social object of each of this object tag pays close attention to also as Search Results;

Further, when the quantity of each included social object of Search Results is larger, in order to reduce follow-up calculated amount when each social object is sorted, can also get rid of a part of social object from Search Results, the social object that is excluded can not reach for the quantity of the social object of paying close attention to this social activity object the social object of predetermined number threshold value.

Step 203, successively with each social object in each the social object in the Search Results as current social object, obtain the social object that this current social object is paid close attention to, and the social object of paying close attention to this current social object, namely obtain the social object that each social object is paid close attention in each social object, and the social object of paying close attention to each social object.

For the ease of obtaining, can be in advance for each social object in the social networks, adopt as the mode of following table 1, to the social object of each social object concern, and the social object of paying close attention to each social object is stored:

Table 1

Social object	The quantity of paying close attention to	The social object of paying close attention to	The social object of paying close attention to	...	The social object of paying close attention to
						U 1	S 1	U(1,1)	U(1,2)	...	U(1,S 1)
U 2	S 2	U(2,1)	U(2,2)	...	U(2,S 2)
						...	...	...	...	...	...
U N-1	S N-1	U(N-1,1)	U(N-1,2)	...	U(N-1,S N-1)
						U N	S N	U(N,1)	U(N,2)	...	U(N,S N)

In the above-mentioned table 1, N is the quantity of each the social object in the Search Results, U _iBe i social object, S _iBe the quantity of the social object of i social object concern, U (i, j) is i the S that social object is paid close attention to _iJ in the individual social object social object.

When storage list 1, U ₁-U _NCan be respectively the integer of 0-N, and distinguish the ID of corresponding N social object, thereby can in O (1) complexity, find the concern information of a social object; And, table 1 can be loaded in the larger server of internal memory, thereby accelerate the efficient of obtaining information from table 1.

Step 204, determine successively the weight index value of each social object in the Search Results according to following formula:

$\mathrm{PR}\left(i\right)=\frac{1-d}{N}+d\underset{j\∈\mathrm{in}\left(i\right)}{\mathrm{\Σ}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

Wherein, PR (i) is the weight index value of i social object in each definite social object of this iteration, N is the quantity of each social object, in (i) is the set of the social object of i social object of concern, out (j) is the set of the social object of j social object concern, | out (j) | be the quantity of the social object of j social object concern, pr (j) is the weight index value of the definite j of a last iteration social object, and pr (j) was the default initial weight desired value of j social object when iteration was determined for the first time, the default initial weight desired value of described each social object equates, for example, can be 1, d is default concern transition probability, has characterized after paying close attention to a social object, from the social object that this social activity object that is concerned is paid close attention to, continue the probability that the social object of selection is paid close attention to, concrete numerical value can arrange according to actual needs flexibly, for example, can be set to 0.85.

Step 205, judge whether to satisfy the termination condition that iteration is determined, if satisfy, enter step 206, if do not satisfy, enter step 204, carry out next iteration and determine.

Concrete, the termination condition that iteration is determined can reach the fixed value that sets in advance for the number of times M that current iteration is determined;

The termination condition that iteration is determined also can satisfy following relational expression for the number of times M that current iteration is determined:

$\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}|\mathrm{PR}{\left(i\right)}_{M-1}-\mathrm{PR}{\left(i\right)}_M|<{\mathrm{PR}}_C;$ Perhaps, $\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}|\mathrm{PR}{\left(i\right)}_{M-1}-\mathrm{PR}{\left(i\right)}_M|\&le;{\mathrm{PR}}_C;$

Wherein, PR (i) _M-1Be the weight index value of i social object determining of the M-1 time iteration, PR (i) _MBe the weight index value of i social object determining of the M time iteration, PR _CBe default weight difference threshold.

Step 206, the weight index value order from high to low of determining according to last iteration sort to each social object that Search Results comprises, and return each social object according to the order of arranging to client, in order on client, represent each social object according to weight index value order from high to low.

The said method that adopts the embodiment of the invention 1 to provide, because determined weight index value has characterized the significance level of social object, so return each social object according to weight index value order from high to low, so that can checking by click still less, the user namely finds self social object of searching of expectation, thereby reduced the user operation has been checked in the click of each social object of comprising in the Search Results, and then reduced the consumption that the social networks side is processed resource, and the consumption that reduced network bandwidth resources.

Embodiment 2:

In the practical application of social networks, when social object is searched for, also often need to search for social object for a certain object tag, for example, for a certain topic label social object is searched for, may not only comprise that mark has the social object of this object tag this moment in the Search Results, also may include the social object that does not mark this object tag, in the embodiment of the invention 2, namely when for a certain object tag social object being searched for, the method that how to sort according to significance level for each the social object in the Search Results is described in detail, and as shown in Figure 3, specifically comprises following treatment step:

Step 301, user carry search condition by the social object search request of client to the webserver transmission of social networks in the social object search request.

The webserver of step 302, social networks is searched for social object according to the search condition that carries after receiving this social activity object search request, the social object of each that obtains being complementary with search condition.

Wherein, the concrete mode according to search condition is searched for social object can adopt variety of way of the prior art.

In this step, specifically can determine the object tag that is complementary with search condition, and mark is had each social object of this object tag as Search Results;

Can also further mark there be social object that the social object of each of this object tag pays close attention to also as Search Results;

Further, when the quantity of each included social object of Search Results is larger, in order to reduce follow-up calculated amount when each social object is sorted, can also get rid of a part of social object from Search Results, the social object that is excluded can not reach for the quantity of the social object of paying close attention to this social activity object the social object of predetermined number threshold value.

Step 303, determine and object tag that search condition is complementary that this object tag can be the interest label, also can be the topic label.

When the object tag having determined in the above-mentioned steps 302 to be complementary with search condition, omit this step.

Step 304, determine whether each social object that Search Results comprises marks this object tag that is complementary with search condition.

Step 305, successively with each social object in each the social object in the Search Results as current social object, obtain the social object that this current social object is paid close attention to, and the social object of paying close attention to this current social object, namely obtain the social object that each social object is paid close attention in each social object, and the social object of paying close attention to each social object.

For the ease of obtaining, can be in advance for each social object in the social networks, adopt as the mode of following table 2, to the social object of each social object concern, and the social object of paying close attention to each social object is stored:

Table 2

Social object	The quantity of paying close attention to	The social object of paying close attention to	The social object of paying close attention to	...	The social object of paying close attention to
						U 1	S 1	U(1,1)	U(1,2)	...	U(1,S 1)
U 2	S 2	U(2,1)	U(2,2)	...	U(2,S 2)
						...	...	...	...	...	...
U N-1	S N-1	U(N-1,1)	U(N-1,2)	...	U(N-1,S N-1)
						U N	S N	U(N,1)	U(N,2)	...	U(N,S N)

In the above-mentioned table 2, N is the quantity of each the social object in the Search Results, U _iBe i social object, S _iBe the quantity of the social object of i social object concern, U (i, j) is i the S that social object is paid close attention to _iJ in the individual social object social object.

When storage list 2, U ₁-U _NCan be respectively the integer of 0-N, and distinguish the ID of corresponding N social object, thereby can in O (1) complexity, find the concern information of a social object; And, table 2 can be loaded in the larger server of internal memory, thereby accelerate the efficient of obtaining information from table 2.

There is not strict sequencing between this step 305 and above-mentioned steps 303 and the step 304.

Step 306, this step can be carried out according to following dual mode:

First kind of way: determine successively weight index value for each social object in the Search Results of this object tag according to following formula:

$\mathrm{PR}\left(i\right)=(1-d)r_i+d\underset{j\&Element;\mathrm{in}\left(i\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{PR}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

Wherein, PR (i) is the weight index value of i social object in each definite social object of this iteration, r when i social object marking has this object tag _iBe 1, r when i social object do not marked this object tag _iBe 0, in (i) is the set of the social object of i social object of concern, out (j) is the set of the social object of j social object concern, | out (j) | be the quantity of the social object of j social object concern, pr (j) is the weight index value of the definite j of a last iteration social object, and pr (j) was the default initial weight desired value of j social object when iteration was determined for the first time, the initial weight desired value that described each social object acceptance of the bid is marked with the social object of described object tag is the first default initial weight desired value, the initial weight desired value that does not mark the social object that described object tag is arranged in described each social object is the second default initial weight desired value, and described the first initial weight desired value is greater than described the second initial weight desired value, for example, the first initial weight desired value is 1, the second initial weight desired value is 0, d is default concern transition probability, characterized after paying close attention to a social object, from the social object that this social activity object that is concerned is paid close attention to, continue the probability that the social object of selection is paid close attention to, concrete numerical value can arrange according to actual needs flexibly, for example, can be set to 0.85.

The second way: determine successively weight index value for each social object in the Search Results of this object tag according to following formula:

$\mathrm{PR}\left(i\right)=(1-d)\mathrm{pr}\left(i\right)+d\underset{j\&Element;\mathrm{in}\left(i\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

Wherein, PR (i) is the weight index value of i social object in definite described each the social object of this iteration, in (i) is the set of the social object of i social object of concern, out (j) is the set of the social object of j social object concern, | out (j) | be the quantity of the social object of j social object concern, pr (j) is the weight index value of the definite j of a last iteration social object, and pr (j) was the default initial weight desired value of j social object when iteration was determined for the first time, the initial weight desired value that described each social object acceptance of the bid is marked with the social object of described object tag is the first default initial weight desired value, the initial weight desired value that does not mark the social object that described object tag is arranged in described each social object is the second default initial weight desired value, and described the first initial weight desired value is greater than described the second initial weight desired value, for example, the first initial weight desired value is 1, the second initial weight desired value is 0, d is default concern transition probability, characterized after paying close attention to a social object, from the social object that this social activity object that is concerned is paid close attention to, continue the probability that the social object of selection is paid close attention to, concrete numerical value can arrange according to actual needs flexibly, for example, can be set to 0.85.

The below adopt the above-mentioned second way with instantiation to the description of giving an example of this step:

Suppose that Search Results comprises 5 social objects, be respectively U1, U2, U3, U4 and U5, and U1, U3 and U5 mark have the object tag T that is complementary with search condition, U2 and U4 have not marked this object tag T, the first initial weight desired value is 1, the second initial weight desired value is 0, and the situation of the social object paid close attention to separately of U1, U2, U3, U4 and U5 is as shown in table 3:

Table 3

Social object	The quantity of paying close attention to	The social object of paying close attention to	The social object of paying close attention to	The social object of paying close attention to
					U1	3	U2	U3	U4
U2	2	U3	U4	?
					U3	2	U1	U2	?
U4	1	U3	?	?
					U5	2	U2	U3	?

With the first time iteration be defined as example, as follows for definite process of the weight index value of each social object of object tag T:

$\mathrm{PR}\left(1\right)=(1-d)\mathrm{pr}\left(1\right)+d\underset{j\&Element;\mathrm{in}\left(1\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|}=(1-0.85)*\mathrm{pr}\left(1\right)+0.85*(\mathrm{pr}\left(3\right)/2)=0.575;$

$\mathrm{PR}\left(2\right)=(1-d)\mathrm{pr}\left(2\right)+d\underset{j\&Element;\mathrm{in}\left(1\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

$=(1-0.85)*\mathrm{pr}\left(2\right)+0.85*(\mathrm{pr}\left(1\right)/3+\mathrm{pr}\left(3\right)/2+\mathrm{pr}\left(5\right)/2)=1.13$

$\mathrm{PR}\left(3\right)=(1-d)\mathrm{pr}\left(3\right)+d\underset{j\&Element;\mathrm{in}\left(1\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

$=(1-0.85)*\mathrm{pr}\left(3\right)+0.85*(\mathrm{pr}\left(1\right)/3+\mathrm{pr}\left(2\right)/2+\mathrm{pr}\left(4\right)+\mathrm{pr}\left(5\right)/2)=0.708$

$\mathrm{PR}\left(4\right)=(1-d)\mathrm{pr}\left(4\right)+d\underset{j\&Element;\mathrm{in}\left(1\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

$=(1-0.85)*\mathrm{pr}\left(4\right)+0.85*(\mathrm{pr}\left(1\right)/3+\mathrm{pr}\left(2\right)/2)=0.283$

$\mathrm{PR}\left(5\right)=(1-d)\mathrm{pr}\left(5\right)+d\underset{j\&Element;\mathrm{in}\left(1\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|}=(1-0.85)*\mathrm{pr}\left(5\right)=0.15.$

Step 307, judge whether to satisfy the termination condition that iteration is determined, if satisfy, enter step 308, if do not satisfy, enter step 306, carry out next iteration and determine.

Concrete, the termination condition that iteration is determined can reach the fixed value that sets in advance for the number of times M that current iteration is determined;

The termination condition that iteration is determined also can satisfy following relational expression for the number of times M that current iteration is determined:

$\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}|\mathrm{PR}{\left(i\right)}_{M-1}-\mathrm{PR}{\left(i\right)}_M|<{\mathrm{PR}}_C;$ Perhaps, $\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}|\mathrm{PR}{\left(i\right)}_{M-1}-\mathrm{PR}{\left(i\right)}_M|\&le;{\mathrm{PR}}_C;$

Wherein, PR (i) _M-1Be the weight index value of i social object determining of the M-1 time iteration, PR (i) _MBe the weight index value of i social object determining of the M time iteration, PR _CBe default weight difference threshold.

Step 308, the weight index value order from high to low of determining according to last iteration sort to each social object that Search Results comprises, and return each social object according to the order of arranging to client, in order on client, represent each social object according to weight index value order from high to low.

The said method that adopts the embodiment of the invention 2 to provide, because determined weight index value has characterized the significance level of social object, so return each social object according to weight index value order from high to low, so that can checking by click still less, the user namely finds self social object of searching of expectation, thereby reduced the user operation has been checked in the click of each social object of comprising in the Search Results, and then reduced the consumption that the social networks side is processed resource, and the consumption that reduced network bandwidth resources.

And, can be for a certain object tag, each social object that Search Results is comprised reasonably sorts according to importance order from high to low, namely further makes things convenient for the user to find self social object of searching of expectation.

Further, in the embodiment of the invention 2, when determining that the object tag that is complementary with search condition has when a plurality of, can be according to above-mentioned steps 306 and step 307, determine respectively the weight index value of each social object for each object tag in these a plurality of object tag, and based on these a plurality of object tag label weighted value separately, each social object is weighted summation for the weight index value of these a plurality of object tag respectively, to obtain the final weight index value of social object, specifically can be as follows:

$\mathrm{Score}\left(i\right)=\underset{j=1}{\overset{P}{\mathrm{\&Sigma;}}}{W}_j*\mathrm{PR}{\left(i\right)}_j;$

Wherein, Score (i) is the final weight index value of i social object in the Search Results, and P is the number of labels of these a plurality of object tag, W _jLabel weighted value for j object tag in these a plurality of object tag can arrange according to actual needs flexibly, PR (i) _jBe the weight index value for the i of j object tag social object.

Accordingly, in the above-mentioned steps 308, specifically can sort to each social object that Search Results comprises according to the final weight index value order from high to low of determining, and return each social object according to the order of arranging to client, in order on client, represent each social object according to weight index value order from high to low.

Thereby make it possible to for a plurality of object tag, each social object that Search Results is comprised reasonably sorts according to importance order from high to low, namely further makes things convenient for the user to find self social object of searching of expectation.

In the embodiment of the invention, when determining the weight index value of social object, can determine by disposal system as shown in Figure 4.

Disposal system shown in Figure 4 comprises a controller and a plurality of processing server, the task that controller carries out needs is distributed to each processing server, each task can comprise the subtask of determining respectively the weight index value for a plurality of social object sets, each task also can comprise the subtask of determining respectively the weight index value of social object for a plurality of object tag, controller can also be distributed to each processing server with above-mentioned table 2, so that each processing server fast finding is to the needed data of weight index value of determining social object.

Each processing server can be opened a plurality of threads and come Processing tasks, for example opens k thread, and k generally can be the number of the CPU of processing server, in order to make the performance of processing server reach optimum.Why adopting this to process strategy, be because the storage of table 2 need to take a large amount of spaces, and table 2 is read-only, thereby the shared same table 2 of a plurality of threads of each processing server gets final product.

The task that each thread of each processing server can need to be carried out to the controller request, and the task record that obtains, after the task processing finishes, result is reported controller.

Embodiment 3:

Based on same inventive concept, the social object search method that provides according to the above embodiment of the present invention, correspondingly, the embodiment of the invention 3 also provides a kind of social object search device, be equivalent to the above-mentioned webserver, also this searcher can be integrated in the above-mentioned webserver, its structural representation specifically comprises as shown in Figure 5:

Search unit 501 is used for social object being searched for the social object of each that obtains being complementary with search condition according to search condition;

Acquiring unit 502 is used for successively with described each social object of each social object obtaining the social object of this current social object concern as current social object, and the social object of paying close attention to this current social object;

Weight determining unit 503 is used for the social object paid close attention to respectively according to described each social object, and the social object of paying close attention to respectively described each social object, determines the weight index value of described each social object;

Sequencing unit 504 is used for according to weight index value order from high to low, returns described each social object.

Further, acquiring unit 502 also is used for successively with described each social object of each social object determining the quantity of the social object of this current social object concern as current social object, and the quantity of paying close attention to the social object of this current social object;

Weight determining unit 503, the concrete quantity that is used for the social object paid close attention to respectively according to described each social object, and the quantity of paying close attention to respectively the social object of described each social object are determined the weight index value of described each social object.

Further, weight determining unit 503, the concrete weight index value that is used for determining successively according to following formula described each social object, and iteration is determined M time:

$\mathrm{PR}\left(i\right)=\frac{1-d}{N}+d\underset{j\&Element;\mathrm{in}\left(i\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

Wherein, PR (i) is the weight index value of i social object in definite described each the social object of this iteration, N is the quantity of described each social object, in (i) is the set of the social object of i social object of concern, out (j) is the set of the social object of j social object concern, | out (j) | be the quantity of the social object of j social object concern, pr (j) is the weight index value of the definite j of a last iteration social object, and pr (j) was the default initial weight desired value of j social object when iteration was determined for the first time, the default initial weight desired value of described each social object equates that d is default concern transition probability;

Iteration determined M time result is as the weight index value of final described each social object.

Further, above-mentioned searcher also comprises:

Label determining unit 505 is used for definite object tag that is complementary with described search condition; And determine whether described each social object has marked described object tag;

Weight determining unit 503, concrete weight index value for determining successively according to following formula for described each social object of described object tag, and iteration is determined M time:

$\mathrm{PR}\left(i\right)=(1-d)r_i+d\underset{j\&Element;\mathrm{in}\left(i\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{PR}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

Wherein, PR (i) is the weight index value of i social object in definite described each the social object of this iteration, r when i social object marking has described object tag _iBe 1, r when i social object do not marked described object tag _iBe 0, in (i) is the set of the social object of i social object of concern, out (j) is the set of the social object of j social object concern, | out (j) | be the quantity of the social object of j social object concern, pr (j) is the weight index value of the definite j of a last iteration social object, and pr (j) was the default initial weight desired value of j social object when iteration was determined for the first time, the initial weight desired value that described each social object acceptance of the bid is marked with the social object of described object tag is the first default initial weight desired value, the initial weight desired value that does not mark the social object that described object tag is arranged in described each social object is the second default initial weight desired value, and described the first initial weight desired value is greater than described the second initial weight desired value, and d is default concern transition probability;

Iteration determined M time result is as the weight index value for described each social object of described object tag.

Further, above-mentioned searcher also comprises:

Label determining unit 505 is used for definite object tag that is complementary with described search condition; And determine whether described each social object has marked described object tag;

Weight determining unit 503, concrete weight index value for determining successively according to following formula for described each social object of described object tag, and iteration is determined M time:

$\mathrm{PR}\left(i\right)=(1-d)\mathrm{pr}\left(i\right)+d\underset{j\&Element;\mathrm{in}\left(i\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

Wherein, PR (i) is the weight index value of i social object in definite described each the social object of this iteration, in (i) is the set of the social object of i social object of concern, out (j) is the set of the social object of j social object concern, out (j) | be the quantity of the social object of j social object concern, pr (j) is the weight index value of the definite j of a last iteration social object, and pr (j) was the default initial weight desired value of j social object when iteration was determined for the first time, the initial weight desired value that described each social object acceptance of the bid is marked with the social object of described object tag is the first default initial weight desired value, the initial weight desired value that does not mark the social object that described object tag is arranged in described each social object is the second default initial weight desired value, and described the first initial weight desired value is greater than described the second initial weight desired value, and d is default concern transition probability;

Iteration determined M time result is as the weight index value for described each social object of described object tag.

The number of times M that iteration was determined when further, weight determining unit 503 was carried out the definite end of iteration is the fixed value that sets in advance; Perhaps

M satisfies following relational expression:

$\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}|\mathrm{PR}{\left(i\right)}_{M-1}-\mathrm{PR}{\left(i\right)}_M|<{\mathrm{PR}}_C;$ Perhaps, $\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}|\mathrm{PR}{\left(i\right)}_{M-1}-\mathrm{PR}{\left(i\right)}_M|\&le;{\mathrm{PR}}_C;$

Wherein, PR (i) _M-1Be the weight index value of i social object determining of the M-1 time iteration, PR (i) _MBe the weight index value of i social object determining of the M time iteration, PR _CBe default weight difference threshold.

The function of above-mentioned each unit can corresponding to the respective handling step of Fig. 1 to the flow process shown in Figure 3, not repeat them here.

In sum, the scheme that the embodiment of the invention provides comprises: social object is searched for the social object of each that obtains being complementary with search condition according to search condition; And successively with each social object in each social object as current social object, obtain the social object that this current social object is paid close attention to, and the social object of paying close attention to this current social object; And the social object of paying close attention to respectively according to each social object, and the social object of paying close attention to respectively each social object is determined the weight index value of each social object; And according to weight index value order from high to low, return each social object.The scheme that adopts the embodiment of the invention to provide reduces the consumption that the social networks side is processed resource to social object search the time, and the consumption that reduces network bandwidth resources.

The searcher that the application's embodiment provides can be realized by computer program.Those skilled in the art should be understood that; above-mentioned Module Division mode only is a kind of in numerous Module Division modes; if be divided into other modules or do not divide module, as long as searcher has above-mentioned functions, all should be within the application's protection domain.

The application is that reference is described according to process flow diagram and/or the block scheme of method, equipment (system) and the computer program of the embodiment of the present application.Should understand can be by the flow process in each flow process in computer program instructions realization flow figure and/or the block scheme and/or square frame and process flow diagram and/or the block scheme and/or the combination of square frame.Can provide these computer program instructions to the processor of multi-purpose computer, special purpose computer, Embedded Processor or other programmable data processing device producing a machine, so that the instruction of carrying out by the processor of computing machine or other programmable data processing device produces the device of the function that is used for being implemented in flow process of process flow diagram or a plurality of flow process and/or square frame of block scheme or a plurality of square frame appointments.

These computer program instructions also can be stored in energy vectoring computer or the computer-readable memory of other programmable data processing device with ad hoc fashion work, so that the instruction that is stored in this computer-readable memory produces the manufacture that comprises command device, this command device is implemented in the function of appointment in flow process of process flow diagram or a plurality of flow process and/or square frame of block scheme or a plurality of square frame.

These computer program instructions also can be loaded on computing machine or other programmable data processing device, so that carry out the sequence of operations step producing computer implemented processing at computing machine or other programmable devices, thereby be provided for being implemented in the step of the function of appointment in flow process of process flow diagram or a plurality of flow process and/or square frame of block scheme or a plurality of square frame in the instruction that computing machine or other programmable devices are carried out.

Obviously, those skilled in the art can carry out various changes and modification to the present invention and not break away from the spirit and scope of the present invention.Like this, if of the present invention these are revised and modification belongs within the scope of claim of the present invention and equivalent technologies thereof, then the present invention also is intended to comprise these changes and modification interior.

Claims (10)

1. a social object search method is characterized in that, comprising:

Social object is searched for the social object of each that obtains being complementary with search condition according to search condition;

Successively with each social object in described each social object as current social object, obtain the social object that this current social object is paid close attention to, and the social object of paying close attention to this current social object;

The social object of paying close attention to respectively according to described each social object, and the social object of paying close attention to respectively described each social object are determined the weight index value of described each social object;

According to weight index value order from high to low, return described each social object.

2. the method for claim 1 is characterized in that, before the weight index value of determining described each social object, also comprises:

Successively with each social object in described each social object as current social object, determine the quantity of the social object that this current social object is paid close attention to, and the quantity of paying close attention to the social object of this current social object;

The social object of paying close attention to respectively according to described each social object, and the social object of paying close attention to respectively described each social object, determine specifically to comprise the weight index value of described each social object:

The quantity of the social object of paying close attention to respectively according to described each social object, and the quantity of paying close attention to respectively the social object of described each social object are determined the weight index value of described each social object.

3. the method for claim 1 is characterized in that, the social object of paying close attention to respectively according to described each social object, and the social object of paying close attention to respectively described each social object are determined specifically to comprise the weight index value of described each social object:

According to the following formula successively weight index value of definite described each social object, and iteration is determined M time:

$\mathrm{PR}\left(i\right)=\frac{1-d}{N}+d\underset{j\&Element;\mathrm{in}\left(i\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

Wherein, PR (i) is the weight index value of i social object in definite described each the social object of this iteration, N is the quantity of described each social object, in (i) is the set of the social object of i social object of concern, out (j) is the set of the social object of j social object concern, out (j) | be the quantity of the social object of j social object concern, pr (j) is the weight index value of the definite j of a last iteration social object, and pr (j) was the default initial weight desired value of j social object when iteration was determined for the first time, the default initial weight desired value of described each social object equates that d is default concern transition probability;

Iteration determined M time result is as the weight index value of final described each social object.

4. the method for claim 1 is characterized in that, before the weight index value of determining described each social object, also comprises:

Definite object tag that is complementary with described search condition;

Determine whether described each social object has marked described object tag;

The social object of paying close attention to respectively according to described each social object, and the social object of paying close attention to respectively described each social object, determine specifically to comprise the weight index value of described each social object:

Determine successively weight index value for described each social object of described object tag according to following formula, and iteration is determined M time:

$\mathrm{PR}\left(i\right)=(1-d)r_i+d\underset{j\&Element;\mathrm{in}\left(i\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{PR}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

Wherein, PR (i) is the weight index value of i social object in definite described each the social object of this iteration, r when i social object marking has described object tag _iBe 1, r when i social object do not marked described object tag _iBe 0, in (i) is the set of the social object of i social object of concern, out (j) is the set of the social object of j social object concern, | out (j) | be the quantity of the social object of j social object concern, pr (j) is the weight index value of the definite j of a last iteration social object, and pr (j) was the default initial weight desired value of j social object when iteration was determined for the first time, the initial weight desired value that described each social object acceptance of the bid is marked with the social object of described object tag is the first default initial weight desired value, the initial weight desired value that does not mark the social object that described object tag is arranged in described each social object is the second default initial weight desired value, and described the first initial weight desired value is greater than described the second initial weight desired value, and d is default concern transition probability;

Iteration determined M time result is as the weight index value for described each social object of described object tag.

5. the method for claim 1 is characterized in that, before the weight index value of determining described each social object, also comprises:

Definite object tag that is complementary with described search condition;

Determine whether described each social object has marked described object tag;

The social object of paying close attention to respectively according to described each social object, and the social object of paying close attention to respectively described each social object, determine specifically to comprise the weight index value of described each social object:

Determine successively weight index value for described each social object of described object tag according to following formula, and iteration is determined M time:

$\mathrm{PR}\left(i\right)=(1-d)\mathrm{pr}\left(i\right)+d\underset{j\&Element;\mathrm{in}\left(i\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

Wherein, PR (i) is the weight index value of i social object in definite described each the social object of this iteration, in (i) is the set of the social object of i social object of concern, out (j) is the set of the social object of j social object concern, | out (j) | be the quantity of the social object of j social object concern, pr (j) is the weight index value of the definite j of a last iteration social object, and pr (j) was the default initial weight desired value of j social object when iteration was determined for the first time, the initial weight desired value that described each social object acceptance of the bid is marked with the social object of described object tag is the first default initial weight desired value, the initial weight desired value that does not mark the social object that described object tag is arranged in described each social object is the second default initial weight desired value, and described the first initial weight desired value is greater than described the second initial weight desired value, and d is default concern transition probability;

Iteration determined M time result is as the weight index value for described each social object of described object tag.

6. such as the arbitrary described method of claim 3-5, it is characterized in that M is the fixed value that sets in advance; Perhaps

M satisfies following relational expression:

$\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}|\mathrm{PR}{\left(i\right)}_{M-1}-\mathrm{PR}{\left(i\right)}_M|<{\mathrm{PR}}_C;$ Perhaps, $\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}|\mathrm{PR}{\left(i\right)}_{M-1}-\mathrm{PR}{\left(i\right)}_M|\&le;{\mathrm{PR}}_C;$

Wherein, PR (i) _M-1Be the weight index value of i social object determining of the M-1 time iteration, PR (i) _MBe the weight index value of i social object determining of the M time iteration, PR _CBe default weight difference threshold.

7. a social object search device is characterized in that, comprising:

Search unit is used for social object being searched for the social object of each that obtains being complementary with search condition according to search condition;

Acquiring unit is used for successively with described each social object of each social object obtaining the social object of this current social object concern as current social object, and the social object of paying close attention to this current social object;

The weight determining unit is used for the social object paid close attention to respectively according to described each social object, and the social object of paying close attention to respectively described each social object, determines the weight index value of described each social object;

Sequencing unit is used for according to weight index value order from high to low, returns described each social object.

8. device as claimed in claim 7 is characterized in that, described weight determining unit, and the concrete weight index value that is used for determining successively according to following formula described each social object, and iteration is determined M time:

$\mathrm{PR}\left(i\right)=\frac{1-d}{N}+d\underset{j\&Element;\mathrm{in}\left(i\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

Wherein, PR (i) is the weight index value of i social object in definite described each the social object of this iteration, N is the quantity of described each social object, in (i) is the set of the social object of i social object of concern, out (j) is the set of the social object of j social object concern, | out (j) | be the quantity of the social object of j social object concern, pr (j) is the weight index value of the definite j of a last iteration social object, and pr (j) was the default initial weight desired value of j social object when iteration was determined for the first time, the default initial weight desired value of described each social object equates that d is default concern transition probability;

Iteration determined M time result is as the weight index value of final described each social object.

9. device as claimed in claim 7 is characterized in that, also comprises:

The label determining unit is used for definite object tag that is complementary with described search condition; And determine whether described each social object has marked described object tag;

Described weight determining unit, concrete weight index value for determining successively according to following formula for described each social object of described object tag, and iteration is determined M time:

$\mathrm{PR}\left(i\right)=(1-d)r_i+d\underset{j\&Element;\mathrm{in}\left(i\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{PR}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

Wherein, PR (i) is the weight index value of i social object in definite described each the social object of this iteration, r when i social object marking has described object tag _iBe 1, r when i social object do not marked described object tag _iBe 0, in (i) is the set of the social object of i social object of concern, out (j) is the set of the social object of j social object concern, | out (j) | be the quantity of the social object of j social object concern, pr (j) is the weight index value of the definite j of a last iteration social object, and pr (j) was the default initial weight desired value of j social object when iteration was determined for the first time, the initial weight desired value that described each social object acceptance of the bid is marked with the social object of described object tag is the first default initial weight desired value, the initial weight desired value that does not mark the social object that described object tag is arranged in described each social object is the second default initial weight desired value, and described the first initial weight desired value is greater than described the second initial weight desired value, and d is default concern transition probability;

Iteration determined M time result is as the weight index value for described each social object of described object tag.

10. device as claimed in claim 7 is characterized in that, also comprises:

The label determining unit is used for definite object tag that is complementary with described search condition; And determine whether described each social object has marked described object tag;

Described weight determining unit, concrete weight index value for determining successively according to following formula for described each social object of described object tag, and iteration is determined M time:

$\mathrm{PR}\left(i\right)=(1-d)\mathrm{pr}\left(i\right)+d\underset{j\&Element;\mathrm{in}\left(i\right)}{\mathrm{\&Sigma;}}\frac{\mathrm{pr}\left(j\right)}{\left|\mathrm{out}\left(j\right)\right|};$

Wherein, PR (i) is the weight index value of i social object in definite described each the social object of this iteration, in (i) is the set of the social object of i social object of concern, out (j) is the set of the social object of j social object concern, | out (j) | be the quantity of the social object of j social object concern, pr (j) is the weight index value of the definite j of a last iteration social object, and pr (j) was the default initial weight desired value of j social object when iteration was determined for the first time, the initial weight desired value that described each social object acceptance of the bid is marked with the social object of described object tag is the first default initial weight desired value, the initial weight desired value that does not mark the social object that described object tag is arranged in described each social object is the second default initial weight desired value, and described the first initial weight desired value is greater than described the second initial weight desired value, and d is default concern transition probability;

Iteration determined M time result is as the weight index value for described each social object of described object tag.

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