CN108600961A - Preparation method and device, equipment, the storage medium of user's similarity - Google Patents
Preparation method and device, equipment, the storage medium of user's similarity Download PDFInfo
- Publication number
- CN108600961A CN108600961A CN201810244938.6A CN201810244938A CN108600961A CN 108600961 A CN108600961 A CN 108600961A CN 201810244938 A CN201810244938 A CN 201810244938A CN 108600961 A CN108600961 A CN 108600961A
- Authority
- CN
- China
- Prior art keywords
- user
- similarity
- obtaining
- movement track
- relationship
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000002360 preparation method Methods 0.000 title abstract 3
- 238000000034 method Methods 0.000 claims description 35
- 238000004590 computer program Methods 0.000 claims description 18
- 238000004364 calculation method Methods 0.000 claims description 9
- 230000015654 memory Effects 0.000 claims description 9
- 238000004891 communication Methods 0.000 description 12
- 238000010586 diagram Methods 0.000 description 4
- 230000009191 jumping Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 231100001263 laboratory chemical safety summary Toxicity 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W8/00—Network data management
- H04W8/02—Processing of mobility data, e.g. registration information at HLR [Home Location Register] or VLR [Visitor Location Register]; Transfer of mobility data, e.g. between HLR, VLR or external networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W8/00—Network data management
- H04W8/18—Processing of user or subscriber data, e.g. subscribed services, user preferences or user profiles; Transfer of user or subscriber data
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Business, Economics & Management (AREA)
- Databases & Information Systems (AREA)
- General Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Computing Systems (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a kind of preparation method of user's similarity and device, equipment, storage mediums.The preparation method of user's similarity includes：First user's motion track and second user motion track are obtained, and according to the first user motion track and the second user motion track, obtains motion track similarity；The first customer relationship circle and second user relation loop are obtained, and according to the first customer relationship circle and the second user relation loop, obtains relation loop similarity；According to the motion track similarity and the relation loop similarity, user's similarity between the first user and second user is obtained.Using the present invention, the accuracy of user's similarity of acquisition can be improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for obtaining user similarity.
Background
In the operation process of a telecommunication operator, when a communication number used by a certain user changes, related records of new and old numbers of the user often need to be merged, and how to identify whether the two numbers belong to the same user, that is, how to judge whether a certain new network-accessing user and a certain old network-accessing user are the same user becomes a core technical problem in the operation process of the telecommunication operator.
In the prior art, it is generally determined whether two users are the same user by calculating the similarity between the two users. Specifically, when a new network-accessing user is detected, automatically calculating user similarity between all old network-accessing users in the communication network and the new network-accessing user, and when the user similarity which is larger than a preset threshold value is detected to exist in the calculated user similarity, considering that the old network-accessing user similar to the new network-accessing user exists in the communication network, so that the new network-accessing user is judged to be a re-network-accessing user; and then, judging that the old network access user with the maximum user similarity and the new network access user are the same user, and merging the corresponding related records of the old network access user and the new network access user. The existing method for calculating the user similarity between the new network access user and the old network access user is generally obtained by calculating the similarity of user communication records, and the user similarity is obtained according to a single basis and has low accuracy.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a storage medium for obtaining user similarity, which can improve the accuracy of the obtained user similarity.
The method for obtaining the user similarity provided by the embodiment of the invention specifically comprises the following steps:
obtaining a first user movement track and a second user movement track, and obtaining movement track similarity according to the first user movement track and the second user movement track;
obtaining a first user relationship circle and a second user relationship circle, and obtaining relationship circle similarity according to the first user relationship circle and the second user relationship circle;
and obtaining the user similarity between the first user and the second user according to the moving track similarity and the relation circle similarity.
Further, the obtaining a first user movement track and a second user movement track, and obtaining a movement track similarity according to the first user movement track and the second user movement track specifically includes:
obtaining the first user movement track and the second user movement track; the first user movement track comprises at least one piece of first time information; the second user movement track comprises at least one piece of second time information;
obtaining time similarity according to each piece of first time information and each piece of second time information;
and obtaining the similarity of the movement tracks according to the movement tracks of the first user, the movement tracks of the second user and the time similarity.
Further, the obtaining a first user relationship circle and a second user relationship circle, and obtaining a relationship circle similarity according to the first user relationship circle and the second user relationship circle specifically include:
obtaining the first user relationship circle and the second user relationship circle;
obtaining a relation circle difference certainty degree according to the first user relation circle and the second user relation circle;
and obtaining the similarity of the relationship circle according to the difference certainty degrees of the first user relationship circle, the second user relationship circle and the relationship circle.
Further, the obtaining of the user similarity between the first user and the second user according to the moving track similarity and the relationship circle similarity specifically includes:
and carrying out weighted summation on the similarity of the moving track and the similarity of the relationship circle to obtain the user similarity.
Further, the performing weighted summation on the moving track similarity and the relationship circle similarity to obtain the user similarity specifically includes:
obtaining a preset similarity weight gamma;
according to the similarity sim of the movement trackMT(u, v) the relationship circle similarity simRC(u, v), the similarity weight γ and a preset user similarity calculation model sim (u, v) ═ γ × simMT(u,v)+(1-γ)*simRC(u, v), and calculating to obtain the user similarity sim (u, v).
Further, γ is 0.6.
Further, after obtaining the user similarity between the first user and the second user according to the movement trajectory similarity and the relationship circle similarity, the method further includes:
judging whether the user similarity is greater than a preset threshold value or not;
if yes, the first user and the second user are judged to be the same user;
if not, determining that the first user and the second user are not the same user.
Correspondingly, an embodiment of the present invention further provides an apparatus for obtaining user similarity, which specifically includes:
the device comprises a movement track similarity obtaining module, a first user movement track and a second user movement track, wherein the movement track similarity obtaining module is used for obtaining a first user movement track and a second user movement track and obtaining movement track similarity according to the first user movement track and the second user movement track;
the relationship ring similarity obtaining module is used for obtaining a first user relationship ring and a second user relationship ring and obtaining relationship ring similarity according to the first user relationship ring and the second user relationship ring; and the number of the first and second groups,
and the user similarity obtaining module is used for obtaining the user similarity between the first user and the second user according to the moving track similarity and the relationship circle similarity.
The embodiment of the present invention further provides an apparatus, which specifically includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor implements the method for obtaining user similarity as described above when executing the computer program.
The embodiment of the present invention further provides a computer-readable storage medium, which specifically includes a stored computer program, where when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the method for obtaining user similarity described above.
The embodiment of the invention has the following beneficial effects:
according to the method, the device, the equipment and the storage medium for obtaining the user similarity, the user similarity between the first user and the second user is obtained by comprehensively considering the similarity between the first user movement track and the second user movement track and the similarity between the first user relationship circle and the second user relationship circle, so that the user similarity is obtained according to diversification, the obtained user similarity is high in conformity with the actual situation, and the accuracy of the obtained user similarity can be improved.
Drawings
Fig. 1 is a schematic flow chart of a preferred embodiment of a method for obtaining user similarity according to the present invention;
fig. 2 is a schematic structural diagram of a preferred embodiment of the apparatus for obtaining user similarity provided by the present invention;
fig. 3 is a schematic structural diagram of a preferred embodiment of the apparatus provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a schematic flowchart of a preferred embodiment of the method for obtaining user similarity provided by the present invention includes steps S11 to S13, which are specifically as follows:
s11: the method comprises the steps of obtaining a first user movement track and a second user movement track, and obtaining movement track similarity according to the first user movement track and the second user movement track.
S12: and obtaining a first user relationship circle and a second user relationship circle, and obtaining the relationship circle similarity according to the first user relationship circle and the second user relationship circle.
S13: and obtaining the user similarity between the first user and the second user according to the moving track similarity and the relation circle similarity.
In the embodiment, the user similarity between the first user and the second user is obtained by comprehensively considering the similarity between the first user movement track and the second user movement track and the similarity between the first user relationship circle and the second user relationship circle, so that the user similarity is obtained according to diversification, the obtained user similarity is high in conformity with a real situation, and the accuracy of the obtained user similarity can be improved.
In another preferred embodiment, the step S11 further includes steps S1101 to S1103, specifically as follows:
s1101: obtaining a first user movement track and a second user movement track; the first user movement track comprises at least one piece of first time information; the second user movement track comprises at least one piece of second time information.
It should be noted that the embodiment of the present invention is executed by a system. Wherein the system may be a system in a telecommunication operator server.
A telecommunication operator sets a plurality of base stations everywhere, and the system obtains the user movement track of the user by obtaining the communication data of the user. Specifically, the system monitors the first user in real time, acquires communication data such as WeChat, short message and QQ of the first user, analyzes the communication data to judge base stations which the first user passes through in the period of time, and records the base stations to obtain the movement track of the first user. In the same way, the movement track of the second user can be obtained.
Further, the first user movement track comprises at least one first track point; the second user moving track comprises at least one second track point; each piece of first time information and each piece of first track point have one-to-one correspondence; and each piece of second time information and each piece of second track point have one-to-one correspondence.
More preferably, the first time information is time information when the first user arrives at the corresponding first track point; and the second time information is the time information when the second user arrives at the corresponding second track point.
It should be noted that, in this embodiment, each time it is detected that the first user moves to a position near one base station, the base station is recorded as one track point, and the time information when the first user arrives at the base station is recorded, and the movement track of the first user can be obtained by recording the base stations that the first user passes through within the preset time period and the time information when the first user arrives at each base station. In the same way, the second user movement track can be obtained.
S1102: and obtaining time similarity according to each piece of first time information and each piece of second time information.
It should be noted that, according to each piece of first time information and each piece of second time information, the time similarity (i.e., the probability that the first user and the second user appear at the same geographic location in the same time period) of the first user and the second user is obtained, so as to obtain the time similarity.
Further, the step S1102 further includes a step S1102_1, which is as follows:
s1102_ 1: according to each first time information Ti(u) each of the second time information Tj(v) And a preset time similarity calculation modelCalculating to obtain the time similarity COL; wherein u represents the first user; v represents the second user; n (u) is the total number of first track points in the first user movement track; n (v) is the total number of second track points in the second user moving track; Δ T is a preset time precision; l isi(u) representing an ith first track point in the first user movement track; l isj(v) Representing a jth second track point in the second user movement track; delta (L)i(u),Lj(v) Is the coincidence degree of the ith first track point in the first user moving track and the jth second track point in the second user moving track.
Note that, more preferably, the value of Δ T is set to 1 hour. When the ith first track point in the first user moving track is superposed with the jth second track point in the second user moving track, delta (L)i(u),Lj(v) 1, otherwise, δ (L)i(u),Lj(v))＝0。
S1103: and obtaining the similarity of the movement tracks according to the movement tracks of the first user, the movement tracks of the second user and the time similarity.
It should be noted that, in this embodiment, the similarity between the first user movement track and the second user movement track is calculated by combining the time similarity, so as to obtain the movement track similarity between the first user and the second user.
Further, the step S1103 further includes steps S1103_1 to S1103_2, which are as follows:
s1103_ 1: calculating a model according to the first user movement track, the second user movement track and the preset track similarityCalculating to obtain initial track similarity DLCSS(ii) a Wherein LCSS (u, v) represents the longest common subsequence between the first user movement trajectory and the second user movement trajectory; len (a)uRepresenting the total number of first track points in the first user moving track; len (a)vAnd the total number of second track points in the second user moving track is represented.
It should be noted that, in the following description,wherein u isiRepresenting the ith first track point in the first user moving track; v. ofjRepresenting a jth second track point in the second user moving track; gamma is a preset similarity threshold; dist (u)i,vj) Is the Euclidean distance between the first user movement track and the second user movement track.
S1103_ 2: according to the initial track similarity DLCSSAnd obtaining the moving track similarity according to the time similarity.
Further, according to the initial track similarity DLCSSThe time similarity COL and a user similarity calculation model sim (u, v) ═ DLCSSAnd COL, calculating and obtaining the similarity sim (u, v) of the movement tracks.
According to the embodiment, the time similarity is introduced in the process of calculating the moving track similarity, so that the obtaining basis of the moving track similarity can be diversified, and the accuracy of the obtained moving track similarity is improved.
In another preferred embodiment, the step S12 further includes steps S1201 to S1203, specifically as follows:
s1201: a first user relationship circle and a second user relationship circle are obtained.
It should be noted that the embodiment of the present invention is executed by a system. Wherein the system may be a system in a telecommunication operator server.
Telecommunication operators set a plurality of base stations everywhere, and the system obtains the relationship circle of users by obtaining the communication data of the users and analyzing the communication data. Specifically, the system monitors the first user in real time, obtains communication data of the first user such as WeChat, short message and QQ, and obtains a user relationship circle formed by friends of the first user by analyzing the communication data. In the same way, a user relationship circle of the second user can be obtained.
Further, at least one first neighboring user of the first user is included in the first user relationship circle; at least one second neighboring user of the second user is included in the second user relationship circle.
It should be noted that the first neighboring user refers to other users in the first user relationship circle except the first user, that is, refers to friends of the first user. Similarly, the second adjacent user refers to another user except the second user in the second user relationship circle, that is, refers to a friend of the second user.
Further, the step S1201 further includes steps S1201_1 to S1201_3, which are as follows:
s1201_ 1: obtaining the first user relationship circle.
S1201_ 2: obtaining a third user relationship circle corresponding to each first adjacent user in the first user relationship circles; and each third user relationship circle comprises at least one third adjacent user of the corresponding first adjacent user.
S1201_ 3: and setting any one third adjacent user as the second user, and setting a user relationship circle corresponding to the second user as the second user relationship circle.
It should be noted that, if the first neighboring user is a friend of the first user, and the third neighboring user is a friend of the first neighboring user, the second user is a friend of the first user. In this embodiment, the similarity between the first user and the friend of the first user is determined by comparing the user relationship circle of the first user with the user relationship circle of the friend of the first user.
For example, if the first user relationship circle corresponding to the first user u includes the first neighboring users z and f, the third user relationship circle corresponding to the first neighboring user z and the third user relationship circle corresponding to the first neighboring user f are obtained respectively. Assuming that the third user relationship circle corresponding to the first adjacent user z includes third adjacent users H ', L', K ', and J', and the third user relationship circle corresponding to the first adjacent user f includes third adjacent users X ', M', Y ', and N', the third adjacent users H ', L', K ', J', X ', M', Y ', and N' are sequentially used as second users v, respectively, and a second user relationship circle corresponding to the second user v is obtained. And then, calculating the similarity between the first user relationship circle and the second user relationship circle, so as to obtain the user similarity between the first user u and the second user v.
S1202: and obtaining the relation circle difference certainty factor according to the first user relation circle and the second user relation circle.
The relationship circle difference certainty degree refers to a degree of certainty of difference between the first user relationship circle and the second user relationship circle.
Further, the step S1202 further includes steps S1202_1 to S1202_2, which are as follows:
s1202_ 1: obtaining a set of neighboring users I consisting of all of the first neighboring users and all of the second neighboring users.
S1202_ 2: determining a calculation model according to each first adjacent user, each second adjacent user and a preset differenceCalculating to obtain the relation circle difference certainty degree cer (u, v); wherein,u represents the first user; v represents the second user;ru,irepresenting a weight between the first user and an ith neighbor user in the set of neighbor users I; r isv,iRepresenting a weight between the second user and an ith neighbor user in the set of neighbor users I; rpRepresenting a set of weights greater than the weight mean; rnRepresenting a set of weights that is less than the mean of the weights; the weight mean is an average of the weights between the first user and each neighboring user in the set of neighboring users I.
It should be noted that, in the present embodiment, the weight between two users may be expressed by how close the relationship between the two users is.Is selected from the above-mentioned adjacent usersA set consisting of adjacent users with opposite relationship between a user u and a second user v;the first user u and the second user v are connected with the first user u and the second user v through the first user v; i isuThe set of the adjacent users related to the first user u in the adjacent user set I is shown. Therefore, when the difference certainty degree of the relationship circle is larger, it indicates that the common friends of the first user and the second user are more, and the first user is more similar to the second user. When in useAnd IuWhen the relation circle difference certainty degree is consistent, the relation circle difference certainty degree reaches the maximum value, namely cer (u, v) is equal to 1.
S1203: and obtaining the similarity of the relationship circle according to the difference certainty degrees of the first user relationship circle, the second user relationship circle and the relationship circle.
It should be noted that, in this embodiment, the similarity between the first user relationship circle and the second user relationship circle is calculated by combining the relationship circle difference certainty degree, so as to obtain the relationship circle similarity between the first user and the second user.
Further, the step S1203 further includes steps S1203_1 to S1203_2, which are as follows:
s1203_ 1: calculating a model according to the similarity of each first adjacent user, each second adjacent user and a preset relationship circleCalculating to obtain initial relationship ring similarity RC; wherein,u represents the first user; v represents the second user;ru,irepresenting a weight between the first user and an ith neighbor user in the set of neighbor users I; r isv,iRepresenting a weight between the second user and an ith neighbor user in the set of neighbor users I; rpRepresenting a set of weights greater than the weight mean; rnRepresenting a set of weights that is less than the mean of the weights; the weight mean is an average of the weights between the first user and each neighboring user in the set of neighboring users I.
It should be noted that, in the following description,the first user u and the second user v are in a same relationship with each other;the first user u and the second user v are connected with the first user u and the second user v through the first user v; i isuThe set of the adjacent users related to the first user u in the adjacent user set I is shown. Therefore, when the difference certainty degree of the relationship circle is larger, it indicates that the common friends of the first user and the second user are more, and the first user is more similar to the second user. When in useAnd IuWhen the relation circle difference certainty degree is consistent, the relation circle difference certainty degree reaches the maximum value, namely cer (u, v) is equal to 1.
S1203_ 2: and obtaining the similarity of the relationship ring according to the initial relationship ring similarity RC and the difference certainty of the relationship ring.
Further, the relationship ring similarity sim (uv) is calculated and obtained according to the initial relationship ring similarity RC, the relationship ring difference certainty cer (u, v) and a preset user similarity calculation model sim (u, v) ═ RC (u, v).
In the embodiment, the relationship ring difference certainty degree is introduced in the process of calculating the relationship ring similarity degree, so that the relationship ring similarity degree can be obtained according to diversification, and the accuracy of the obtained relationship ring similarity degree is improved.
In another preferred embodiment, the step S13 further includes a step S1301, which is as follows:
s1301: and carrying out weighted summation on the similarity of the moving track and the similarity of the relationship circle to obtain the user similarity.
Further, the step S1301 further includes steps S1301_1 to S1301_2, which are as follows:
s1301_ 1: a preset similarity weight gamma is obtained.
More preferably, γ is 0.6.
S1301_ 2: according to the similarity sim of the movement trackMT(u, v) the relationship circle similarity simRC(u, v), the similarity weight γ and a preset user similarity calculation model sim (u, v) ═ γ × simMT(u,v)+(1-γ)*simRC(u, v), and calculating to obtain the user similarity sim (u, v).
In another preferred embodiment, after the step S13, the method further includes steps S14 to S16, which are as follows:
s14: and judging whether the user similarity is greater than a preset threshold value, if so, jumping to S15, and if not, jumping to S16.
S15: and judging that the first user and the second user are the same user.
S16: and determining that the first user and the second user are not the same user.
It should be noted that, in this embodiment, the similarity between the first user and the second user is determined by calculating the similarity between the first user movement track and the second user movement track and the similarity between the first user relationship circle and the second user relationship circle, so that it can be determined whether the newly accessed user is a re-accessed user in the telecommunication operation process. For example, assume that the number used before a subscriber a is 159 x and is deactivated after 2 months of use, and that a subscriber b has opened a new number 186 x after 2 months in the same telecommunications carrier. At this time, the telecommunication operator analyzes the communication data of the user A and the user B to respectively obtain a user movement track and a user relationship circle of the user A and a user movement track and a user relationship circle of the user B, and then, calculates the similarity between the user movement track of the user A and the user movement track of the user B and the similarity between the user relationship circle of the user A and the user relationship circle of the user B to obtain the user similarity between the user A and the user B, if the user similarity is greater than a certain preset threshold value, the user A and the user B can be considered as the same person, and the user B is judged as a re-network-accessing user; if the similarity of the user is smaller than or equal to a certain preset threshold value, the user A and the user B are considered to be not the same person, and the user B is judged to be a new network access user. In this embodiment, since the accuracy of the user similarity obtained according to the above embodiment is high, the accuracy of the determination of the re-accessing user can be improved.
It should be further noted that the above step numbers are only used for indicating different steps, and do not limit the execution sequence between the steps.
According to the method for obtaining the user similarity provided by the embodiment of the invention, the user similarity between the first user and the second user is obtained by comprehensively considering the similarity between the first user movement track and the second user movement track and the similarity between the first user relationship circle and the second user relationship circle, so that the user similarity is obtained according to diversification, the obtained user similarity is high in conformity with the actual situation, and the accuracy of the obtained user similarity can be improved.
Correspondingly, the invention also provides a device for obtaining the user similarity, which can realize all the processes of the method for obtaining the user similarity in the embodiment.
As shown in fig. 2, a schematic structural diagram of a preferred embodiment of the apparatus for obtaining user similarity provided by the present invention is specifically as follows:
a movement track similarity obtaining module 21, configured to obtain a first user movement track and a second user movement track, and obtain a movement track similarity according to the first user movement track and the second user movement track;
a relationship ring similarity obtaining module 22, configured to obtain a first user relationship ring and a second user relationship ring, and obtain a relationship ring similarity according to the first user relationship ring and the second user relationship ring; and the number of the first and second groups,
and a user similarity obtaining module 23, configured to obtain a user similarity between the first user and the second user according to the moving trajectory similarity and the relationship circle similarity.
Further, the module for obtaining the similarity of the movement tracks specifically includes:
a user movement track obtaining unit, configured to obtain the first user movement track and the second user movement track; the first user movement track comprises at least one piece of first time information; the second user movement track comprises at least one piece of second time information;
a time similarity obtaining unit, configured to obtain a time similarity according to each piece of the first time information and each piece of the second time information; and the number of the first and second groups,
and the movement track similarity obtaining unit is used for obtaining the movement track similarity according to the first user movement track, the second user movement track and the time similarity.
Further, the relationship ring similarity obtaining module specifically includes:
a user relationship circle obtaining unit, configured to obtain the first user relationship circle and the second user relationship circle;
a relationship circle difference certainty degree obtaining unit, configured to obtain a relationship circle difference certainty degree according to the first user relationship circle and the second user relationship circle; and the number of the first and second groups,
and the relationship circle similarity obtaining unit is used for obtaining the relationship circle similarity according to the first user relationship circle, the second user relationship circle and the relationship circle difference certainty factor.
Further, the user similarity obtaining module specifically includes:
and the user similarity obtaining unit is used for carrying out weighted summation on the moving track similarity and the relationship ring similarity to obtain the user similarity.
Further, the user similarity obtaining unit specifically includes:
a similarity weight obtaining subunit, configured to obtain a preset similarity weight γ; and the number of the first and second groups,
a user similarity calculation operator unit for calculating the similarity sim of the movement trackMT(u, v) the relationship circle similarity simRC(u, v), the similarity weight γ and a preset user similarity calculation model sim (u, v) ═ γ × simMT(u,v)+(1-γ)*simRC(u, v), and calculating to obtain the user similarity sim (u, v).
Further, γ is 0.6.
Further, the apparatus for obtaining user similarity further includes:
the user similarity judging module is used for judging whether the user similarity is greater than a preset threshold value or not; and the number of the first and second groups,
the first processing module is used for judging that the first user and the second user are the same user when the user similarity is judged to be larger than a preset threshold value; or,
and the second processing module is used for judging that the first user and the second user are not the same user when judging that the similarity of the users is not larger than a preset threshold value.
According to the device for obtaining the user similarity provided by the embodiment of the invention, the user similarity between the first user and the second user is obtained by comprehensively considering the similarity between the first user movement track and the second user movement track and the similarity between the first user relationship circle and the second user relationship circle, so that the user similarity is obtained according to diversification, the obtained user similarity is high in conformity with the actual situation, and the accuracy of the obtained user similarity can be improved.
The invention also provides equipment.
As shown in fig. 3, a schematic structural diagram of a preferred embodiment of the apparatus provided by the present invention includes a processor 31, a memory 32, and a computer program stored in the memory 32 and configured to be executed by the processor 31, where the processor 31 implements the method for obtaining the user similarity according to any one of the above embodiments when executing the computer program.
It should be noted that fig. 3 only illustrates an example in which one memory and one processor in the apparatus are connected, and in some specific embodiments, the apparatus may further include a plurality of memories and/or a plurality of processors, and the specific number and the connection mode thereof may be set and adapted according to actual needs.
According to the device provided by the embodiment of the invention, the user similarity between the first user and the second user is obtained by comprehensively considering the similarity between the first user movement track and the second user movement track and the similarity between the first user relationship circle and the second user relationship circle, so that the user similarity is obtained according to diversification, the obtained user similarity is high in conformity with the actual situation, and the accuracy of the obtained user similarity can be improved.
The present invention further provides a computer-readable storage medium, which specifically includes a stored computer program, where when the computer program runs, a device where the computer-readable storage medium is located is controlled to execute the method for obtaining user similarity according to any of the above embodiments.
It should be noted that, all or part of the flow in the method according to the above embodiments of the present invention may also be implemented by a computer program instructing related hardware, where the computer program may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above embodiments of the method may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be further noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The computer-readable storage medium provided by the embodiment of the invention obtains the user similarity between the first user and the second user by comprehensively considering the similarity between the first user movement track and the second user movement track and the similarity between the first user relationship circle and the second user relationship circle, so that the user similarity is obtained according to diversification, the obtained user similarity is high in conformity with the actual situation, and the accuracy of the obtained user similarity can be improved.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A method for obtaining user similarity is characterized by comprising the following steps:
obtaining a first user movement track and a second user movement track, and obtaining movement track similarity according to the first user movement track and the second user movement track;
obtaining a first user relationship circle and a second user relationship circle, and obtaining relationship circle similarity according to the first user relationship circle and the second user relationship circle;
and obtaining the user similarity between the first user and the second user according to the moving track similarity and the relation circle similarity.
2. The method for obtaining user similarity according to claim 1, wherein the obtaining a first user movement track and a second user movement track, and obtaining the movement track similarity according to the first user movement track and the second user movement track specifically includes:
obtaining the first user movement track and the second user movement track; the first user movement track comprises at least one piece of first time information; the second user movement track comprises at least one piece of second time information;
obtaining time similarity according to each piece of first time information and each piece of second time information;
and obtaining the similarity of the movement tracks according to the movement tracks of the first user, the movement tracks of the second user and the time similarity.
3. The method for obtaining user similarity according to claim 1, wherein the obtaining a first user relationship circle and a second user relationship circle, and obtaining the relationship circle similarity according to the first user relationship circle and the second user relationship circle specifically includes:
obtaining the first user relationship circle and the second user relationship circle;
obtaining a relation circle difference certainty degree according to the first user relation circle and the second user relation circle;
and obtaining the similarity of the relationship circle according to the difference certainty degrees of the first user relationship circle, the second user relationship circle and the relationship circle.
4. The method for obtaining user similarity according to claim 1, wherein the obtaining of the user similarity between the first user and the second user according to the movement trajectory similarity and the relationship circle similarity specifically includes:
and carrying out weighted summation on the similarity of the moving track and the similarity of the relationship circle to obtain the user similarity.
5. The method for obtaining user similarity according to claim 4, wherein the weighting and summing the movement trajectory similarity and the relationship circle similarity to obtain the user similarity specifically includes:
obtaining a preset similarity weight gamma;
according to the similarity sim of the movement trackMT(u, v) the relationship circle similarity simRC(u, v), the similarity weight γ and a preset user similarity calculation model sim (u, v) ═ γ × simMT(u,v)+(1-γ)*simRC(u, v), and calculating to obtain the user similarity sim (u, v).
6. The method according to claim 5, wherein γ is 0.6.
7. The method for obtaining user similarity according to claim 1, wherein after obtaining the user similarity between the first user and the second user according to the movement trajectory similarity and the relationship circle similarity, the method further comprises:
judging whether the user similarity is greater than a preset threshold value or not;
if yes, the first user and the second user are judged to be the same user;
if not, determining that the first user and the second user are not the same user.
8. An apparatus for obtaining user similarity, comprising:
the device comprises a movement track similarity obtaining module, a first user movement track and a second user movement track, wherein the movement track similarity obtaining module is used for obtaining a first user movement track and a second user movement track and obtaining movement track similarity according to the first user movement track and the second user movement track;
the relationship ring similarity obtaining module is used for obtaining a first user relationship ring and a second user relationship ring and obtaining relationship ring similarity according to the first user relationship ring and the second user relationship ring; and the number of the first and second groups,
and the user similarity obtaining module is used for obtaining the user similarity between the first user and the second user according to the moving track similarity and the relationship circle similarity.
9. An apparatus comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the method of obtaining user similarity according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the method for obtaining user similarity according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810244938.6A CN108600961A (en) | 2018-03-23 | 2018-03-23 | Preparation method and device, equipment, the storage medium of user's similarity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810244938.6A CN108600961A (en) | 2018-03-23 | 2018-03-23 | Preparation method and device, equipment, the storage medium of user's similarity |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108600961A true CN108600961A (en) | 2018-09-28 |
Family
ID=63627316
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810244938.6A Pending CN108600961A (en) | 2018-03-23 | 2018-03-23 | Preparation method and device, equipment, the storage medium of user's similarity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108600961A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109168138A (en) * | 2018-11-26 | 2019-01-08 | 中国联合网络通信集团有限公司 | The recognition methods for the number of changing, device and equipment in net |
| CN109769244A (en) * | 2019-01-08 | 2019-05-17 | 中国联合网络通信集团有限公司 | Recognition methods, device, equipment and the readable storage medium storing program for executing of abnormal phonecard |
| CN111694875A (en) * | 2019-03-14 | 2020-09-22 | 百度在线网络技术（北京）有限公司 | Method and apparatus for outputting information |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104317900A (en) * | 2014-10-24 | 2015-01-28 | 重庆邮电大学 | Multiattribute collaborative filtering recommendation method oriented to social network |
| CN105069145A (en) * | 2015-08-20 | 2015-11-18 | 中国科学院计算技术研究所 | Method and system for determining user relationship strength in social network |
| WO2016064167A1 (en) * | 2014-10-20 | 2016-04-28 | 남기원 | Personal relationship network search system and method therefor |
| CN106570764A (en) * | 2016-11-09 | 2017-04-19 | 广州杰赛科技股份有限公司 | User relationship predicting method and device |
| CN106971345A (en) * | 2016-01-08 | 2017-07-21 | 车海莺 | A kind of location recommendation method based on position social networks |
-
2018
- 2018-03-23 CN CN201810244938.6A patent/CN108600961A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016064167A1 (en) * | 2014-10-20 | 2016-04-28 | 남기원 | Personal relationship network search system and method therefor |
| CN104317900A (en) * | 2014-10-24 | 2015-01-28 | 重庆邮电大学 | Multiattribute collaborative filtering recommendation method oriented to social network |
| CN105069145A (en) * | 2015-08-20 | 2015-11-18 | 中国科学院计算技术研究所 | Method and system for determining user relationship strength in social network |
| CN106971345A (en) * | 2016-01-08 | 2017-07-21 | 车海莺 | A kind of location recommendation method based on position social networks |
| CN106570764A (en) * | 2016-11-09 | 2017-04-19 | 广州杰赛科技股份有限公司 | User relationship predicting method and device |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109168138A (en) * | 2018-11-26 | 2019-01-08 | 中国联合网络通信集团有限公司 | The recognition methods for the number of changing, device and equipment in net |
| CN109769244A (en) * | 2019-01-08 | 2019-05-17 | 中国联合网络通信集团有限公司 | Recognition methods, device, equipment and the readable storage medium storing program for executing of abnormal phonecard |
| CN109769244B (en) * | 2019-01-08 | 2021-10-15 | 中国联合网络通信集团有限公司 | Abnormal telephone card identification method, device, equipment and readable storage medium |
| CN111694875A (en) * | 2019-03-14 | 2020-09-22 | 百度在线网络技术（北京）有限公司 | Method and apparatus for outputting information |
| CN111694875B (en) * | 2019-03-14 | 2023-04-25 | 百度在线网络技术（北京）有限公司 | Method and device for outputting information |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108764951B (en) | User similarity obtaining method and device, equipment and storage medium | |
| CN106682906B (en) | Risk identification and service processing method and equipment | |
| CN112950231A (en) | XGboost algorithm-based abnormal user identification method, device and computer-readable storage medium | |
| CN107423883B (en) | Risk identification method and device for to-be-processed service and electronic equipment | |
| CN109063920A (en) | A kind of transaction risk recognition methods, device and computer equipment | |
| CN107610695A (en) | Driver's voice wakes up the dynamic adjusting method of instruction word weight | |
| CN108600961A (en) | Preparation method and device, equipment, the storage medium of user's similarity | |
| CN107240029B (en) | Data processing method and device | |
| CN106776925B (en) | Method, server and system for predicting gender of mobile terminal user | |
| CN111126623B (en) | Model updating method, device and equipment | |
| CN111127062A (en) | Group fraud identification method and device based on space search algorithm | |
| CN111611390B (en) | Data processing method and device | |
| US20240104679A1 (en) | Identity Management | |
| CN108900975A (en) | The detection method and device of user's motion track, equipment, storage medium | |
| CN106485521A (en) | User credit degree appraisal procedure and device | |
| CN108509560B (en) | User similarity obtaining method and device, equipment and storage medium | |
| CN110781929B (en) | Credit prediction model training method, prediction method and device, medium and equipment | |
| CN107241219B (en) | Users to trust degree prediction technique and device | |
| WO2023275603A1 (en) | Methods, apparatuses, devices and storage media for training object detection network and for detecting object | |
| CN108683995B (en) | Method and device for obtaining user movement track | |
| CN113098974A (en) | Method for determining equipment number, server and storage medium | |
| CN109362027B (en) | Positioning method, device, equipment and storage medium | |
| CN108830103B (en) | Method and device for automatically generating user privacy policy and handheld device | |
| CN108337644B (en) | Data processing method and device | |
| CN113115301B (en) | Determination method, device and readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180928 |
|
| RJ01 | Rejection of invention patent application after publication |