Ranking aggregation method and device, equipment and storage medium

Abstract

The invention discloses a ranking aggregation method and device, equipment and a storage medium. The method comprises the steps of obtaining ranking information; constructing a user similarity relation network based on the ranking information, and decomposing the user similarity relation network into a plurality of high-density communities according to a preset community detection algorithm; obtaining behavior structure characteristics of the high-density community based on the ranking information, and determining ranking weights of users in the high-density community according to the behavior structure characteristics of the high-density community; and calculating a ranking aggregation result of the objects by using the ranking weight and the ranking information based on a preset ranking aggregation method. Analyzing ranking results of the users on the objects, finding out high-density communities among the users, obtaining ranking weights of the high-density communities, obtaining ranking aggregation results according to ranking information and the ranking weights of the users in the high-density communities, and sending the ranking aggregation results to the users. The ranking weight determined through the behavior structure characteristics of the collusion users reflects the effectiveness of the collusion ranking result, so that the authenticity of the ranking aggregation result is improved.

Description

Technical Field

[0001] The present invention relates to the field of ranking aggregation technology, and in particular to a ranking aggregation method and apparatus, device and storage medium. Background Art

[0002] Ranking is an intuitive and effective form of selection and evaluation opinion display. Ranking problems appear in many fields, such as university or subject rankings given by professional evaluation agencies, scientist rankings in academic fields, and the possibility ranking of pathogenic genes in biomedical engineering. In most cases, decision makers often need to take into account the ranking criteria of different attributes or the preference information of different judges to obtain a consistent ranking of the object. The task of integrating many single ranking lists into a consistent ranking list that takes into account all information is also called the ranking aggregation problem.

[0003] At present, due to the lack of an effective identification mechanism, most ranking aggregation methods have poor robustness against abnormal data such as malicious negative reviews and online scams. Especially when a large number of users collude to attack, that is, multiple users publish similar ranking results after prior consultation, traditional ranking aggregation methods will fail, causing these abnormal data to become "mainstream opinions" in the information fusion process, thereby controlling the ranking results. Summary of the invention

[0004] Based on this, it is necessary to propose a ranking aggregation method and device, equipment and storage medium to address the above problems in order to improve the reliability of ranking results.

[0005] To achieve the above objectives, the present application provides a ranking aggregation method in a first aspect, the method comprising:

[0006] Obtaining ranking information, wherein the ranking information includes a ranking result of each user for a preset object;

[0007] Constructing a user similarity relationship network based on the ranking information, and decomposing the user similarity relationship network into a plurality of high-density communities according to a preset community detection algorithm;

[0008] Obtaining the behavior structure characteristics of the high-density community based on the ranking information, and determining the ranking weights of users in the high-density community according to the behavior structure characteristics of the high-density community;

[0009] The ranking aggregation result of the object is calculated based on a preset ranking aggregation method using the ranking weight and the ranking information.

[0010] Furthermore, constructing a user similarity relationship network based on the ranking information specifically includes:

[0011] Normalizing the ranking information to obtain ranking data of the object by each user;

[0012] Calculate user similarity between two users according to the ranking data of the objects by each user, wherein the user similarity is the similarity between the ranking results of two users;

[0013] Take users as nodes and similarities as edges to build a user similarity relationship network.

[0014] Furthermore, the calculating of user similarities between two users according to the ranking data of the objects by each user specifically includes:

[0015] Acquire a first ranking object set of a first user and a second ranking object set of a second user, wherein the first user and the second user are any one of all users;

[0016] taking the union of the first ranking object set and the second ranking object set as a third ranking object set;

[0017] Calculate the ranking similarity of each ranked object in the third ranked object set by the first user and the second user according to the ranking data of each user on the object and the third ranked object set;

[0018] The user similarity between the two users is calculated according to the ranking similarity of the first user and the second user to each ranking object in the third ranking object set.

[0019] Furthermore, decomposing the user similarity relationship network into a plurality of high-density communities according to a preset community detection algorithm specifically includes:

[0020] Taking each node in the user similarity relationship network as a community, calculating the modularity gain between the first node and the community where each other node is located for the i-th time, wherein the first node is any one of all nodes, and the initial value of i is 1;

[0021] When there is a positive gain in the modularity gain between the first node and the communities where each other node is located, the first node is moved to the community where the second node is located to obtain an aggregated community, wherein the second node is the node corresponding to the maximum modularity gain among the modularity gains between the first node and the communities where each other node is located;

[0022] Each of the aggregated communities is taken as a new node, with i=i+1, and the step of calculating the modularity gain between the first node and the communities where other nodes are located for the i-th time is continued until a preset stop condition is met, thereby obtaining a plurality of high-density communities in the user similarity relationship network.

[0023] Furthermore, determining the ranking weights of users in the high-density community according to the behavioral structure characteristics of the high-density community specifically includes:

[0024] Calculating a preset collusion index according to the behavioral structural characteristics of the high-density community to obtain collusion index data of the high-density community;

[0025] The ranking weight of each user in the high-density community is calculated according to the collusion indicator data.

[0026] Furthermore, the calculating of a preset collusion index according to the behavioral structural characteristics of the high-density community to obtain the collusion index data of the high-density community specifically includes:

[0027] According to the behavior structure characteristics of the high-density community, a target ranking object set in the target high-density community and ranking data of the target ranking objects combined by users in the target high-density community are obtained, wherein the target high-density community is any one of all high-density communities, and the target ranking object set is a set of objects ranked by two or more users in the target high-density community;

[0028] Obtaining ranking data of the target ranking object set from other communities except the target high-density community;

[0029] Calculating the deviation of the ranking of the target high-density community based on the ranking data of the target ranking object set in the target high-density community and the ranking data of the target ranking object set in other communities to obtain the ranking deviation of the target high-density community;

[0030] Obtaining the number of users who rank a target ranking object in the target high-density community based on the behavioral structure characteristics of the high-density community, where the target ranking object is any one of the target ranking object set;

[0031] Obtaining the total number of users who have ranked the target ranking object;

[0032] Calculate a ratio based on the number of users who rank the target ranking object in the target high-density community and the total number of users to obtain a user ratio of the target high-density community;

[0033] Determining the community size of the target high-density community according to the behavioral structural characteristics of the high-density community;

[0034] The ranking deviation, user ratio and community size of the target high-density community constitute the collusion indicator data of the target high-density community.

[0035] Furthermore, the ranking weight of each user in the high-density community is calculated by the following formula:

[0036]

[0037] Where W(g) is the ranking weight of the target high-density community, RD(g) is the ranking deviation of the target high-density community, UR(g) is the user ratio of the target high-density community, GS(g) is the community size of the target high-density community, and U g is the number of users in the target high-density community.

[0038] To achieve the above-mentioned purpose, the second aspect of the present application provides a ranking aggregation device, the device comprising: an information acquisition unit, a network decomposition unit and the ranking aggregation unit;

[0039] The information acquisition unit is used to acquire ranking information, wherein the ranking information includes a ranking result of each user for a preset object;

[0040] The network decomposition unit is used to construct a user similarity relationship network based on the ranking information, and decompose the user similarity relationship network into a plurality of high-density communities according to a preset community detection algorithm;

[0041] The ranking aggregation unit is used to obtain the behavior structure characteristics of the high-density community based on the ranking information, and determine the ranking weights of users in the high-density community according to the behavior structure characteristics of the high-density community;

[0042] The ranking aggregation result of the object is calculated based on a preset ranking aggregation method using the ranking weight and the ranking information.

[0043] To achieve the above-mentioned purpose, the third aspect of the present application provides a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the processor executes the steps of the method described in the first aspect.

[0044] To achieve the above-mentioned purpose, the fourth aspect of the present application provides a computer device, including a memory and a processor, characterized in that the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method described in the first aspect.

[0045] The embodiments of the present invention have the following beneficial effects:

[0046] The embodiment of the present invention proposes a ranking aggregation method, which includes: obtaining ranking information, the ranking information includes the ranking results of each user for a preset object; constructing a user similarity relationship network based on the ranking information, and decomposing the user similarity relationship network into a number of high-density communities according to a preset community detection algorithm; obtaining the behavioral structure characteristics of the high-density community based on the ranking information, and determining the ranking weights of users in the high-density community according to the behavioral structure characteristics of the high-density community; and calculating the ranking aggregation results of the object using the ranking weights and ranking information based on a preset ranking aggregation method. By analyzing the ranking results of the user for the object, the high-density community between the users is found, and the appropriate ranking weight is determined according to the behavioral structure characteristics of the high-density community, so as to aggregate the ranking information and ranking weights of the users in the high-density community to obtain the ranking aggregation results, and the ranking weights determined by the behavioral structure characteristics of the colluding users are used to reflect the effectiveness of the ranking results used for collusion, so as to improve the authenticity of the ranking aggregation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] in:

[0049] Figure 1 Schematic diagram of the flow of the ranking aggregation method in an embodiment of the present invention;

[0050] Figure 2 A user similarity relationship network in an embodiment of the present invention;

[0051] Figure 3 It is a structural block diagram of a ranking aggregation device in an embodiment of the present invention;

[0052] Figure 4 1 is a diagram showing the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Ranking is an intuitive form of selection and evaluation, which is convenient for users to compare and choose. When collecting users' ranking information on objects, it is found that there may be a large number of users who give malicious negative reviews to certain specific objects, such as water army scoring, etc. This phenomenon is called user collusion attack in the present invention, and multiple users who publish similar ranking results after prior consultation are called suspected collusion attack groups.

[0055] In order to solve the problem of collusion user attacks, the embodiment of the present invention discloses a ranking aggregation method, which obtains the user similarity relationship network and uses community mining to identify high-density communities in the user similarity relationship network, that is, suspected collusion attack groups. For users suspected of collusion attacks, measures such as reducing weights are taken to eliminate the impact of abnormal data on ranking aggregation results. For details, please refer to Figure 1 , Figure 1 The following is a flow chart of a ranking aggregation method according to an embodiment of the present invention. The method includes:

[0056] Step 110: Obtain ranking information, where the ranking information includes the ranking result of each user for a preset object.

[0057] In one embodiment of the present invention, assume that there are N users and M objects, and the users can rank some or all of the objects. Let R = (r ui ) N×M Represents the ranking matrix obtained, that is, ranking information. Among them, r ui ≤L u , r ui represents the ranking of object i in the ranking results of user u, L u =|r ui |r ui >0, 1≤i≤M, 1≤u≤N, L u Indicates the ranking length of the ranking result of user u. If user u does not rank object i, then r ui = 0; if user u ranks object i and object j the same, then r ui =r uj .

[0058] Step 120: construct a user similarity relationship network based on the ranking information, and decompose the user similarity relationship network into a number of high-density communities according to a preset community detection algorithm.

[0059] In the embodiment of the present invention, similarity analysis is performed on the ranking results of each user for the object to determine the similarity of the ranking results among all users, and a user similarity relationship network is constructed with each user as a node and the similarity between two users as an edge.

[0060] The ranking objects and ranking results of objects for users in high-density communities are highly similar, and these users are considered to be suspected of conspiracy attacks. Since users in high-density communities are very likely to have malicious behavior when ranking, the ranking weights of users in high-density communities can be reduced to eliminate the impact of abnormal data on the aggregate ranking results during aggregate ranking, making the aggregate ranking results more reliable.

[0061] In order to identify high-density communities in user similarity networks, i.e. suspected groups of collusion attacks, the embodiment of the present invention uses a preset community detection algorithm to mine high-density communities in user similarity networks, so as to determine the weight of the ranking results of users in the high-density communities based on the ranking information of the high-density communities, thereby improving the reliability of the ranking results of users in the high-density communities.

[0062] Step 130: Obtain the behavior structure characteristics of the high-density community based on the ranking information, and determine the ranking weights of users in the high-density community according to the behavior structure characteristics of the high-density community.

[0063] In the embodiment of the present invention, the behavior characteristics and structural characteristics such as the ranking objects, ranking results, and number of users in the high-density community can be obtained based on the ranking information analysis.

[0064] The behavioral structural characteristics of high-density communities can determine whether the ranking behavior of users in high-density communities is abnormal. For example, if users in high-density communities provide similar / identical rankings for a specific object, it means that users are conspiring to attack the object or that the ranking is being scalped by online trolls. If a specific object is mainly ranked by users in high-density communities, and users other than the community rarely participate in the ranking, it can also mean that users are conspiring to attack the object or that the ranking is being scalped by online trolls. Most communities consisting of 2 or 3 users may be formed by chance, while large communities consisting of many users are often formed in collusion in advance.

[0065] In one embodiment of the present invention, the malicious behavior degree of the high-density community is determined by the behavioral structure characteristics of the high-density community, and an appropriate ranking weight is determined based on the malicious behavior degree, so as to perform ranking aggregation based on the ranking weight.

[0066] Step 140: Calculate the ranking aggregation result of the object using the ranking weight and ranking information based on a preset ranking aggregation method.

[0067] In the embodiment of the present invention, users belonging to the same high-density community have the same ranking weight. The ranking aggregation method can be Borda Count method, Dowdall method, etc., which are not limited here.

[0068] The ranking aggregation method aggregates the ranking results and ranking weights of multiple users into a comprehensive ranking list, which can be expressed as:

[0069]

[0070] In the formula, w u represents the ranking weight of user u, R u represents the ranking result of user u, represents the ranking aggregation method, and then the ranking r of object i in the ranking results of user u ui Can be replaced by r ui ·w u .

[0071] The present invention establishes an overall association between users through their ranking results and obtains a user similarity network corresponding thereto; identifies high-density communities in the user similarity network, i.e., suspected collusion attack groups, through community mining, calculates the ranking weight of each community, and then aggregates the ranking results of multiple users in combination with the ranking weights, which can effectively eliminate the influence of abnormal data such as malicious evaluations and collusion attacks, making the aggregated ranking results more accurate and reliable.

[0072] In one embodiment of the present invention, step 120, constructing a user similarity relationship network based on ranking information, specifically includes:

[0073] Step 121, normalize the ranking information to obtain the ranking data of each user for the object.

[0074] In the embodiment of the present invention, the user can rank some or all of all objects, so the maximum and minimum value normalization is used to linearly map the ranking results to the [0,1] interval to obtain the normalized user ranking data of the objects. The ranking data can be calculated by the following formula:

[0075]

[0076] In the formula, r ui represents the ranking of object i in the ranking results of user u, max(R u ) and min(R u ) represent the ranking results R of user u respectively. u The maximum and minimum ranking in .

[0077] Step 122, calculate the user similarity between two users based on the ranking data of each user on the object, and the user similarity is the similarity between the ranking results of two users.

[0078] By analyzing the ranking data of each object by each user, the similarity of the ranking results between any two users can be obtained.

[0079] In one embodiment of the present invention, Step 122, calculating the user similarity between two users according to the ranking data of each user on the object, specifically includes:

[0080] A1. Obtain a first ranking object set of a first user and a second ranking object set of a second user, wherein the first user and the second user are any one of all users.

[0081] Specifically, the ranking object sets of any two users are obtained. For example, the ranking object set of user u is (A, B, C, D), and the ranking object set of user v is (A, B, C).

[0082] A2. The union of the first ranking object set and the second ranking object set is used as the third ranking object set.

[0083] Specifically, take the union of the ranking object sets of two users. For example, if the ranking object set of user u is (A, B, C, D) and the ranking object set of user v is (A, B, C), then the third ranking object set is (A, B, C).

[0084] A3. Calculate the ranking similarity of each ranked object in the third ranked object set by the first user and the second user based on the ranking data of each user on the object and the third ranked object set.

[0085] In the embodiment of the present invention, the ranking similarity between two users for each third ranking object is determined based on the ranking data of the two users for the third ranking object.

[0086] Specifically, the ranking similarity between two users can be obtained according to the following formula:

[0087]

[0088] Where s(u,v,i) is the ranking similarity between user u and user v on object i, w(r i ″) is the similarity weight, represents the ranking data of user u for object i, Represents the ranking data of user v for object i.

[0089] In another embodiment of the present invention, a method for determining similarity weight based on ranking data is introduced. The similarity weight can be obtained according to the following formula:

[0090]

[0091] r i ″=min(r u ' i ,r v ' i ),

[0092]

[0093] In the formula, α is a positive constant that determines the weight w(r i ″) with r i The rate of decrease of the increase of ″, min(r u ' i ,r v ' i ) indicates ranking r u ' i and r v ' i The minimum value in r u ' i 、r v ' i Indicates how far a high rank is from the top of the ranked list or how far a low rank is from the bottom of the ranked list. This similarity weight reflects that deviations between high-ranked or low-ranked objects are more important than deviations between objects in the middle.

[0094] A4. Calculate the user similarity between the two users according to the ranking similarity of the first user and the second user for each ranking object in the third ranking object set.

[0095] Specifically, after obtaining the ranking similarity of the first user and the second user for each ranking object in the third ranking object set, the user similarity between the two users, that is, the similarity between the ranking results of the two users, can be further calculated.

[0096] In one embodiment of the invention, the user similarity between two users is calculated by the following formula:

[0097]

[0098] Where S(u,v) is the user similarity between user u and user v, I u and I v They represent the ranking object sets of user u and user v, namely the first ranking object set and the second ranking object set respectively.

[0099] Step 123. Take users as nodes and similarities as edges to build a user similarity relationship network.

[0100] Since the ranking results published by colluding users are the same or similar, in order to identify colluding users, an embodiment of the present invention constructs a user similarity relationship network based on user similarity, in which users are used as nodes and similar relationships are used as edges. User similarity is defined as the weight of the edge, and only edges with weights greater than a preset threshold β are retained.

[0101] In one embodiment of the present invention, in order to identify high-density communities in a user similarity relationship network, a Louvain community detection algorithm may be used. Specifically, step 130 decomposes the user similarity relationship network into a plurality of high-density communities according to a preset community detection algorithm, and specifically includes:

[0102] Step 131. Take each node in the user similarity relationship network as a community, and calculate the modularity gain between the first node and the community where each other node is located for the i-th time, where the first node is any one of all the nodes and the initial value of i is 1.

[0103] In the embodiment of the present invention, each node in the user similarity relationship network is regarded as an independent community, and the modularity gain is calculated for all nodes. For example, the community where node u (the first node) is located is c u , the community where node v (any other node) is located is c v , then, node u and community c v The modular gain formula is:

[0104]

[0105] In the formula, ∑ in Indicates community c v The sum of the edge weights within k u,in Represents a link from node u to community c v The sum of the weights of the nodes in ∑ tot Indicates link to community c v The sum of the edge weights of any node in k u represents the sum of the weights of all edges linked to node u, and m represents the sum of the weights of all edges in the user similarity relationship network.

[0106] The above method can be used to calculate the gain between any node and the community where other nodes are located.

[0107] Step 132: When there is a positive modularity gain between the first node and the community where each other node is located, the first node is moved to the community where the second node is located to obtain an aggregated community, wherein the second node is the node corresponding to the maximum modularity gain among the modularity gains between the first node and the community where each other node is located.

[0108] The embodiment of the present invention selects a local optimal method to perform the mobile operation. Considering the modularity gain between two adjacent nodes, the node u is moved from the original community c u Move to community c where node v is located v The modularity gain size is used to perform the move operation.

[0109] Specifically, if the first node does not generate a positive gain when it moves to the community where other nodes are located, it will not move; if there is a positive gain when the first node moves to the community where other nodes are located, the first node moves to the community with the maximum modularity gain to obtain the aggregated community.

[0110] Step 133, take each aggregated community as a new node, and i=i+1, and continue to execute the step of calculating the modularity gain between the first node and the communities where other nodes are located for the i-th time until the preset stopping condition is met, and obtain several high-density communities in the user similarity relationship network.

[0111] In the embodiment of the present invention, each aggregated community is regarded as a new node, and the edge weights between communities are converted into edge weights between new nodes, that is, the edge weight between two new nodes is equal to the sum of the edge weights between the nodes in the two communities, so as to construct a new network.

[0112] At this step, the process jumps to Step 131 and iterates until the user similarity relationship network cannot be simplified any more, or reaches a predetermined stop condition, such as stopping the iteration after a preset number of iterations. In the above manner, the user similarity relationship network is finally decomposed into multiple high-density communities.

[0113] In one embodiment of the present invention, step 140, determining the ranking weights of users in the high-density community according to the behavioral structure characteristics of the high-density community, specifically includes:

[0114] Step 141. Calculate the preset collusion index according to the behavioral structure characteristics of the high-density community to obtain the collusion index data of the high-density community.

[0115] In order to eliminate the impact of abnormal data, the behavioral and structural characteristics of colluding users can be considered, and the collusion index can be introduced to calculate the weight of high-density communities.

[0116] In one embodiment of the present invention, the collusion index may include ranking deviation, user ratio and community size. Specifically, Step 141 calculates the preset collusion index according to the behavioral structure characteristics of the high-density community to obtain the collusion index data of the high-density community, which specifically includes:

[0117] B1. Obtain a target ranking object set in a target high-density community and ranking data of the target ranking object set by users in the target high-density community according to the behavioral structural characteristics of the high-density community, wherein the target high-density community is any one of all high-density communities, and the target ranking object set is a set of objects ranked by two or more users in the target high-density community; obtain ranking data of the target ranking object set by other communities except the target high-density community; calculate the deviation based on the ranking data of the target ranking object set in the target high-density community and the ranking data of the target ranking object set in other communities, and obtain the ranking deviation of the target high-density community.

[0118] Specifically, the ranking deviation inside and outside the target high-density community is calculated based on the behavioral characteristics, and the target high-density community is any one of all high-density communities. The target ranking object set of users in the target high-density community and the ranking data of the target ranking objects by users in the target high-density community are obtained through the ranking information. The target ranking object set is the set of objects ranked by two or more users in the target high-density community.

[0119] Based on the behavioral characteristics of the above-mentioned target high-density community and the behavioral characteristics of other communities outside the target high-density community, the ranking deviation inside and outside the target high-density community is determined.

[0120] In one embodiment of the present invention, the ranking deviation is the mean of the average ranking deviations of objects inside and outside the high-density community, expressed as:

[0121]

[0122] Where RD(g) represents the ranking deviation of high-density community g, I g represents the set of target ranking objects ranked by more than two users in the high-density community g, and gavg(i) and ngavg(i) represent the average ranking of object i inside and outside the high-density community g, respectively.

[0123] It is understandable that the larger the ranking deviation RD(g) of the high-density community g is, the more likely it is that the high-density community g is a collusion user community.

[0124] B2. Based on the behavioral structure characteristics of high-density communities, obtain the number of users who rank the target ranking object in the target high-density community, where the target ranking object is any one in the target ranking object set; obtain the total number of users who rank the target ranking object. Calculate the ratio based on the number of users who rank the target ranking object in the target high-density community and the total number of users to obtain the user ratio of the target high-density community.

[0125] Specifically, the proportion of users who rank the object in the high-density community to the total number of users is calculated based on the behavioral characteristics. The number of users who rank the target ranking object in the target high-density community is obtained through ranking information. Based on the behavioral characteristics of the above target high-density community and the total number of users who rank the target ranking object in the entire user similarity relationship network, the maximum value of the ratio of the number of users who rank the target ranking object in the high-density community to the total number of users who rank the target ranking object is obtained, and it is used as the user ratio of the target high-density community, which can be calculated by the following formula:

[0126]

[0127] In the formula, UR(g) represents the proportion of users in high-density community g, U i represents the total number of users who rank the target ranking object i, U gi Represents the number of users who rank the target ranking object i in the high-density community g.

[0128] It can be understood that the larger the user ratio UR(g) of the high-density community g is, the greater the possibility that the high-density community g is a collusion user community.

[0129] B3. Determine the community size of the target high-density community based on the behavioral structural characteristics of the high-density community.

[0130] Specifically, the number of users in each high-density community is obtained through ranking information, and the community size GS(g) of the high-density community is obtained:

[0131]

[0132] Where U g Represents the number of users in the high-density community g.

[0133] It can be understood that the larger the community size GS(g) of the high-density community g is, the greater the possibility that the high-density community g is a collusion user community.

[0134] B4. The ranking deviation, user ratio and community size of the target high-density community constitute the collusion indicator data of the target high-density community.

[0135] Step 142: Calculate the ranking weight of each user in the high-density community based on the collusion indicator data.

[0136] In one embodiment of the present invention, the ranking weight of each user in the high-density community is calculated by the following formula:

[0137]

[0138] Where W(g) is the ranking weight of the target high-density community, RD(g) is the ranking deviation of the target high-density community, UR(g) is the user ratio of the target high-density community, GS(g) is the community size of the target high-density community, and U g is the number of users in the target high-density community.

[0139] The embodiment of the present invention formally describes the ranking data through the method of network science, establishes the overall association between users, and obtains the corresponding user similarity relationship network; identifies the high-density communities in the user similarity relationship network, that is, the suspected group of conspiracy attack, through community mining, calculates the ranking weight of the high-density communities, and then aggregates the ranking results of multiple users in combination with the ranking weight, which can effectively eliminate the influence of abnormal data such as malicious evaluation and conspiracy attack.

[0140] In one embodiment of the present invention, it is assumed that 8 users rank 5 objects, and each ranking includes 3 objects. The specific rankings are shown in Table 1. As can be seen from Table 1, the ranking results of users u6, u7 and u8 are the same, and are seriously inconsistent with the rankings of other judges, which is suspected of conspiracy attack.

[0141] Table 1 Ranking matrix

[0142] <![CDATA[i1]]> <![CDATA[i2]]> <![CDATA[i3]]> <![CDATA[i4]]> <![CDATA[i5]]> <![CDATA[u1]]> 1 2 3 0 0 <![CDATA[u2]]> 1 2 0 3 0 <![CDATA[u3]]> 0 1 2 3 0 <![CDATA[u4]]> 1 2 0 0 3 <![CDATA[u5]]> 1 0 2 3 0 <![CDATA[u6]]> 3 2 1 0 0 <![CDATA[u7]]> 3 2 1 0 0 <![CDATA[u8]]> 3 2 1 0 0

[0143] The ranking aggregation method of the present invention is used to aggregate the ranking, and the threshold β is selected as 0.6. First, the overall association between users is established, and the corresponding user similarity relationship network is obtained, such as Figure 2 As shown, Figure 2 The user similarity relationship network in the embodiment of the present invention. Secondly, the Louvain community detection algorithm is used to identify high-density communities in the user similarity relationship network. Figure 2 It can be seen that users u6, u7, and u8 are identified as suspected collusion attack groups. On this basis, the weight of each user is calculated using the collusion index, as shown in Table 2. Finally, combining the above weights and user ranking data, the aggregate ranking is obtained by the Borda counting method as shown in Table 3. The ranking results are: v1>v2>v3>v4>v5. Based on this, it can be seen that the ranking aggregation method proposed in the present invention can effectively eliminate the influence of abnormal data.

[0144] Table 2 User weights

[0145] <![CDATA[w1]]> <![CDATA[w2]]> <![CDATA[w3]]> <![CDATA[w4]]> <![CDATA[w5]]> <![CDATA[w6]]> <![CDATA[w7]]> <![CDATA[w8]]> 1 1 1 1 1 0.4656 0.4656 0.4656

[0146] Table 3 Weighted ranking matrix

[0147] <![CDATA[i1]]> <![CDATA[i2]]> <![CDATA[i3]]> <![CDATA[i4]]> <![CDATA[i5]]> <![CDATA[u1]]> 1 2 3 0 0 <![CDATA[u2]]> 1 2 0 3 0 <![CDATA[u3]]> 0 1 2 3 0 <![CDATA[u4]]> 1 2 0 0 3 <![CDATA[u5]]> 1 0 2 3 0 <![CDATA[u6]]> 1.3695 0.9312 0.4656 0 0 <![CDATA[u7]]> 1.3695 0.9312 0.4656 0 0 <![CDATA[u8]]> 1.3695 0.9312 0.4656 0 0

[0148] The embodiment of the present invention also provides a ranking aggregation device, which can be referred to Figure 3 , Figure 3 The ranking aggregation device in the embodiment of the present invention includes: an information acquisition unit 301 , a network decomposition unit 302 and a ranking aggregation unit 303 .

[0149] The information acquisition unit 301 is used to acquire ranking information, where the ranking information includes the ranking result of each user for a preset object.

[0150] The network decomposition unit 302 is used to construct a user similarity relationship network based on the ranking information, and decompose the user similarity relationship network into a plurality of high-density communities according to a preset community detection algorithm.

[0151] The ranking aggregation unit 303 is used to obtain the behavior structure characteristics of the high-density community based on the ranking information, and determine the ranking weights of users in the high-density community according to the behavior structure characteristics of the high-density community.

[0152] The ranking aggregation result of the object is calculated based on the preset aggregation method using the ranking weight and ranking information.

[0153] The ranking aggregation device proposed in the embodiment of the present invention establishes an overall association between users based on the ranking results of the users, and obtains a user similarity relationship network corresponding thereto; identifies high-density communities in the user similarity relationship network, i.e., suspected groups of collusion attacks, through community mining, calculates the ranking weight of each community, and then aggregates the ranking results of multiple users in combination with the ranking weights, which can effectively eliminate the influence of abnormal data such as malicious evaluations and collusion attacks, making the aggregated ranking results more accurate and reliable.

[0154] Figure 4 FIG. 1 shows an internal structure diagram of a computer device in one embodiment of the present invention. The computer device may be a terminal or a system. Figure 4 As shown, the computer device includes a processor, a memory and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor may implement each step in the above method embodiment. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor may implement each step in the above method embodiment. Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0155] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes each step in the above method embodiment.

[0156] In one embodiment, a computer-readable storage medium is proposed, which stores a computer program. When the computer program is executed by a processor, the processor executes each step in the above method embodiment. A person of ordinary skill in the art can understand that all or part of the processes in the above embodiment method can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium, and the program can include the processes of the embodiments of the above methods when executed. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0157] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0158] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A ranking aggregation method, characterized in that: The method comprises: Obtaining ranking information, wherein the ranking information includes a ranking result of each user for a preset object; Constructing a user similarity relationship network based on the ranking information, and decomposing the user similarity relationship network into a plurality of high-density communities according to a preset community detection algorithm; Obtaining the behavior structure characteristics of the high-density community based on the ranking information, and determining the ranking weights of users in the high-density community according to the behavior structure characteristics of the high-density community; The ranking aggregation result of the object is calculated based on a preset ranking aggregation method using the ranking weight and the ranking information.

2. The method according to claim 1, characterized in that The step of constructing a user similarity relationship network based on the ranking information specifically includes: Normalizing the ranking information to obtain ranking data of the object by each user; Calculate user similarity between two users according to the ranking data of the objects by each user, wherein the user similarity is the similarity between the ranking results of two users; Take users as nodes and similarities as edges to build a user similarity relationship network.

3. The method according to claim 2, characterized in that The calculating the user similarity between two users according to the ranking data of the objects by each user specifically includes: Acquire a first ranking object set of a first user and a second ranking object set of a second user, wherein the first user and the second user are any one of all users; taking the union of the first ranking object set and the second ranking object set as a third ranking object set; Calculate the ranking similarity of each ranked object in the third ranked object set by the first user and the second user according to the ranking data of each user on the object and the third ranked object set; The user similarity between the two users is calculated according to the ranking similarity of the first user and the second user to each ranking object in the third ranking object set.

4. The method according to claim 1, characterized in that Decomposing the user similarity relationship network into a plurality of high-density communities according to a preset community detection algorithm specifically includes: Taking each node in the user similarity relationship network as a community, calculating the modularity gain between the first node and the community where each other node is located for the i-th time, wherein the first node is any one of all nodes, and the initial value of i is 1; When there is a positive gain in the modularity gain between the first node and the communities where each other node is located, the first node is moved to the community where the second node is located to obtain an aggregated community, wherein the second node is the node corresponding to the maximum modularity gain among the modularity gains between the first node and the communities where each other node is located; Each of the aggregated communities is taken as a new node, with i=i+1, and the step of calculating the modularity gain between the first node and the communities where other nodes are located for the i-th time is continued until a preset stop condition is met, thereby obtaining a plurality of high-density communities in the user similarity relationship network.

5. The method according to claim 1, characterized in that The determining the ranking weights of users in the high-density community according to the behavioral structure characteristics of the high-density community specifically includes: Calculating a preset collusion index according to the behavioral structural characteristics of the high-density community to obtain collusion index data of the high-density community; The ranking weight of each user in the high-density community is calculated according to the collusion indicator data.

6. The method according to claim 5, characterized in that The step of calculating a preset collusion index according to the behavioral structural characteristics of the high-density community to obtain the collusion index data of the high-density community specifically includes: According to the behavior structure characteristics of the high-density community, a target ranking object set in the target high-density community and ranking data of the target ranking objects combined by users in the target high-density community are obtained, wherein the target high-density community is any one of all high-density communities, and the target ranking object set is a set of objects ranked by two or more users in the target high-density community; Obtaining ranking data of the target ranking object set from other communities except the target high-density community; Calculating the deviation of the ranking of the target high-density community based on the ranking data of the target ranking object set in the target high-density community and the ranking data of the target ranking object set in other communities to obtain the ranking deviation of the target high-density community; Obtaining the number of users who rank a target ranking object in the target high-density community based on the behavioral structure characteristics of the high-density community, where the target ranking object is any one of the target ranking object set; Obtaining the total number of users who have ranked the target ranking object; Calculate a ratio based on the number of users who rank the target ranking object in the target high-density community and the total number of users to obtain a user ratio of the target high-density community; Determining the community size of the target high-density community according to the behavioral structural characteristics of the high-density community; The ranking deviation, user ratio and community size of the target high-density community constitute the collusion indicator data of the target high-density community.

7. The method according to claim 6, characterized in that The ranking weight of each user in the high-density community is calculated by the following formula: Where W(g) is the ranking weight of the target high-density community, RD(g) is the ranking deviation of the target high-density community, UR(g) is the user ratio of the target high-density community, GS(g) is the community size of the target high-density community, and U g is the number of users in the target high-density community.

8. A ranking aggregation device, characterized in that: The device comprises: an information acquisition unit, a network decomposition unit and the ranking aggregation unit; The information acquisition unit is used to acquire ranking information, wherein the ranking information includes a ranking result of each user for a preset object; The network decomposition unit is used to construct a user similarity relationship network based on the ranking information, and decompose the user similarity relationship network into a plurality of high-density communities according to a preset community detection algorithm; The ranking aggregation unit is used to obtain the behavior structure characteristics of the high-density community based on the ranking information, and determine the ranking weights of users in the high-density community according to the behavior structure characteristics of the high-density community; The ranking aggregation result of the object is calculated based on a preset ranking aggregation method using the ranking weight and the ranking information.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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