CN107506455A - A kind of friend recommendation method for merging user social contact circle similarity - Google Patents

A kind of friend recommendation method for merging user social contact circle similarity Download PDF

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CN107506455A
CN107506455A CN201710755695.8A CN201710755695A CN107506455A CN 107506455 A CN107506455 A CN 107506455A CN 201710755695 A CN201710755695 A CN 201710755695A CN 107506455 A CN107506455 A CN 107506455A
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msub
mrow
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徐光侠
代皓
王尧
潘霖
黄德玲
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Chongqing University of Post and Telecommunications
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Abstract

The present invention proposes a kind of friend recommendation method for merging user social contact circle similarity, is related to the identification of social networks social circle, social circle's Similarity measures and user's Similarity measures, belongs to data mining and commending system field.A kind of method is proposed, using clustering method, corporations' division is carried out to user good friend using Fast Unfolding algorithms and then calculates corporations' similarity between user, Top n is selected, further according to user property, calculates the similarity between user.By the way that user social contact circle similarity and user's similarity are combined, recommendation list is calculated, realizes high-precision friend recommendation.

Description

A kind of friend recommendation method for merging user social contact circle similarity
Technical field
The present invention relates to data mining and commending system field, more particularly to a kind of fusion user social contact circle similarity Friend recommendation method.
Background technology
With Web2.0 development, social networks turns into the typical case occurred in Web2.0 mode developments, individual character Change recommended technology to be also widely used in social networks.Social networks that is to say social network service (Social Network Service, SNS), intuitively say and can be described as network social activity, its development is broadly divided into：E-mail, BBS, BLOG, Facebook/ Renren Network etc. stage.In social networks, user is by adding the mode such as good friend, concern mechanism Carry out interaction and Information Sharing, and adding good friend between each other necessarily makes to produce a kind of contact between user, with when Between development, a kind of good friend's social networks is together form between user and its good friend and the good friend of good friend, therefrom we can To recognize the hobby of user, Information Sharing, communicational aspects and its active degree etc..Further, since social networks In user base number it is bigger, and the user in network is the stranger for being short in understanding and trusting between each other mostly, therefore Friend relation can not possibly be arbitrarily formed, these are all the problem of carrying out having to consider during friend recommendation in social networks.
It is various to push away constantly by the concern of scholars as the core component of commending system, the research of proposed algorithm Algorithm is recommended to emerge in an endless stream, but proposed algorithm has certain limitation, and what is recommended is the object with " physical property " mostly, and Research in terms of recommendation about social network user or good friend is then relatively fewer.Even if in existing friend recommendation algorithm, It is that structure good friend's recommended models such as information, user's topic preference are chained by certain method such as user mostly, two users get over Similar, they are more likely to become good friend, main to include using Saltonx similarity factors, Jaccard similarity factors as representative Based on the similarity indices of common neighbours, the similarity indices based on node similarity using Adamic-Adar as representative；Together When, in addition to the SimRank index scheduling algorithms based on random walk.As can be seen here, traditional user recommends method mainly with individual Recommended based on body similarity, few influence for considering community's factor.
The content of the invention
In order to overcome defect present in above-mentioned prior art, it is an object of the invention to provide one kind to merge user social contact circle The friend recommendation method of similarity.This method is produced initially by the way that social circle's similarity is identified, calculated to user social contact circle Recommendation list, the consequently recommended list of user's similarity generation is calculated, while with the information of user social contact circle and user itself letter Breath, which improves, recommends precision.
In order to realize the above-mentioned purpose of the present invention, the invention provides a kind of good friend for merging user social contact circle similarity to push away Method is recommended, is comprised the following steps：
S1：Gather user profile；
S2：Using the user profile gathered in S1, user social contact circle is identified；
S3：User social contact circle similarity is calculated, and carries out handing over circle sequencing of similarity；
S4：Using the friendship circle sequencing of similarity obtained in S3, with reference to user property, user's similarity is calculated；
S5：The user's similarity obtained to S4 will be ranked up, and obtain the friend recommendation list of corresponding user.
Because user base number can elapse over time in social networks, become quite huge, and user recommends to require Certain real-time.In order to be quickly obtained user social contact circle similarity ranking, Fast Unfolding schemes are selected in S2.Mould Lumpiness (modularity) refers to connecting the ratio shared by the side of community structure internal vertex in network, subtracts in same society The desired value of the ratio of the two nodes is arbitrarily connected under unity structure.The excellent of such division can be portrayed by modularity Bad, modularity is bigger, then the effect of social circle's division is better, and its formula of the calculating of modularity is：
Wherein, Ai,jWhat is represented is weight between summit i and summit j, and m is company's side number of network, Pi,jRepresent be with The desired value for the side number that summit i is connected with summit j, ciWhat is represented is community that summit is assigned to, δ (ci,cj) be used to judge top Whether point i and summit j is divided in same community, if so, then returning to 1, otherwise, returns to 0.
User social contact circle is identified in above-mentioned S2 steps and comprised the following steps：
S21：Using each user as a node, node is initialized, a user is represented with a node, represents to use with side The relation at family, there is contact between user, being then considered as has relation；The weight on side represents the number of contact.
S22：By in the community where each node division to node adjacent thereto, computing module degree Q so that modularity Q reaches maximum.
S23：The community divided in S22 polymerization is turned into a point, network is reconfigured according to the community structure of generation.
S24：The step of repeating S22, S23, untill the structure in network no longer changes, identifies the social activity of user Circle.
The formula of calculating user social contact circle similarity is in above-mentioned steps S3
Wherein pijRepresent user u1、u2The number of overlapping social circle's interior joint, eijRepresent user u1、u2In overlapping social circle The bar number on overlapping side；By user u1Social circle's recognition result be labeled as u1c1,u1c2,u1c3…u1cm, user u2Social circle know Other result queue is u2c1,u2c2,u2c3…u2cn, the degree of overlapping of one overlapping social circle of user is pij×(eij+ 1), m represents to use Family u1The social number of turns, n represents user u2The social number of turns.
Specifically, the user property in above-mentioned steps S4, including the base attribute of user, the position of user, user it is upper Net preference information.
The base attribute of the user is expressed as
userInfo(u1)={ basic (u1),tag(u1)}
Wherein, basic (u1) represent user u1Personal information description, tag (u1) represent u1Label information；
User u1, u2Base attribute similarity is designated as
sim(userInfo(u1),userInfo(u2))=λ1sim(basic(u1),basic(u2))+λ2sim(tag (u1),tag(u2))
Wherein λ1+λ2=1, basic (u2) represent u2The personal information description of user, tag (u2) represent u2Label information.
The information of the position of the user is expressed as place (u1), user u1, u2Location similarity is sim (place (u1),place(u2))；According to province, city, the similarities and differences for commonly using dwell point, sim (place (u are determined1),place(u2)) Value.
The online preference of the user is expressed as net (u1), user u1, u2Preference similarity of surfing the Net is sim (net (u1), net(u2))；User's cosine similarity is often calculated with app information, online duration, flow information according to user, represents that user's is upper Net preference similarity.
The formula of user's similarity is
Relationship of Coefficients is β1+β2+β3=1.
Beneficial effect：
The present invention carries out corporations' division to user good friend using Fast Unfolding algorithms and then calculated between user Corporations' similarity, by the way that the Similarity Measure of good friend is divided into two steps：User social contact circle Similarity Measure and user's phase Calculated like degree, so as to solve in conventional recommendation algorithm, calculate similarity commending friends only by user property, recommend precision The problem of not high.
Brief description of the drawings
The above-mentioned and/or additional aspect and advantage of the present invention, with reference to accompanying drawings below to that will become in the description of embodiment Substantially and it is readily appreciated that, wherein：
Fig. 1 is the overall flow structural representation of the present invention；
Fig. 2 is the user social contact circle identification process figure of the present invention；
Fig. 3 is the user social contact circle Similarity Measure schematic flow sheet of the present invention；
Fig. 4 is user's Similarity Measure schematic flow sheet of the present invention.
Embodiment
The specific implementation to the present invention is further explained in detail below in conjunction with the accompanying drawings.
The invention provides a kind of friend recommendation method for merging user social contact circle similarity, comprise the following steps：
S1：Each node is initialized, by the community where each node division to node adjacent thereto, so that Modularity (Modularity) Q reaches maximum, and this stage is referred to as Modularity Optimization (modularity optimization).
S2：S1 is marked off to the community's polymerization come turns into a point, i.e., the communities of users structure weight generated according to previous step Neotectonics network.The process of the above is repeated, untill the structure in network no longer changes.This process is referred to as Community Aggregation (community's polymerization).
S3：By user u1Social circle's recognition result be labeled as u1c1,u1c2,u1c3…u1cm, user u2Social circle identification Result queue is u2c1,u2c2,u2c3…u2cn, u2c1Represent user u2First social circle.pijRepresent user u1、u2Overlapping society Hand over the number of circle interior joint, eijRepresent user u1、u2The bar number on overlapping side in overlapping social circle.Calculate one overlapping social activity of user The degree of overlapping of circle is pij×(eij+ 1), according toCalculate social circle's similarity of user, m Represent user u1The social number of turns, n represents user u2The social number of turns.Sort to obtain society from big to small according to social circle's similarity Hand over circle similarity ranking Top-n good friends, the input as next step.
S4：Calculate the base attribute similarity of user.User u1Base attribute be expressed as userInfo (u1)={ basic (u1),tag(u1), basic (u1) represent user u1Personal information description, tag (u1) represent u1Label information.User u1, u2Base attribute similarity is designated as sim (userInfo (u1),userInfo(u2))=λ1sim(basic(u1),basic(u2))+ λ2sim(tag(u1),tag(u2))；Wherein λ1+λ2=1, λ1, λ2Represent coefficient.
S5：Calculate the location similarity of user, user u1Positional information be expressed as place (u1), user u1, u2Position Similarity is designated as sim (place (u1),place(u2)).According to province, city, the similarities and differences for commonly using dwell point, sim is determined (place(u1),place(u2)) value, wherein choosing the most frequently used three of user registers addresses as conventional dwell points.
S6：Calculate online preference similarity, user u1Online preference be expressed as net (u1).User u1, u2Online preference Similarity is designated as sim (net (u1),net(u2)).App information, online duration, flow information is often used to calculate more than user according to user String similarity, represent the first-class preference similarity of user.It is normal as user wherein to choose 5 sections of APP of user's frequency of use highest Use APP.
S7：Calculate user u1, u2User's similarityWherein
Relationship of Coefficients is λ1+λ2+λ3=1.
S8：User's similarity caused by previous step is ranked up according to order from high in the end, produces user's similarity Ranking Top-n, obtain the friend recommendation list of corresponding user.
Above-mentioned steps are further described in detail below in conjunction with accompanying drawing.
Fig. 1 is the overall flow structural representation of the present invention.The present invention mainly includes three parts：User social contact circle is known Not, user social contact circle Similarity Measure and user's Similarity Measure, by the Similarity Measure of end user, obtain ranking and produce Raw user's recommendation list.
When carrying out the identification of user social contact circle, user social contact network is converted into graph structure, a user is with a node Represent there was contact between user, being then considered as has relation, and the present invention represents the relation of user with side, and the weight on side represents contact Number.Then by the community where each node division to the node being adjacent, to cause modularity (Modularity) Value constantly become big, the first step is marked off to community (Community) polymerization come turns into a point, i.e., is generated according to previous step Community structure reconfigure network.The process of the above is repeated, untill the structure in network no longer changes, just have identified The social circle of user.According to the social circle of user, the overlapping node of user social contact circle and side are calculated, calculates user social contact circle Similarity, produce social circle similarity ranking.Obtain social circle similarity highest Top-n user, from user's base attribute, Positional information, online three dimensions of preference carry out the measurement of user's similitude, adjust the weight shared by three dimensions, are recommended List.
Fig. 2 is the user social contact circle identification process figure of the present invention.Mainly include two processes：Modularity Optimization and Community Aggregation.First stage is referred to as Modularity Optimization, mainly It is by the community where each node division to the node being adjacent, to cause the value of modularity constantly to become big；Second-order Section is referred to as Community Aggregation, and the first step is mainly marked off to the community's polymerization come turns into a point, i.e. basis The community structure of previous step generation reconfigures network.The process of the above is repeated, untill the structure in network no longer changes.
Specific algorithmic procedure includes：
1. initialization, each point is divided in different social circles；
2. pair each node, by each social circle put where trial is divided into the point being adjacent, calculate now Modularity, whether the difference △ Q for judging to divide front and rear modularity are positive number, if positive number, then receive this division, if not For positive number, then this division is abandoned；
3. the process of the above is repeated, untill it can not increase modularity again；
Scheme 4. construction is new, what each point in new figure represented is that each social circle come is marked in step 3, continues executing with step Rapid 2 and step 3, untill the structure of social circle no longer changes.
The calculation formula of modularity mentioned above is：Wherein, Ai,jRepresent It is the weight between summit i and summit j, m is company's side number of network, Pi,jWhat is represented is the side number being connected with summit i with summit j Desired value, ciWhat is represented is community that summit is assigned to, δ (ci,cj) be used to judge whether summit i is divided in summit j In same community, if so, then returning to 1, otherwise, 0 is returned to.
Fig. 3 is the user social contact circle Similarity Measure schematic flow sheet of the present invention.The result of communities of users identification is as use The input of family social circle Similarity Measure.First, data processing is carried out, by user u1Social circle's recognition result be labeled as u1c1, u1c2,u1c3…u1cm, user u2Social circle's recognition result be labeled as u2c1,u2c2,u2c3…u2cn.It is overlapping to calculate user one The degree of overlapping of social circle is pij×(eij+ 1), wherein pijRepresent user u1、u2The number of overlapping social circle's interior joint, eijRepresent User u1、u2The bar number on overlapping side in overlapping social circle.According toCalculate the social circle of user Similarity, so as to sort to obtain user social contact circle similarity ranking.
Fig. 4 is user's Similarity Measure schematic flow sheet of the present invention.Using user social contact circle ranking as input, from user Base attribute, the position of user, three dimensions of online preference of user carry out the calculating of user's similarity.
First, the base attribute similarity of user is calculated.basic(u1) represent user u1Personal information description, tag (u1) represent u1Label information.Personal information description includes user's sex, age, is used as use in net duration, three labels of selection The representative of family label.User u1Base attribute be expressed as userInfo (u1)={ basic (u1),tag(u1), user u1, u2 Base attribute similarity is designated as sim (userInfo (u1),userInfo(u2))=λ1sim(basic(u1),basic(u2))+λ2sim(tag(u1),tag(u2))；Wherein Relationship of Coefficients is λ1+λ2=1.The personal information description similarity of user, the present invention adopt Calculated with edit distance approach, the method is used for the similarity for calculating two natural language sentences.Similarity span for [0, 1], formula is as follows：sim(basic(u1),basic(u2))=1-Distance (basic (u1),basic(u2))/max (Length(basic(u1)),Length(basic(u2))), wherein, basic (u1)、basic(u2) editing distance be Distance(basic(u1),basic(u2)), Length (x) represents x length.Similarly, user tag similarity is also using volume Distance method is collected to calculate.
Calculate the location similarity of user, user u1Positional information be expressed as place (u1), user u1, u2Position is similar Degree is designated as sim (place (u1),place(u2)).According to province, city, the similarities and differences for commonly using dwell point, sim (place are determined (u1),place(u2)) value, wherein choosing the most frequently used three of user registers addresses as conventional dwell points；If province, city City, conventional parked position all same, sim (place (u1),place(u2))=1；If province, city are identical, parked position is commonly used Difference, sim (place (u1),place(u2))=2/3；If province is identical, city, conventional parked position are different, sim (place (u1),place(u2))=1/3；If province, city, conventional parked position are different, sim (place (u1),place(u2))=0；
Calculate online preference similarity, user u1Online preference be expressed as net (u1).User u1, u2Preference of surfing the Net is similar Degree is designated as sim (net (u1),net(u2)).User's cosine phase is often calculated with app information, online duration, flow information according to user Like degree, the online preference similarity of user is represented.5 sections of APP of user's frequency of use highest are wherein chosen to commonly use as user APP；
Finally, user u is calculated1, u2User's similarityWherein
Relationship of Coefficients is λ1+λ2+λ3=1.

Claims (10)

  1. A kind of 1. friend recommendation method for merging user social contact circle similarity, it is characterised in that comprise the following steps：
    S1：Gather user profile；
    S2：Using the user profile gathered in S1, user social contact circle is identified；
    S3：User social contact circle similarity is calculated, and carries out handing over circle sequencing of similarity；
    S4：Using the friendship circle sequencing of similarity obtained in S3, with reference to user property, user's similarity is calculated；
    S5：The user's similarity obtained to S4 will be ranked up, and obtain the friend recommendation list of corresponding user.
  2. A kind of 2. friend recommendation method for merging user social contact circle similarity according to claim 1, it is characterised in that：Institute State S2 and user social contact circle is identified and comprise the following steps：
    S21：Using each user as a node, node is initialized；
    S22：By in the community where each node division to node adjacent thereto, computing module degree Q so that modularity Q reaches To maximum；
    S23：The community divided in S22 polymerization is turned into a point, network is reconfigured according to the community structure of generation；
    S24：The step of repeating S22, S23, untill the structure in network no longer changes, identifies the social circle of user.
  3. A kind of 3. friend recommendation method for merging user social contact circle similarity according to claim 2, it is characterised in that：Institute State and node initialized in S21, be that a user is represented with a node, the relation of user is represented with side, had contact between user, Then being considered as has relation；The weight on side represents the number of contact.
  4. A kind of 4. friend recommendation method for merging user social contact circle similarity according to claim 2, it is characterised in that：Institute Stating the calculating of modularity in S22 its formula is：
    <mrow> <mi>Q</mi> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mn>2</mn> <mi>m</mi> </mrow> </mfrac> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </munder> <mo>&amp;lsqb;</mo> <msub> <mi>A</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mo>&amp;rsqb;</mo> <mi>&amp;delta;</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>C</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> </mrow>
    Wherein, Ai,jWhat is represented is weight between summit i and summit j, and m is company's side number of network, Pi,jRepresent be and summit i The desired value for the side number being connected with summit j, ciWhat is represented is community that summit is assigned to, δ (ci,cj) be used for judge summit i with Whether summit j is divided in same community, if so, then returning to 1, otherwise, returns to 0.
  5. A kind of 5. friend recommendation method for merging user social contact circle similarity according to claim 1, it is characterised in that：Institute State in S3 calculate user social contact circle similarity formula be
    <mrow> <msub> <mi>SSim</mi> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>u</mi> <mn>2</mn> </msub> </mrow> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mo>&amp;lsqb;</mo> <msub> <mi>p</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&amp;times;</mo> <mrow> <mo>(</mo> <msub> <mi>e</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow>
    Wherein pijRepresent user u1、u2The number of overlapping social circle's interior joint, eijRepresent user u1、u2It is overlapping in overlapping social circle The bar number on side；By user u1Social circle's recognition result be labeled as u1c1,u1c2,u1c3…u1cm, user u2Social circle identification knot Fruit is labeled as u2c1,u2c2,u2c3…u2cn, the degree of overlapping of one overlapping social circle of user is pij×(eij+ 1), m represents user u1 The social number of turns, n represents user u2The social number of turns.
  6. A kind of 6. friend recommendation method for merging user social contact circle similarity according to claim 1, it is characterised in that：Institute State the user property in S4, including the base attribute of user, the position of user, the online preference information of user.
  7. A kind of 7. friend recommendation method for merging user social contact circle similarity according to claim 6, it is characterised in that：Institute The base attribute for stating user is expressed as
    userInfo(u1)={ basic (u1),tag(u1)}
    Wherein, basic (u1) represent user u1Personal information description, tag (u1) represent u1Label information；
    User u1, u2Base attribute similarity is designated as
    sim(userInfo(u1),userInfo(u2))=λ1sim(basic(u1),basic(u2))+λ2sim(tag(u1),tag (u2))
    Wherein λ1+λ2=1, basic (u2) represent u2The personal information description of user, tag (u2) represent u2Label information.
  8. A kind of 8. friend recommendation method for merging user social contact circle similarity according to claim 6, it is characterised in that：Institute The information for stating the position of user is expressed as place (u1), user u1, u2Location similarity is sim (place (u1),place (u2))；According to province, city, the similarities and differences for commonly using dwell point, sim (place (u are determined1),place(u2)) value.
  9. A kind of 9. friend recommendation method for merging user social contact circle similarity according to claim 6, it is characterised in that：Institute The online preference for stating user is expressed as net (u1), user u1, u2Preference similarity of surfing the Net is sim (net (u1),net(u2))；Root User's cosine similarity often is calculated with app information, online duration, flow information according to user, represents that the online preference of user is similar Degree.
  10. 10. a kind of friend recommendation method of fusion user social contact circle similarity according to claim 6 or 7 or 8 or 9, its It is characterised by：The formula of calculating user's similarity is in the S4
    <mrow> <msub> <mi>USim</mi> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>u</mi> <mn>2</mn> </msub> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;lambda;</mi> <mn>1</mn> </msub> <mi>s</mi> <mi>i</mi> <mi>m</mi> <mrow> <mo>(</mo> <mi>b</mi> <mi>a</mi> <mi>s</mi> <mi>i</mi> <mi>c</mi> <mo>(</mo> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo>)</mo> <mo>,</mo> <mi>b</mi> <mi>a</mi> <mi>s</mi> <mi>i</mi> <mi>c</mi> <mo>(</mo> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo>)</mo> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>&amp;lambda;</mi> <mn>2</mn> </msub> <mi>s</mi> <mi>i</mi> <mi>m</mi> <mrow> <mo>(</mo> <mi>p</mi> <mi>l</mi> <mi>a</mi> <mi>c</mi> <mi>e</mi> <mo>(</mo> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo>)</mo> <mo>,</mo> <mi>p</mi> <mi>l</mi> <mi>a</mi> <mi>c</mi> <mi>e</mi> <mo>(</mo> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo>)</mo> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>&amp;lambda;</mi> <mn>3</mn> </msub> <mi>s</mi> <mi>i</mi> <mi>m</mi> <mrow> <mo>(</mo> <mi>n</mi> <mi>e</mi> <mi>t</mi> <mo>(</mo> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo>)</mo> <mo>,</mo> <mi>n</mi> <mi>e</mi> <mi>t</mi> <mo>(</mo> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo>)</mo> <mo>)</mo> </mrow> </mrow>
    Relationship of Coefficients is λ1+λ2+λ3=1.
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