CN107203784B - Similarity calculation method, terminal and computer readable storage medium - Google Patents

Similarity calculation method, terminal and computer readable storage medium Download PDF

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CN107203784B
CN107203784B CN201710372676.7A CN201710372676A CN107203784B CN 107203784 B CN107203784 B CN 107203784B CN 201710372676 A CN201710372676 A CN 201710372676A CN 107203784 B CN107203784 B CN 107203784B
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similarity matrix
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items
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张琪
郭红艳
郭凤阁
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Nanjing Qinhuai Ziyun Chuangyi Enterprise Service Co Ltd
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Abstract

The invention discloses a similarity calculation method, a terminal and a computer readable storage medium, relating to the technical field of information processing, wherein the similarity calculation method comprises the following steps: acquiring a similarity matrix according to the article similarity label; extracting elements which are not in the same row and the same column in the similarity matrix to form irrelevant items, and calculating matching values of all the irrelevant items; determining the similarity of the articles according to the independent item matching value and the comprehensive weight of the similarity matrix so as to reduce the time complexity of the calculation of the similarity of the articles; and the irrelevant item matching value is the average value of all irrelevant items of the similarity matrix and represents the key similar information of the similarity matrix. The method calculates the matching value of the irrelevant items by searching all the irrelevant items of the similarity matrix, and determines the similarity of the articles according to the matching value of the irrelevant items and the comprehensive weight of the similarity matrix. The method can effectively simplify the calculation process of the similarity of the articles and the time complexity of the algorithm, and save the calculation resources of the system.

Description

Similarity calculation method, terminal and computer readable storage medium
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to a similarity calculation method, a terminal, and a computer-readable storage medium.
Background
Currently, similarity calculation is involved in many fields, such as the internet industry, and similarity analysis is performed based on various existing similarity calculation methods.
For example, in the personalized recommendation field, the server collects and stores a large amount of data of the user and the operation object thereof, and needs to recommend a relevant operation object which may be interested by the user to the user according to the operation performed by the user, and needs to calculate the similarity between the operation object to be recommended and the operation object operated by the user in the recommendation process, so as to recommend the operation object with high similarity to the user. Here, the similarity calculation methods generally include three types, that is, a similarity calculation method based on a known condition of the object attribute vector, a similarity calculation method based on an association relationship, and a similarity calculation method based on statistics.
The similarity calculation method is based on the similarity calculation method under the condition that the object attribute vector is known. According to the similarity calculation method, the distance of the object attribute vector under a certain meaning is calculated according to the known condition of the object attribute, and the distance is used as the similarity calculation between the object and the object. For example: euclidean distance, mahalanobis distance, michelson distance, hamming distance, jaccard coefficient, pearson correlation coefficient, cosine similarity, etc.
For example: and calculating cosine similarity between two article labels by using the article label vectors, expanding the values according to a square matrix to form a similarity matrix, and when the label numbers are inconsistent, using a zero-complement matrix as the square matrix, and at the moment, adding the matching values by using a weighted bipartite graph maximum matching algorithm to obtain the article similarity.
The adoption of the exhaustion method and the weighted bipartite graph maximum matching algorithm in the similarity calculation mode leads to a relatively complex calculation process, and the calculation time complexity is O (nm), wherein n is the number of vertexes in the bipartite graph, and m is the number of edges, so that the time cost is very high when the data volume is large.
Disclosure of Invention
The invention mainly aims to provide a similarity calculation method, a terminal and a computer readable storage medium, and aims to solve the problems that in the prior art, the object similarity calculation process is complex, the calculation amount is large, the calculation time complexity is high, and a large amount of system calculation resources are occupied.
In order to achieve the above object, an aspect of the present invention provides a similarity calculation method, including:
acquiring a similarity matrix according to the article similarity label;
extracting elements which are not in the same row and the same column in the similarity matrix to form irrelevant items, and calculating matching values of all the irrelevant items;
determining the similarity of the articles according to the independent item matching value and the comprehensive weight of the similarity matrix so as to reduce the time complexity of the calculation of the similarity of the articles;
and the irrelevant item matching value is the average value of all irrelevant items of the similarity matrix and represents the key similar information of the similarity matrix.
Further, the obtaining of the similarity matrix according to the item similarity label includes:
converting the similarity label into a similarity vector;
and multiplying the similarity vector point to obtain a similarity matrix:
Figure BDA0001303169420000021
wherein n is the number of the article similarity labels.
Further, the matching values of the irrelevant items of the similarity matrix are:
Figure BDA0001303169420000022
wherein q isijFor the ith row and jth column element of the similarity matrix,
Figure BDA0001303169420000023
the number of entries repeated for the similarity matrix,
Figure BDA0001303169420000024
is the number of irrelevant items.
Further, the article similarity is: s is R/n;
wherein R is an unrelated item matching value.
Another aspect of the present invention also provides a similarity calculation terminal, including: a memory, a processor, and a similarity calculation program stored on the memory and executable on the processor, the similarity calculation program when executed by the processor implementing the steps of:
acquiring a similarity matrix according to the article similarity label;
extracting elements which are not in the same row and the same column in the similarity matrix to form irrelevant items, and calculating matching values of all the irrelevant items;
determining the similarity of the articles according to the independent item matching value and the comprehensive weight of the similarity matrix so as to reduce the time complexity of the calculation of the similarity of the articles;
and the irrelevant item matching value is the average value of all irrelevant items of the similarity matrix and represents the key similar information of the similarity matrix.
Further, in the step of obtaining the similarity matrix according to the item similarity label, the processor executes the similarity calculation program to implement the following steps:
converting the similarity label into a similarity vector;
and multiplying the similarity vector point to obtain a similarity matrix:
Figure BDA0001303169420000031
wherein n is the number of the article similarity labels.
Further, in the step of extracting elements in the similarity matrix that are not in the same row and the same column to form independent items, and calculating matching values of all the independent items, the processor executes the similarity calculation program to implement the following steps:
by passing
Figure BDA0001303169420000032
Calculating all the irrelevant item matching values;
wherein q isijFor the ith row and jth column element of the similarity matrix,
Figure BDA0001303169420000033
the number of entries repeated for the similarity matrix,
Figure BDA0001303169420000034
is the number of irrelevant items.
Yet another aspect of the present invention provides a computer readable storage medium storing one or more programs, the one or more programs being executable by one or more processors to perform the steps of:
acquiring a similarity matrix according to the article similarity label;
extracting elements which are not in the same row and the same column in the similarity matrix to form irrelevant items, and calculating matching values of all the irrelevant items;
determining the similarity of the articles according to the independent item matching value and the comprehensive weight of the similarity matrix so as to reduce the time complexity of the calculation of the similarity of the articles;
and the irrelevant item matching value is the average value of all irrelevant items of the similarity matrix and represents the key similar information of the similarity matrix.
Further, in the step of obtaining a similarity matrix according to the item similarity label, the one or more programs may be further executable by the one or more processors to implement the steps of:
converting the similarity label into a similarity vector;
and multiplying the similarity vector point to obtain a similarity matrix:
Figure BDA0001303169420000041
wherein n is the number of the article similarity labels.
Further, in the extracting elements of the similarity matrix that are not in the same row and the same column to form independent items, and in the calculating matching values of all the independent items, the one or more programs may be further executable by the one or more processors to implement the steps of:
by passing
Figure BDA0001303169420000042
Calculating all the irrelevant item matching values;
wherein q isijIs the similarity matrix ofThe i row and the j column element,
Figure BDA0001303169420000043
the number of entries repeated for the similarity matrix,
Figure BDA0001303169420000044
is the number of irrelevant items.
According to the similarity calculation method, the terminal and the computer readable storage medium, all irrelevant items of the similarity matrix are searched, the matching value of the irrelevant items is calculated, and the similarity of the articles is determined according to the matching value of the irrelevant items and the comprehensive weight of the similarity matrix. Compared with the existing weighted bipartite graph maximum matching algorithm, the method is directly realized by addition in combination with the characteristics of similarity matrix independent item matching when extracting independent item matching without using an exhaustion method, thereby simplifying the calculation process of the similarity of the articles, reducing the time complexity of the algorithm from O (nm) to O (1), having good calculation performance under a distributed calculation platform, and effectively saving the calculation resources of the system.
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Fig. 1 is a schematic hardware structure diagram of a mobile terminal for implementing various embodiments of the present invention;
fig. 2 is a diagram of a communication network system architecture according to an embodiment of the present invention;
fig. 3 is a flowchart of a similarity calculation method according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a user inputting a similarity label via a similarity calculation terminal according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating the similarity calculation terminal outputting the similarity according to the embodiment of the present invention;
FIG. 6 is a schematic diagram of a structure of a similarity calculation terminal according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
The terminal may be implemented in various forms. For example, the terminal described in the present invention may include mobile terminals such as a mobile phone, a tablet computer, a mobile phone, a smart phone, a notebook computer, a palmtop computer, a Digital broadcast receiver, a Personal Digital Assistant (PDA), a Portable Media Player (PMP), a navigation terminal, a wearable device, a smart band, a pedometer, and the like, and fixed terminals such as a Digital TV, a desktop computer, and the like.
The following description will be given by way of example of a mobile terminal, and it will be understood by those skilled in the art that the construction according to the embodiment of the present invention can be applied to a fixed type terminal, in addition to elements particularly used for mobile purposes.
Referring to fig. 1, which is a schematic diagram of a hardware structure of a mobile terminal for implementing various embodiments of the present invention, the mobile terminal 100 may include: RF (Radio Frequency) unit 101, WiFi module 102, audio output unit 103, a/V (audio/video) input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, processor 110, and power supply 111. Those skilled in the art will appreciate that the mobile terminal architecture shown in fig. 1 is not intended to be limiting of mobile terminals, which may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The following describes each component of the mobile terminal in detail with reference to fig. 1:
the radio frequency unit 101 may be configured to receive and transmit signals during information transmission and reception or during a call, and specifically, receive downlink information of a base station and then process the downlink information to the processor 110; in addition, the uplink data is transmitted to the base station. Typically, radio frequency unit 101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 101 can also communicate with a network and other devices through wireless communication. The wireless communication may use any communication standard or protocol, including but not limited to GSM (Global System for Mobile communications), GPRS (General Packet Radio Service), CDMA2000(code Division Multiple Access 2000), WCDMA (Wideband code Division Multiple Access), TD-SCDMA (Time Division-Synchronous code Division Multiple Access), FDD-LTE (Frequency Division duplex-Long Term Evolution), and TDD-LTE (Time Division duplex-Long Term Evolution).
WiFi belongs to short-distance wireless transmission technology, and the mobile terminal can help a user to receive and send e-mails, browse webpages, access streaming media and the like through the WiFi module 102, and provides wireless broadband internet access for the user. Although fig. 1 shows the WiFi module 102, it is understood that it does not belong to the essential constitution of the mobile terminal, and may be omitted entirely as needed within the scope not changing the essence of the invention.
The audio output unit 103 may convert audio data received by the radio frequency unit 101 or the WiFi module 102 or stored in the memory 109 into an audio signal and output as sound when the mobile terminal 100 is in a call signal reception mode, a call mode, a recording mode, a voice recognition mode, a broadcast reception mode, or the like. Also, the audio output unit 103 may also provide audio output related to a specific function performed by the mobile terminal 100 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 103 may include a speaker, a buzzer, and the like.
The a/V input unit 104 is used to receive audio or video signals. The a/V input Unit 104 may include a Graphics Processing Unit (GPU) 1041 and a microphone 1042, and the Graphics Processing Unit 1041 processes image data of still pictures or videos obtained by an image capturing terminal (e.g., a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 106. The image frames processed by the graphic processor 1041 may be stored in the memory 109 (or other storage medium) or transmitted via the radio frequency unit 101 or the WiFi module 102. The microphone 1042 may receive sounds (audio data) via the microphone 1042 in a phone call mode, a recording mode, a voice recognition mode, or the like, and may be capable of processing such sounds into audio data. The processed audio (voice) data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 101 in case of a phone call mode. The microphone 1042 may implement various types of noise cancellation (or suppression) algorithms to cancel (or suppress) noise or interference generated in the course of receiving and transmitting audio signals.
The mobile terminal 100 also includes at least one sensor 105, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor that can adjust the brightness of the display panel 1061 according to the brightness of ambient light, and a proximity sensor that can turn off the display panel 1061 and/or a backlight when the mobile terminal 100 is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the posture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile phone, further description is omitted here.
The display unit 106 is used to display information input by a user or information provided to the user. The Display unit 106 may include a Display panel 1061, and the Display panel 1061 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.
The user input unit 107 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the mobile terminal. Specifically, the user input unit 107 may include a touch panel 1071 and other input devices 1072. The touch panel 1071, also referred to as a touch screen, may collect a touch operation performed by a user on or near the touch panel 1071 (e.g., an operation performed by the user on or near the touch panel 1071 using a finger, a stylus pen, or any other suitable object or accessory), and drive a corresponding connection terminal according to a preset program. The touch panel 1071 may include two parts of a touch detection terminal and a touch controller. The touch detection terminal detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing terminal, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 110, and can receive and execute commands sent by the processor 110. In addition, the touch panel 1071 may be implemented in various types, such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. In addition to the touch panel 1071, the user input unit 107 may include other input devices 1072. In particular, other input devices 1072 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like, and are not limited to these specific examples.
Further, the touch panel 1071 may cover the display panel 1061, and when the touch panel 1071 detects a touch operation thereon or nearby, the touch panel 1071 transmits the touch operation to the processor 110 to determine the type of the touch event, and then the processor 110 provides a corresponding visual output on the display panel 1061 according to the type of the touch event. Although the touch panel 1071 and the display panel 1061 are shown in fig. 1 as two separate components to implement the input and output functions of the mobile terminal, in some embodiments, the touch panel 1071 and the display panel 1061 may be integrated to implement the input and output functions of the mobile terminal, and is not limited herein.
The interface unit 108 serves as an interface through which at least one external terminal is connected to the mobile terminal 100. For example, the external terminal may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a terminal having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 108 may be used to receive input (e.g., data information, power, etc.) from an external terminal and transmit the received input to one or more elements within the mobile terminal 100 or may be used to transmit data between the mobile terminal 100 and the external terminal.
The memory 109 may be used to store software programs as well as various data. The memory 109 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 109 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 110 is a control center of the mobile terminal, connects various parts of the entire mobile terminal using various interfaces and lines, and performs various functions of the mobile terminal and processes data by operating or executing software programs and/or modules stored in the memory 109 and calling data stored in the memory 109, thereby performing overall monitoring of the mobile terminal. Processor 110 may include one or more processing units; preferably, the processor 110 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 110.
The mobile terminal 100 may further include a power supply 111 (e.g., a battery) for supplying power to various components, and preferably, the power supply 111 may be logically connected to the processor 110 via a power management system, so as to manage charging, discharging, and power consumption management functions via the power management system.
Although not shown in fig. 1, the mobile terminal 100 may further include a bluetooth module or the like, which is not described in detail herein.
In order to facilitate understanding of the embodiments of the present invention, a communication network system on which the mobile terminal of the present invention is based is described below.
Referring to fig. 2, fig. 2 is an architecture diagram of a communication Network system according to an embodiment of the present invention, where the communication Network system is an LTE system of a universal mobile telecommunications technology, and the LTE system includes a UE (User Equipment) 201, an E-UTRAN (Evolved UMTS Terrestrial Radio Access Network) 202, an EPC (Evolved Packet Core) 203, and an IP service 204 of an operator, which are in communication connection in sequence.
Specifically, the UE201 may be the terminal 100 described above, and is not described herein again.
The E-UTRAN202 includes eNodeB2021 and other eNodeBs 2022, among others. Among them, the eNodeB2021 may be connected with other eNodeB2022 through backhaul (e.g., X2 interface), the eNodeB2021 is connected to the EPC203, and the eNodeB2021 may provide the UE201 access to the EPC 203.
The EPC203 may include an MME (Mobility Management Entity) 2031, an HSS (Home Subscriber Server) 2032, other MMEs 2033, an SGW (Serving gateway) 2034, a PGW (PDN gateway) 2035, and a PCRF (Policy and charging functions Entity) 2036, and the like. The MME2031 is a control node that handles signaling between the UE201 and the EPC203, and provides bearer and connection management. HSS2032 is used to provide registers to manage functions such as home location register (not shown) and holds subscriber specific information about service characteristics, data rates, etc. All user data may be sent through SGW2034, PGW2035 may provide IP address assignment for UE201 and other functions, and PCRF2036 is a policy and charging control policy decision point for traffic data flow and IP bearer resources, which selects and provides available policy and charging control decisions for a policy and charging enforcement function (not shown).
The IP services 204 may include the internet, intranets, IMS (IP Multimedia Subsystem), or other IP services, among others.
Although the LTE system is described as an example, it should be understood by those skilled in the art that the present invention is not limited to the LTE system, but may also be applied to other wireless communication systems, such as GSM, CDMA2000, WCDMA, TD-SCDMA, and future new network systems.
Based on the above mobile terminal hardware structure and communication network system, the present invention provides various embodiments of the method.
Fig. 3 is a similarity calculation method according to a first embodiment of the present invention, as shown in fig. 3, the similarity calculation method includes:
s101, obtaining a similarity matrix according to the article similarity label;
in this step, the similarity label of the article is first converted into a similarity vector. For example, taking news as an article, the similarity label "ship-borne" of the article is quantized into a 100-dimensional similarity vector using word2vec vector as follows:
Figure BDA0001303169420000101
for example, item a contains n similarity tags and can be described by their corresponding similarity vectors [ a [1], a [2], a [3] … …, a [ n ], and item b contains n similarity tags and can be described by the similarity vectors [ b [1], b [2], b [3], … …, b [ n ]. By using the article similarity vector, the cosine similarity between two article similarity labels is calculated, and the cosine similarity values are expanded according to a square matrix to obtain a similarity matrix.
When the number of similarity labels of the articles is not consistent, for example, article a has m similarity labels, article b has n similarity labels, an m × n dimensional similarity matrix is obtained, if the similarity label of article a is greater than the similarity label of article b, that is: m > n, the m × n dimensional similarity matrix may be supplemented with zeros as an n × n dimensional similarity matrix.
For example: news 1 similarity label is [ a [1]]，a[2]，a[3]……，a[n]]The similarity label of News 2 is [ b [1]]，b[2]，b[3]，……，b[n]]Then label a [1] the first similarity of news 1]Respectively carrying out vector dot multiplication with each similarity label of news 2 to obtain a similarity matrix Qn×nThe second similarity label a [2] of news 1]Respectively carrying out vector dot multiplication with each similarity label of news 2 to obtain a similarity matrix Qn×nThe nth similarity label a [ n ] of news 1, and so on]Respectively carrying out vector dot multiplication with each similarity label of news 2 to obtain a similarity matrix Qn×nTo obtain the whole similarity matrix Qn×n，
Figure BDA0001303169420000111
S102, extracting elements which are not in the same row and the same column in the similarity matrix to form irrelevant items, and calculating matching values of all the irrelevant items;
and the irrelevant item matching value is the average value of all irrelevant items of the similarity matrix and represents the key similar information of the similarity matrix. The extraneous items may be formed by extracting elements in the similarity matrix that are not in the same row and column, i.e., one element per row is taken in the similarity matrix and each element does not appear in all matches in the same column. For example, for an n-dimensional similarity matrix, the n elements on the diagonal form a set of independent items, and the number of the independent items of the n-dimensional similarity matrix is
Figure BDA0001303169420000112
When all the irrelevant items of the similarity matrix are obtained, the irrelevant items can be obtained one by adopting an exhaustion method according to the concept.
In a preferred embodiment, the similarity can be directly aligned hereAdding all elements in the matrix and multiplying by the number of repeated items
Figure BDA0001303169420000113
Obtaining the weight sum of all the independent items, and dividing the sum by the number of the independent items
Figure BDA0001303169420000114
Obtaining an irrelevant item matching value R;
Figure BDA0001303169420000115
wherein q isijElements representing the ith row and the jth column of the similarity matrix, n being the dimension of the similarity matrix,
Figure BDA0001303169420000116
the number of entries repeated for the similarity matrix,
Figure BDA0001303169420000117
is the number of irrelevant items.
S103, determining the similarity of the articles according to the independent item matching value and the comprehensive weight of the similarity matrix.
In this step, the similarity S is obtained according to the irrelevant item matching value and the comprehensive weight of the irrelevant item matching number.
S＝R/n；
Where R is the matching value of the unrelated item in step S102, and n is the dimension of the similarity matrix.
The similarity calculation method provided by the invention calculates the matching value of the irrelevant item by searching all the irrelevant items of the similarity matrix, and determines the similarity of the article according to the matching value of the irrelevant item and the comprehensive weight of the similarity matrix. Compared with the existing weighted bipartite graph maximum matching algorithm, the method is directly realized by addition in combination with the characteristics of similarity matrix independent item matching when extracting independent item matching without using an exhaustion method, thereby simplifying the calculation process of the similarity of the articles, reducing the time complexity of the algorithm from O (nm) to O (1), having good calculation performance under a distributed calculation platform, and effectively saving the calculation resources of the system.
The following specifically describes a specific implementation process of the similarity calculation method provided by the present invention, taking article a as news 1 and article b as news 2 as an example.
As shown in fig. 4, the user uses the similarity calculation terminal 100 provided in the present invention to input the similarity label of news 1 and the similarity label of news 2 for four different items. The specific input content is shown in table 1.
Table 1 similarity label for news 1 and similarity label for news 2
Item number Similarity label of news 1 Similarity label for news 2
1 Ship-borne, jet-propelled, double-seat Early warning machine, China air force
2 Government, central and reform Propelling, regulating and battle ship
3 Ship-borne, jet-propelled, double-seat Early warning machine, Chinese air force and flight
4 Ship-borne, jet-propelled, double-seat Early warning machine, Chinese air force, flight, Liaoning
After receiving the similarity label input by the user, the terminal 100 performs the following steps:
s201, acquiring a similarity matrix according to the article similarity label;
wherein, the similarity label is a similarity label of two compared objects, for example, the similarity label of news 1 and the similarity label of news 2 in table 1.
Taking the similarity label of the user input item 1 as an example, news is taken as an article, and the similarity label is quantized into a 100-dimensional vector by using word2vec vector. The word2vec is a tool for converting a single word into a vector form, and can simplify the processing of text content into vector operation in a vector space, and calculate the similarity in the vector space to represent the similarity in text semantics.
Specifically, word2vec vectors are quantized into 100-dimensional vectors respectively by using a similarity label 'ship-borne, jet-propelled, double-seat' of the user input news 1 and a similarity label 'early warning machine and Chinese air force' of the news 2. For example, for the word similarity label "ship-borne", a vector quantized to 100 dimensions using the word2vec vector is:
Figure BDA0001303169420000131
and secondly, performing vector point multiplication on the 100-dimensional vectors corresponding to the similarity labels of the news 1 and the 100-dimensional vectors corresponding to the similarity labels of the news 2 to obtain a similarity matrix.
Specifically, the 100-dimensional vector corresponding to the first similarity label "ship-borne" in news 1 is respectively subjected to vector point multiplication with each similarity label in news 2, namely the 100-dimensional vectors corresponding to the "early warning aircraft" and the "Chinese air force" to obtain elements in the first row of the similarity matrix. For example, for a first similarity label "carrier-based" of news 1 and a first similarity label "early warning machine" of news 2, a value corresponding to each dimension (one hundred in total) in a 100-dimensional vector corresponding to the "carrier-based" is multiplied by a value corresponding to each dimension in a 100-dimensional vector corresponding to the "early warning machine", and then the multiplication result is added and divided by a preset fixed value, so that elements in a first row and a first column of a similarity matrix are obtained. And carrying out vector point multiplication on the first similarity label 'carrier-borne' of the news 1 and the second similarity label 'China air force' of the news 2 according to the mode to obtain elements of the first row and the second column of the similarity matrix.
According to the mode, the 100-dimensional vector corresponding to the second similarity label 'jet' of the news 1 and the 100-dimensional vector corresponding to the similarity label 'early warning machine' and 'Chinese air force' of the news 2 are subjected to vector point multiplication respectively to obtain the elements of the second row of the similarity matrix. And respectively carrying out vector point multiplication on 100-dimensional vectors corresponding to the third similarity label 'two seats' of the news 1 and 100-dimensional vectors corresponding to the similarity labels 'early warning machine' and 'Chinese air force' of the news 2 to obtain elements in the third row of the similarity matrix.
In this embodiment, the two compared article similarity labels have different numbers, that is, news 1 includes 3 similarity labels, news 2 includes 2 similarity labels, the initial similarity matrix obtained by the vector dot product method is a 3 × 2 dimensional matrix, in this case, the third column element of the initial similarity matrix is supplemented with 0 to obtain a 3 × 3 dimensional similarity matrix Q5×5。
Figure BDA0001303169420000141
S201, extracting elements which are not in the same row and the same column in the similarity matrix to form irrelevant items, and calculating matching values of all the irrelevant items;
and the irrelevant item matching value is the average value of all irrelevant items of the similarity matrix and represents the key similar information of the similarity matrix. The independent items may be formed by extracting elements of the similarity matrix that are not in the same row and column, i.e. one element is taken per row in the similarity matrix and each element does not appear in all elements of the same columnThere is a match. For example, for an n-dimensional similarity matrix, the n elements on the diagonal form a set of independent items, and the number of the independent items of the n-dimensional similarity matrix is
Figure BDA0001303169420000142
Specifically, in this step, a similarity matrix Q obtained by inputting similarity labels of news 1 and news 2 for the user in item 1 is obtained5×5The similarity matrix Q5×5All elements in the similarity matrix are added and multiplied by the number of repeated items of the similarity matrix
Figure BDA0001303169420000143
And obtaining the weight sum of all the irrelevant items.
Wherein, the number of repeated items
Figure BDA0001303169420000144
Sum of weights of all independent item matches
Figure BDA0001303169420000145
Divided by the number of independent items
Figure BDA0001303169420000146
Obtaining an irrelevant item matching value R;
Figure BDA0001303169420000147
s203, determining the similarity of the articles according to the independent item matching value and the comprehensive weight of the similarity matrix;
in this step, the similarity S is obtained according to the irrelevant item matching value and the comprehensive weight of the irrelevant item matching number.
S＝R/n；
Where R is the matching value of the unrelated item in step S202, and n is the dimension of the similarity matrix, i.e. 3.
Through the above steps, the similarity S1 of news 1 and news 2 in item 1 can be obtained according to the similarity tag of news 1 and the similarity tag of news 2 input by the user to the terminal 100 in item 1 in table 1.
Repeating the above steps S201 to S203, respectively analyzing the similarity label of news 1 and the similarity label of news 2 input by the user to the terminal 100 in item 2, item 3 and item 4 of table 1 to obtain the similarity S2 of news 1 and news 2 in item 2, the similarity S3 of news 1 and news 2 in item 3 and the similarity S4 of news 1 and news 2 in item 4. The specific output results are shown in fig. 5 and table 2.
Table 2 similarity of news 1 and news 2
Figure BDA0001303169420000151
Based on the above method steps, the embodiment of the present invention further provides a similarity calculation terminal to execute each step in the method embodiment of the present invention. The similarity calculation terminal includes, but is not limited to, a mobile phone, a smart phone, a notebook computer, a digital broadcasting receiver, a PDA, a PAD, a PMP, a navigation device, and the like.
If the similarity calculation terminal has an operating system, the operating system may be UNIX, Linux, Windows, Android (Android), Windows Phone, or the like.
The following description will be given taking a case where the mobile terminal is a mobile phone as an example.
In the first embodiment of the present invention, the mobile terminal further has portability, and specifically, the mobile terminal can be held by one hand, so that when similarity calculation is required in various scenes, the similarity calculation can be realized by utilizing the portability of the mobile terminal.
As shown in fig. 6, the similarity calculation terminal 60 includes: a memory 61, a processor 62 and a similarity calculation program stored on the memory and executable on the processor, the similarity calculation program when executed by the processor implementing the steps of:
step 1: acquiring a similarity matrix according to the article similarity label;
the similarity matrix can be obtained according to the similarity label of the article, and the article can be described by the n-dimensional similarity label. For example, item a may be described by a similarity vector [ a [1], a [2], a [3] … …, a [ n ], and item b may be described by a similarity vector [ b [1], b [2], b [3], … …, b [ n ]. By using the article similarity vector, the cosine similarity between two article similarity labels is calculated, and the cosine similarity values are expanded according to a square matrix to obtain a similarity matrix.
Step 2: extracting elements which are not in the same row and the same column in the similarity matrix to form irrelevant items, and calculating matching values of all the irrelevant items;
and the irrelevant item matching value is the average value of all irrelevant items of the similarity matrix and represents the key similar information of the similarity matrix. The extraneous items may be formed by extracting elements in the similarity matrix that are not in the same row and column, i.e., one element per row is taken in the similarity matrix and each element does not appear in all matches in the same column. For example, for an n-dimensional similarity matrix, the n elements on the diagonal form a set of independent items, and the number of the independent items of the n-dimensional similarity matrix is
Figure BDA0001303169420000161
When all the irrelevant items of the similarity matrix are matched, the irrelevant items can be acquired one by adopting an exhaustion method according to the concept.
In a preferred embodiment, all elements in the similarity matrix are directly added and multiplied by the number of repeated items
Figure BDA0001303169420000162
Obtaining the weight sum of all the independent items, and dividing the sum by the number of the independent items
Figure BDA0001303169420000163
Obtaining an irrelevant item matching value R;
Figure BDA0001303169420000164
wherein q isijElements representing the ith row and the jth column of the similarity matrix, n being the dimension of the similarity matrix,
Figure BDA0001303169420000165
the number of entries repeated for the similarity matrix,
Figure BDA0001303169420000166
is the number of irrelevant items.
And step 3: and determining the similarity of the articles according to the independent item matching value and the comprehensive weight of the similarity matrix.
The number of the irrelevant item matches can be determined according to the permutation and combination rule, and for the similarity matrix with the dimension n, the number of the irrelevant item matches is
Figure BDA0001303169420000167
In this step, the similarity S is obtained according to the irrelevant item matching value and the comprehensive weight of the irrelevant item matching number.
S＝R/n；
Where R is the matching value of the unrelated item in step S102, and n is the dimension of the similarity matrix.
Based on the above embodiments, the present invention also provides a computer-readable storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the steps of:
acquiring a similarity matrix according to the article similarity label;
extracting elements which are not in the same row and the same column in the similarity matrix to form irrelevant items, and calculating matching values of all the irrelevant items;
determining the similarity of the articles according to the independent item matching value and the comprehensive weight of the similarity matrix so as to reduce the time complexity of the calculation of the similarity of the articles;
and the irrelevant item matching value is the average value of all irrelevant items of the similarity matrix and represents the key similar information of the similarity matrix.
Specifically, in the step of obtaining the similarity matrix according to the item similarity label, the one or more programs may be further executable by the one or more processors to implement the following steps:
converting the similarity label into a similarity vector;
and multiplying the similarity vector point to obtain a similarity matrix:
Figure BDA0001303169420000171
wherein n is the number of the article similarity labels.
Further, in the extracting elements in the similarity matrix that are not in the same row and the same column to form independent items, and in the calculating matching values of all the independent items, the one or more programs may be further executable by the one or more processors to implement the steps of:
by passing
Figure BDA0001303169420000172
Calculating all the irrelevant item matching values;
wherein q isijFor the ith row and jth column element of the similarity matrix,
Figure BDA0001303169420000173
the number of entries repeated for the similarity matrix,
Figure BDA0001303169420000174
is the number of irrelevant items.
According to the similarity calculation method, the terminal and the computer readable storage medium, all irrelevant items of the similarity matrix are searched, the matching value of the irrelevant items is calculated, and the similarity of the articles is determined according to the matching value of the irrelevant items and the comprehensive weight of the similarity matrix. Compared with the existing weighted bipartite graph maximum matching algorithm, the method is directly realized by addition in combination with the characteristics of similarity matrix independent item matching when extracting independent item matching without using an exhaustion method, thereby simplifying the calculation process of the similarity of the articles, reducing the time complexity of the algorithm from O (nm) to O (1), having good calculation performance under a distributed calculation platform, and effectively saving the calculation resources of the system.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (4)

1. A similarity calculation method, characterized by comprising:
acquiring a similarity matrix according to the article similarity label;
extracting elements which are not in the same row and the same column in the similarity matrix to form irrelevant items, and calculating matching values of all the irrelevant items;
determining the similarity of the articles according to the independent item matching value and the comprehensive weight of the similarity matrix so as to reduce the time complexity of the calculation of the similarity of the articles;
the irrelevant item matching value is the average value of all irrelevant items of the similarity matrix and represents key similar information of the similarity matrix;
the obtaining of the similarity matrix according to the item similarity label includes:
converting the similarity label into a similarity vector;
and multiplying the similarity vector point to obtain a similarity matrix:
Figure FDA0002293004370000011
wherein n is the number of the article similarity labels and q is the number of the article similarity labels11Is the 1 st row and 1 st column element of the similarity matrix;
the matching value of the irrelevant item of the similarity matrix is as follows:
Figure FDA0002293004370000012
wherein q isijFor the ith row and jth column element of the similarity matrix,
Figure FDA0002293004370000013
the number of entries repeated for the similarity matrix,
Figure FDA0002293004370000014
is the number of irrelevant items.
2. The similarity calculation method according to claim 1, wherein the item similarity is: s is R/n;
wherein R is an unrelated item matching value.
3. A similarity calculation terminal, characterized by comprising: a memory, a processor, and a similarity calculation program stored on the memory and executable on the processor, the similarity calculation program when executed by the processor implementing the steps of:
acquiring a similarity matrix according to the article similarity label;
extracting elements which are not in the same row and the same column in the similarity matrix to form irrelevant items, and calculating matching values of all the irrelevant items;
determining the similarity of the articles according to the independent item matching value and the comprehensive weight of the similarity matrix so as to reduce the time complexity of the calculation of the similarity of the articles;
the irrelevant item matching value is the average value of all irrelevant items of the similarity matrix and represents key similar information of the similarity matrix;
in the step of obtaining the similarity matrix according to the item similarity label, the processor executes the similarity calculation program to implement the steps of:
converting the similarity label into a similarity vector;
and multiplying the similarity vector point to obtain a similarity matrix:
Figure FDA0002293004370000021
wherein n is the number of the article similarity labels and q is the number of the article similarity labels11Is the 1 st row and 1 st column element of the similarity matrix;
in the step of extracting elements in the similarity matrix that are not in the same row and the same column to form unrelated items, and calculating matching values of all the unrelated items, the processor executes the similarity calculation program to implement the following steps:
by passing
Figure FDA0002293004370000022
Calculating all the irrelevant item matching values;
wherein q isijFor the ith row and jth column element of the similarity matrix,
Figure FDA0002293004370000023
the number of entries repeated for the similarity matrix,
Figure FDA0002293004370000024
is the number of irrelevant items.
4. A computer-readable storage medium storing one or more programs, the one or more programs being executable by one or more processors for performing the steps of:
acquiring a similarity matrix according to the article similarity label;
extracting elements which are not in the same row and the same column in the similarity matrix to form irrelevant items, and calculating matching values of all the irrelevant items;
determining the similarity of the articles according to the independent item matching value and the comprehensive weight of the similarity matrix so as to reduce the time complexity of the calculation of the similarity of the articles;
the irrelevant item matching value is the average value of all irrelevant items of the similarity matrix and represents key similar information of the similarity matrix;
in the obtaining a similarity matrix from the item similarity label step, the one or more programs may be further executable by the one or more processors to perform the steps of:
converting the similarity label into a similarity vector;
and multiplying the similarity vector point to obtain a similarity matrix:
Figure FDA0002293004370000031
wherein n is the similarity of the articlesNumber of tags, q11Is the 1 st row and 1 st column element of the similarity matrix;
in the extracting elements of the similarity matrix that are not in the same row and the same column to form unrelated items, and in the calculating matching values of all the unrelated items, the one or more programs may be further executable by the one or more processors to implement the steps of:
by passing
Figure FDA0002293004370000032
Calculating all the irrelevant item matching values;
wherein q isijFor the ith row and jth column element of the similarity matrix,
Figure FDA0002293004370000033
the number of entries repeated for the similarity matrix,
Figure FDA0002293004370000034
is the number of irrelevant items.
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