JP2024032488A - Information processing system, computer program, and information processing method - Google Patents

Abstract

The present invention evaluates the similarity in data structure between data sets. A first data set including data describing characteristics of each of the plurality of first elements is obtained for the plurality of first elements in the first set (S110). For the plurality of second elements in the second set, a second dataset is obtained that includes data describing characteristics of each of the plurality of second elements (S120). Based on the comparison between the neighborhood graph of the first set based on the similarity between the plurality of first elements and the neighborhood graph of the second set based on the similarity between the plurality of second elements, The similarity in data structure between the data set and the second data set is evaluated (S130-S220). [Selection diagram] Figure 4

Description

The present disclosure relates to an information processing system and an information processing method.

Conventionally, consumer purchasing behavior has been analyzed based on product sales data. Analyzing consumers' exposure behavior to mass media and network content is also being conducted.

Data fusion techniques are also known that combine multiple pieces of data collected by different means based on a common variable. Patent Document 1 discloses that a first data set related to a first consumer group and a second data set related to a second consumer group are shared between the first data set and the second data set. Discloses a technique for combining using variables.

Japanese Patent Application Publication No. 2016-126609

When attempting to combine a first data set with a second data set, multiple types of data sets may be prepared as candidates for the second data set to be combined. For example, when attempting to combine a data set related to consumer purchasing behavior as a second data set with a first data set, multiple data sets related to the purchasing behavior of different sets of consumers may be combined as candidates. It may be available from the data vendor.

Alternatively, multiple data sets describing purchasing behavior using different parameters may be prepared by processing sales history such as POS data. Examples of parameters include the number of products purchased and the purchase price.

Here, consider a case where the first data set is a data set related to a consumer set in which consumers of all ages and all genders are approximately uniformly present. In this case, rather than selecting a data set of only female consumers as the second data set to be combined, a data set of a consumer set in which consumers of all ages and all genders are approximately uniformly selected is selected. It is thought that the accuracy of data fusion will improve if this is selected.

That is, the accuracy of data fusion between the first data set and the second data set is considered to change depending on the consumer set handled by the second data set. Similarly, the accuracy of data fusion between the first data set and the second data set is considered to vary depending on the type of purchasing behavior parameter described by the second data set. This is because the distribution of consumers on the feature space changes depending on the type of parameter.

Thus, the accuracy of data fusion between the first data set and the second data set depends on the similarity of the data structures between the first data set and the second data set. This dependence is not limited to purchasing behavior data sets.

Therefore, according to one aspect of the present disclosure, it is possible to provide a system and method capable of evaluating the similarity in data structure between a first data set and a second data set with respect to various types of data sets. desirable.

According to one aspect of the present disclosure, an information processing system including a first acquisition section, a second acquisition section, and an evaluation section is provided. The first acquisition unit is configured to acquire, with respect to the plurality of first elements in the first set, a first data set including data describing characteristics of each of the plurality of first elements. The second acquisition unit is configured to acquire, with respect to the plurality of second elements in the second set, a second data set including data describing characteristics of each of the plurality of second elements.

The evaluation unit generates a neighborhood graph of the first set based on the similarity between the plurality of first elements determined from the first data set, and a neighborhood graph between the plurality of second elements determined from the second data set. and a second set of neighborhood graphs based on the similarity of the first dataset and the second dataset.

The neighborhood graph is related to the distribution of multiple elements on the feature space. Therefore, according to the above comparison, it is possible to evaluate the similarity between the data structure of the first data set and the data structure of the second data set.

According to one aspect of the present disclosure, the first acquisition section, the second acquisition section, the first similarity calculation section, the second similarity calculation section, the first eigenvalue calculation section, and the first similarity calculation section. Another information processing system including two eigenvalue calculation units and an evaluation unit may be provided.

The first acquisition unit is configured to acquire, with respect to the plurality of first elements in the first set, a first data set including data describing characteristics of each of the plurality of first elements. The second acquisition unit is configured to acquire, with respect to the plurality of second elements in the second set, a second data set including data describing characteristics of each of the plurality of second elements.

The first similarity calculation unit is configured to calculate the similarity between the plurality of first elements based on the first data set. The second similarity calculation unit is configured to calculate the similarity between the plurality of second elements based on the second data set.

The first eigenvalue calculation unit is configured to calculate a group of eigenvalues of the first graph Laplacian matrix as a group of first eigenvalues based on the degree of similarity between the plurality of first elements. The first graph Laplacian matrix corresponds to a neighborhood graph in which each of the plurality of first elements is connected to one or more first elements in the first set whose similarity level satisfies a predetermined condition. is the graph Laplacian matrix.

The second eigenvalue calculation unit is configured to calculate a group of eigenvalues of the second graph Laplacian matrix as a group of second eigenvalues based on the degree of similarity between the plurality of second elements. The second graph Laplacian matrix corresponds to a neighborhood graph in which each of the plurality of second elements is connected to one or more second elements in the second set whose degree of similarity satisfies a predetermined condition. is the graph Laplacian matrix.

The evaluation unit is configured to evaluate similarity regarding the data structure between the first data set and the second data set based on a comparison between the first set of eigenvalues and the second set of eigenvalues. configured.

A neighborhood graph relates to the distribution of multiple elements in a feature space. When two neighborhood graphs are similar, the groups of eigenvalues of the two corresponding graph Laplacian matrices are also similar. According to the above comparison, it is possible to evaluate the similarity between the neighborhood graph regarding the first set and the neighborhood graph regarding the second set.

The neighborhood graph corresponds to the data structure of the corresponding dataset. Therefore, according to the above comparison, it is possible to evaluate the similarity between the data structure of the first data set and the data structure of the second data set.

This evaluation is useful, for example, in determining compatibility regarding data fusion between the first data set and the second data set. Evaluation is useful, for example, in selecting datasets to be combined in data fusion. However, the evaluation is not limited to data fusion applications.

According to one aspect of the present disclosure, the evaluation unit includes a group of first eigenvalues for each of the plurality of first eigenvalues included in the group of first eigenvalues, which are ranked based on the size of the eigenvalues. and the ranking of each of the plurality of second eigenvalues included in the group of second eigenvalues in the group of second eigenvalues, each of the plurality of first eigenvalues is The similarity regarding the data structure may be evaluated by comparing the eigenvalues with the eigenvalues of the same rank among the eigenvalues. According to such an evaluation, it is possible to more appropriately evaluate the similarity in data structure between data sets.

According to one aspect of the present disclosure, the evaluation unit assigns each of the eigenvalues from the first to a predetermined rank in descending order of the eigenvalues included in the group of first eigenvalues to the same rank among the plurality of second eigenvalues. Similarity regarding data structures may be evaluated by comparing with the eigenvalues of .

According to one aspect of the present disclosure, the evaluation unit may be configured to calculate the similarity evaluation value regarding the data structure using the sum of squares of errors. Each of the errors may be a difference between a first eigenvalue of a corresponding rank among the plurality of first eigenvalues and a second eigenvalue of a corresponding rank among the plurality of second eigenvalues. By using the sum of squared errors, it is possible to more appropriately evaluate the similarity in data structure between data sets.

According to one aspect of the present disclosure, the first graph Laplacian matrix is a graph Laplacian matrix of a nearest neighbor graph connecting each of the plurality of first elements to a first element having the highest similarity in the first set. It may be a matrix. The second graph Laplacian matrix may be a graph Laplacian matrix of a nearest neighbor graph in which each of the plurality of second elements is connected to the second element having the highest degree of similarity in the second set.

According to one aspect of the present disclosure, the second acquisition unit may acquire a plurality of evaluation target datasets as the second dataset. Each of the plurality of evaluation target data sets may be a data set including data describing characteristics of each of the plurality of elements with respect to the plurality of elements in the corresponding set. The plurality of data sets to be evaluated may be data sets relating to different sets, or data sets having different described characteristics.

The second similarity calculation unit may calculate the similarity between the plurality of elements in the corresponding set for each of the plurality of evaluation target data sets. The second eigenvalue calculation unit generates a neighborhood graph for each of the plurality of evaluation target data sets, and converts each of the plurality of elements in the corresponding set into a corresponding set whose high degree of similarity satisfies a predetermined condition. A group of eigenvalues of a graph Laplacian matrix corresponding to a neighborhood graph connected to one or more elements in the graph may be calculated as a group of eigenvalues to be compared.

The evaluation unit evaluates the first data based on a comparison between a group of comparison target eigenvalues based on the corresponding evaluation target dataset and a first group of eigenvalues for each of the plurality of evaluation target datasets. Similarity in data structure between the set and the corresponding dataset to be evaluated may be evaluated.

According to one aspect of the present disclosure, the information processing system may further include a selection unit. The selection unit may be configured to select, as a combination target, a data set with the highest evaluation of similarity regarding data structure from among the plurality of evaluation target data sets.

According to one aspect of the present disclosure, the information processing system may further include a coupling unit. The combination unit associates the first data set and the second data set selected for combination with data describing characteristics of similar elements between the first set and the corresponding set. may be configured to be coupled to.

According to such selection, a second data set suitable for combination with the first data set can be selected from among a plurality of data sets. Therefore, highly accurate data fusion between the first data set and the second data set can be achieved.

According to one aspect of the present disclosure, the first data set may be a data set that describes characteristics of a plurality of people in the first set as a plurality of first elements. The second data set may be a data set that describes characteristics of a plurality of people in the second set as a plurality of second elements.

According to one aspect of the present disclosure, a computer program for causing a computer to implement at least some of the functions in the information processing system described above may be provided. According to one aspect of the present disclosure, a computer program for causing a computer to function as at least part of the first acquisition unit, the second acquisition unit, and the evaluation unit may be provided.

According to one aspect of the present disclosure, the first acquisition unit, the second acquisition unit, the first similarity calculation unit, the second similarity calculation unit, the first eigenvalue calculation unit, and the second eigenvalue calculation unit. , and a computer program for causing a computer to function as at least a part of the evaluation section. A computer program may be recorded on a computer-readable non-transitory storage medium.

According to one aspect of the present disclosure, an information processing method may be provided. The information processing method may be executed by a computer. The information processing method may include, for the plurality of first elements in the first set, obtaining a first data set including data describing characteristics of each of the plurality of first elements.

The information processing method may include, regarding the plurality of second elements in the second set, obtaining a second dataset including data describing characteristics of each of the plurality of second elements.

The information processing method generates a neighborhood graph of a first set based on the similarity between a plurality of first elements determined from a first data set, and a plurality of second elements determined from a second data set. a neighborhood graph of the second set based on the similarity between the first dataset and the second dataset; good.

According to one aspect of the present disclosure, another information processing method may be provided. Another information processing method may be performed by a computer. Another information processing method may include, with respect to the plurality of first elements in the first set, obtaining a first data set including data describing characteristics of each of the plurality of first elements. good.

Another information processing method may include, with respect to the plurality of second elements in the second set, obtaining a second data set including data describing characteristics of each of the plurality of second elements. good.

Another information processing method may include calculating the similarity between the plurality of first elements based on the first data set. Another information processing method may include calculating the similarity between the plurality of second elements based on the second data set.

Another information processing method may include calculating a group of eigenvalues of the first graph Laplacian matrix as a group of first eigenvalues based on the similarity between the plurality of first elements. The first graph Laplacian matrix corresponds to a neighborhood graph in which each of the plurality of first elements is connected to one or more first elements in the first set whose degree of similarity satisfies a predetermined condition. It can be a graph Laplacian matrix.

Another information processing method may include calculating a group of eigenvalues of the second graph Laplacian matrix as a group of second eigenvalues based on the similarity between the plurality of second elements. The second graph Laplacian matrix corresponds to a neighborhood graph in which each of the plurality of second elements is connected to one or more second elements in the second set whose degree of similarity satisfies a predetermined condition. It can be a graph Laplacian matrix.

Another information processing method evaluates the similarity in terms of data structure between a first data set and a second data set based on a comparison between a first set of eigenvalues and a second set of eigenvalues. It may include doing.

According to the above information processing method, it is possible to evaluate the similarity in data structure between the first data set and the second data set with respect to various types of data sets. The information processing system and information processing method described above are not limited to data fusion applications.

FIG. 1 is a block diagram showing the configuration of an information processing system. FIG. 3 is a diagram illustrating an example of generation of an extended data set by data fusion. 2 is a flowchart (part 1) representing evaluation processing executed by a processor. 12 is a flowchart (part 2) representing evaluation processing executed by the processor. 2 is a flowchart (Part 1) showing extended processing executed by a processor. 12 is a flowchart (part 2) illustrating extended processing executed by the processor.

Exemplary embodiments of the present disclosure will be described below with reference to the drawings.
The information processing system 1 of this embodiment is configured by installing a dedicated computer program Pr into a general-purpose computer. As shown in FIG. 1, the information processing system 1 includes a processor 11, a memory 13, a storage 15, a user interface 17, and a communication interface 19.

The processor 11 is configured to execute processing according to a computer program Pr stored in the storage 15. The memory 13 is a primary storage device including a RAM, and is used as a work area when the processor 11 executes processing.

The storage 15 is a secondary storage device including, for example, a hard disk drive or a solid state drive, and stores not only the computer program Pr but also various data used when executing the processing according to the computer program Pr.

The user interface 17 includes an input device for inputting operation signals from a user operating the information processing system 1 to the processor 11, and a display for displaying various information to the user. Examples of input devices include keyboards and pointing devices.

The communication interface 19 includes a LAN (Local Area Network) interface and a USB (Universal Serial Serial) interface, and is used for communication with external devices. The information processing system 1 transmits and receives data to and from external devices through the communication interface 19.

The processor 11 generates an extended data set 15C by extending the first data set 15A stored in the storage 15 using the second data set 15B stored in the storage 15 by executing processing according to the computer program Pr. do. The first data set 15A and the second data set 15B are obtained in advance from an external device through the communication interface 19, for example, and stored in the storage 15.

The first data set 15A is a data set that describes a first feature regarding a first set, and includes feature data for each first entity as the first feature data. Each of the first entities corresponds to each of the plurality of elements included in the first set. The first set is a first set of entities. The first set may be a first set of consumers. According to one example, the first entity is a consumer, ie a person.

The first feature data for each first entity is data that describes the first feature of the corresponding first entity. For example, the first data set 15A may be a data set regarding the purchasing behavior of a first set of consumers, as shown in FIG. In this case, the first characteristic data may be data describing characteristics of the corresponding consumer's purchasing behavior. The first characteristic data may be, for example, data describing whether or not each product is purchased with respect to a plurality of products.

The second data set 15B is a data set that describes a second feature regarding the second set, and includes feature data for each second entity as the second feature data. Each of the second entities corresponds to each of the plurality of elements included in the second set.

The second set is a second set of entities. The second set may be a second set of consumers. The second set of consumers can be the same or different set of consumers than the first set of consumers. According to one example, the second entity is a consumer, ie a person.

The second feature data for each second entity is data that describes the second feature of the corresponding second entity. The second feature data may be data describing a feature that is the same as or different from the first feature described by the first feature data. That is, the second data set 15B is a data set in which at least one of the second set and the second feature is different from the first set and the first feature.

For example, the second data set 15B may be a data set regarding the online behavior of a second set of consumers, as shown in FIG. According to the example shown in FIG. 2, the online behavior may be the behavior of visiting a website. The second characteristic data may be, for example, data describing whether or not each website has been visited, regarding a plurality of websites.

The extended data set 15C is a data set obtained by adding information included in the second data set 15B to the first data set 15A. The expansion increases the amount of information about the first entity. An increase in the amount of information will be useful for analyzing human behavior and distributing advertisements.

According to this embodiment, the processor 11 is configured to execute the evaluation process shown in FIGS. 3 and 4 according to instructions from the user. According to the evaluation process, the similarity of the data structure between the first data set 15A and the second data set 15B that the user wants to combine by data fusion is evaluated, and thereby the accuracy of data fusion is estimated in advance. be evaluated. The accuracy of data fusion corresponds to the accuracy (ie, accuracy) of the information described by the expanded data set 15C generated by data fusion.

The data structure of the first data set 15A and the second data set 15B is the structure of a graph when the similarity between entities in the first data set 15A and the second data set 15B is expressed in a graph, respectively. corresponds to As is well known, a graph is composed of a set of nodes (in other words, points) and links (in other words, edges).

In the evaluation process, each node of the first entity in the first set is connected to the node of the first entity with the highest degree of similarity on the feature space as a graph corresponding to the first data set 15A. A nearest neighbor graph is used.

Similarly, as a graph corresponding to the second data set 15B, each node of the second entity in the second set is connected to the node of the second entity with the highest degree of similarity on the feature space. A constructed nearest neighbor graph is used.

When the evaluation process shown in FIG. 3 starts, the processor 11 reads the first data set 15A designated by the user through the user interface 17 from the storage 15. Based on the read first data set 15A, the processor 11 generates a feature vector x for each first entity for the plurality of first entities included in the first set (S110).

Specifically, for each first entity, the processor 11 calculates the feature vector x=(x1 , x2, ..., xM1). M1 corresponds to the number of dimensions of the feature vector x.

When the first data set 15A is a data set representing the characteristics of the consumer's purchasing behavior as illustrated in FIG. 2, the feature vector x can include vector elements for each product. The vector element of each product represents whether the corresponding consumer has purchased the corresponding product.

In subsequent S120, the processor 11 reads the second data set 15B specified by the user through the user interface 17 from the storage 15. Based on the read second data set 15B, the processor 11 generates a feature vector y for each second entity for the plurality of second entities included in the second set.

Specifically, for each second entity, the processor 11 calculates the feature vector y=(y1 ,y2,...,yM2). M2 corresponds to the number of dimensions of the feature vector y.

If the second data set 15B is a data set representing characteristics of the consumer's online behavior as illustrated in FIG. 2, the feature vector y may include a vector element for each website. The vector element for each website represents whether the corresponding consumer has visited the corresponding website.

In subsequent S130, the processor 11 calculates the similarity R1 between the first entities included in the first set. For all possible combinations of two first entities in the first set, the processor 11 calculates, for each combination, the similarity R1 between the two first entities constituting the combination using the feature vector x. calculate.

The similarity R1 may be, for example, a cosine similarity calculated by the normalized inner product of the feature vectors x of the two first entities forming the combination. However, the similarity R1 is not limited to cosine similarity.

In subsequent S140, the processor 11 calculates a first graph Laplacian matrix L1 based on the similarity R1 between the first entities. The first graph Laplacian matrix L1 is a graph Laplacian matrix of the nearest neighbor graph of the first data set 15A. The first graph Laplacian matrix L1 can be calculated using the equation L1=D1-A1 using the degree matrix D1 and the adjacency matrix A1 of the nearest neighbor graph.

In the nearest neighbor graph of the first data set 15A, one of the plurality of first entities is selected as the processing target entity, and similarity between the processing target entity and the processing target entity is determined from the processing target entity node. Defined by executing the procedure of extending a link (in other words, a directed edge) toward the node of the one first entity with the highest degree R1 for all the plurality of first entities in the first set. It can be a directed graph.

In subsequent S150, the processor 11 calculates the eigenvalues λ1[1], λ1[2], ..., λ1[i], ..., λ1[N1] of the first graph Laplacian matrix L1. The value N1 is the number of unique values.

The index i of the eigenvalue λ1[i] (i=1, 2, ..., N1) is the eigenvalue λ1[1], λ1[2] of the eigenvalue λ1[i], which is ranked based on the size of the eigenvalue. , ..., λ1[i], ..., λ1[N1] in a group. That is, λ1[1]≧λ1[2]≧…≧λ1[N1].

In subsequent S160, the processor 11 determines, when adding the eigenvalues λ1[1], λ1[2], ..., λ1[i], ..., λ1[N1] in descending order, the eigenvalue exceeds a predetermined proportion α of the total sum. Determine the rank K1 of λ1[K1]. α may for example have the value 0.9. That is, the processor 11 determines the minimum value K1 that satisfies the following conditional expression.

In subsequent S170, the processor 11 calculates the similarity R2 between the second entities included in the second set. For every possible combination of two second entities in the second set, the processor 11 calculates, for each combination, the similarity R2 of the two second entities constituting the combination using the feature vector y. do.

The similarity R2 may be, for example, a cosine similarity calculated by the normalized inner product of the feature vectors y of the two second entities forming the combination. However, the similarity R2 is not limited to cosine similarity.

In subsequent S180, the processor 11 calculates a second graph Laplacian matrix L2 based on the similarity R2 between the second entities. The second graph Laplacian matrix L2 is a graph Laplacian matrix of the nearest neighbor graph of the second data set 15B.

The second graph Laplacian matrix L2 can be calculated using the equation L2=D2-A2 using the degree matrix D2 and the adjacency matrix A2 of the nearest neighbor graph. In the nearest neighbor graph, one of the plurality of second entities is selected as the processing target entity, and the similarity R2 between the selected processing target entity and the processing target entity is the highest. It can be a directed graph defined by performing the procedure of extending a link (in other words, a directed edge) toward a node of one second entity for all of the plurality of second entities in the second set. .

In subsequent S190, the processor 11 calculates the eigenvalues λ2[1], λ2[2], ..., λ2[i], ..., λ2[N2] of the second graph Laplacian matrix L2. The value N2 is the number of unique values.

The index i of the eigenvalue λ2[i] (i=1, 2,..., N2) is the eigenvalue λ2[1], λ2[2] of the eigenvalue λ2[i], which is ranked based on the size of the eigenvalue. , ..., λ2[i], ..., λ2[N2] in a group. That is, λ2[1]≧λ2[2]≧...≧λ2[N2].

In subsequent S200, the processor 11 determines, when adding the eigenvalues λ2[1], λ2[2], ..., λ2[i], ..., λ2[N2] in descending order, the eigenvalue exceeds a predetermined percentage α of the total sum. Determine the rank K2 of λ2[K2]. That is, the processor 11 determines the minimum value K2 that satisfies the following conditional expression. α may for example have the value 0.9.

In subsequent S210, the processor 11 sets the smaller value min{K1, K2} of the values K1 and K2 to the value K.

In subsequent S220, the processor 11 calculates the sum of squares of the errors of the eigenvalues according to the following equation as the evaluation value E regarding the similarity of data structures between the first data set 15A and the second data set 15B.

Each of the errors is the eigenvalue λ1[i] of the corresponding rank among the eigenvalues λ1[1], λ1[2], ..., λ1[i], ..., λ1[K] of the first graph Laplacian matrix L1. and the eigenvalue λ2[i] of the corresponding rank among the eigenvalues λ2[1], λ2[2], ..., λ2[i], ..., λ2[K] of the second graph Laplacian matrix L2 (λ1[i]−λ2[i]).

Calculating the sum of squares of errors involves calculating the first rank included in a group of eigenvalues λ1[1], λ1[2], ..., λ1[i], ..., λ1[N1] of the first graph Laplacian matrix L1. The eigenvalues λ1[1], λ1[2], ..., λ1[i], ..., λ1[K] up to a predetermined rank are expressed as the eigenvalues λ2[1], λ2[2] of the second graph Laplacian matrix L2, respectively. ], ..., λ2[i], ..., λ2[K] of the same rank.

In subsequent S230, the processor 11 displays the evaluation value E calculated in S220 to the user through the display of the user interface 17. After that, the evaluation process ends.

According to this evaluation process, the user can predict in advance the accuracy of data fusion between the first data set 15A and the second data set 15B based on the displayed evaluation value E.

Specifically, the user can determine that the smaller the displayed evaluation value E, the higher the similarity in data structure between the first data set 15A and the second data set 15B. . The user can determine that the smaller the displayed evaluation value E, the more highly accurate data fusion can be achieved between the first data set 15A and the second data set 15B. Thereby, the user can determine that it is possible to obtain the expanded data set 15C with high information accuracy.

Next, details of the extended processing executed by the processor 11 when the user inputs an instruction to execute extended processing through the user interface 17 will be described with reference to FIGS. 5 and 6. Along with the execution instruction, the user specifies a plurality of data sets through the user interface 17 as candidates for the second data set 15B to be combined with the first data set 15A. The plurality of data sets may be data sets relating to different sets, or data sets having different described characteristics.

In the expansion process, among these multiple data sets, the data set with the smallest evaluation value E calculated using the same method as the evaluation process described above is selected as the second data set 15B to be combined. The selected second data set 15B is combined with the first data set 15A by data fusion.

When the expansion process starts, the processor 11 reads the first data set 15A designated by the user through the user interface 17 from the storage 15, similar to the process at S110. The processor 11 generates a feature vector x for each first entity based on the read first data set 15A (S310). Furthermore, the processor 11 acquires a plurality of data sets designated as candidates for the second data set 15B to be combined by reading them from the storage 15 (S320).

Thereafter, the processor 11 executes the processes of S330 to S360 in the same way as the processes of S130 to S160. That is, in S330, the processor 11 calculates the similarity R1 between the first entities.

In S340, the processor 11 calculates a first graph Laplacian matrix L1 based on the similarity R1 between the first entities. In subsequent S350, the processor 11 calculates the eigenvalues λ1[1], λ1[2], ..., λ1[i], ..., λ1[N1] of the first graph Laplacian matrix L1. The value N1 is the number of unique values. The eigenvalues λ1[1], λ1[2],..., λ1[i],..., λ1[N1] satisfy the conditional expression λ1[1]≧λ1[2]≧...≧λ1[N1].

In subsequent S360, the processor 11 determines, when adding the eigenvalues λ1[1], λ1[2], ..., λ1[i], ..., λ1[N1] in descending order, the eigenvalue exceeds a predetermined proportion α of the total sum. Determine the rank K1 of λ1[K1]. α may for example have the value 0.9.

In subsequent S370, the processor 11 selects one dataset to be evaluated from among the plurality of candidate datasets. In subsequent S380, the processor 11 generates a feature vector y of the corresponding entity for each entity based on the dataset to be evaluated.

The entity here is an element in the sample set of information handled by the dataset to be evaluated. A sample set may correspond to a consumer set. An entity may be each of a plurality of consumers included in a consumer set.

The dataset to be evaluated includes, for each entity, feature data that describes the characteristics of the corresponding entity. Generation of the feature vector y for each entity in S380 is performed in the same way as the process in S120 regarding the second data set 15B.

In subsequent S390, the processor 11 calculates the similarity R3 between entities included in the sample set handled by the evaluation target dataset based on the feature vector y, similar to the process in S170.

For all possible combinations of two entities in the sample set, the processor 11 calculates, for each combination, the similarity R3 between the two entities that make up the combination using the feature vector y. Similarity R3 may be a cosine similarity.

In the subsequent step 400, the processor 11 calculates a graph Laplacian matrix L3 based on the similarity R3 between entities, similar to the process in S180.

The graph Laplacian matrix L3 is a graph Laplacian matrix of the nearest neighbor graph of the dataset to be evaluated. In the nearest neighbor graph, one of the multiple entities in the sample set is selected as the processing target entity, and from the node of the selected processing target entity, the similarity R3 between the selected processing target entity and the processing target entity is highest. It may be a directed graph defined by executing a procedure for extending links (in other words, directed edges) toward a node of one entity for all of the plurality of entities in the sample set.

In subsequent S410, the processor 11 calculates the eigenvalues λ3[1], λ3[2], ..., λ3[i], ..., λ3[N3] of the graph Laplacian matrix L3, similarly to the process in S190. The value N3 is the number of eigenvalues, and the eigenvalues λ3[1], λ3[2], ..., λ3[i], ..., λ3[N3] satisfy the conditional expression λ3[1]≧λ3[2]≧...≧ λ3[N3] is satisfied.

In subsequent S420, similarly to the process in S200, the processor 11 determines that when the eigenvalues λ3[1], λ3[2], ..., λ3[i], ..., λ3[N3] are added in descending order, the total The rank K3 of the eigenvalue λ3 [K3] exceeding a predetermined percentage α of the total is determined. α may for example have the value 0.9.

In subsequent S430, the processor 11 sets the smaller value min{K1, K3} of the values K1 and K3 to the value K.

In subsequent S440, the processor 11 calculates the sum of squares of the errors of the eigenvalues according to the following equation as the evaluation value E regarding the similarity of the data structure between the first data set 15A and the data set to be evaluated.

In subsequent S450, the processor 11 determines whether the processes of S370 to S440 have been executed for all of the plurality of data sets designated as candidates. If it is determined that it has not been executed (No in S450), the processor 11 selects one new data set that has not been selected as an evaluation target from among the candidates as a data set to be evaluated (S370). The processes of S380 to S440 are executed for the newly selected data set to be evaluated.

In this way, the processor 11 makes a negative determination in S450 and repeatedly executes the processes of S370 to S440 until the processes of S370 to S440 are executed for all of the plurality of data sets designated as candidates. As a result, an evaluation value E is calculated for each data set with respect to a plurality of data sets designated as candidates.

When determining that the processes of S370 to S440 have been executed for all of the plurality of data sets (Yes in S450), the processor 11 selects the data set with the smallest evaluation value E from among the plurality of data sets specified as candidates. The data set is determined as having the highest similarity in data structure to the first data set 15A (S460).

Then, the processor 11 selects the dataset with the smallest evaluation value E from among the plurality of datasets designated as candidates as the second dataset 15B to be combined with the first dataset 15A (S460). .

Thereafter, the processor 11 combines the first data set 15A with the second data set 15B using data fusion technology, thereby converting the first data set 15A into the selected second data set 15B. An expanded data set 15C is generated using the extended data set 15C (S470).

The combination between the first data set 15A and the second data set 15B is performed by combining the feature data of the first entity and the feature data of the second entity involved. Combining two pieces of feature data corresponds to associating the two pieces of feature data.

According to the first example, feature data of a first entity and feature data of a second entity that have similar features are combined. According to the second example, the relative positions are similar based on the relative position of each first entity in the first set and the relative position of each second entity in the second set on the feature space. Feature data of the first entity and feature data of the second entity are combined.

After that, the processor 11 outputs the generated extended data set 15C (S480). Specifically, the processor 11 writes the extended data set 15C to the storage 15. The extended data set 15C written to the storage 15 is useful for analyzing consumer behavior, for example.

Here, data fusion technology will be additionally explained. Applicants have already disclosed several data fusion techniques through prior patent applications. When the first data set 15A and the second data set 15B include variables common between the first entity and the second entity, such as demographic attributes, the processor 11 determines whether the data is determined based on the common variable. The first data set 15A and the second data set 15B can be combined so that the feature data of the first entity and the feature data of the second entity that have similar features are combined.

Alternatively, if a common variable is not included between the first data set 15A and the second data set 15B, the processor 11 determines the relative position of the first entity in the first set on the feature space; The relative position of the second entity in the second set on the feature space and the combination of the similar first entity and second entity are determined, and the features of the first entity having similar relative positions are determined. A second data set 15B can be combined with the first data set 15A to combine the data with the second entity's feature data.

According to the information processing system 1 of the present embodiment described above, the similarity of data structures between data sets is evaluated based on the eigenvalues of the graph Laplacian matrix based on the nearest neighbor graph.

The nearest neighbor graph corresponds to the data structure of the corresponding dataset. The nearest neighbor graph relates to the distribution on the feature space of multiple elements that make up the set. When two neighborhood graphs are similar, the groups of eigenvalues of the two corresponding graph Laplacian matrices are also similar.

Therefore, by comparing the eigenvalues, it is possible to evaluate the similarity between the nearest neighbor graph for the first set and the nearest neighbor graph for the second set. As a result, it is possible to evaluate the similarity between the data structure of the first data set 15A and the data structure of the second data set 15B.

This evaluation helps in selecting datasets to be combined in data fusion. By combining the first data set 15A with the second data set 15B, which has a highly similar data structure, by data fusion technology, an expanded data set 15C with high accuracy regarding the expanded information is generated. Can be done.

In other words, it is better to combine two datasets with similar data structures than to combine two datasets with significantly different data structures to better combine feature data between entities in the entire dataset. can.

In particular, in this embodiment, based on the idea that the larger the eigenvalue is, the more important it is to evaluate the data structure, and furthermore, the larger the ratio of the sum of eigenvalues to the total eigenvalue, the more each value of the eigenvalues corresponding to the sum becomes The values K1, K2, and K3 are calculated and the value K is determined based on the idea that they represent the entire eigenvalues corresponding to the sum.

Then, as described above, the evaluation value E is calculated from the sum of squares of the K errors. That is, according to the method for calculating the evaluation value E of this embodiment, even if the number of eigenvalues differs between the datasets to be compared, the eigenvalues can be compared and the evaluation value E regarding the similarity of data structures can be appropriately calculated. can do. Therefore, according to the present embodiment, it is possible to achieve good evaluation regarding the similarity of data structures and good data fusion based on this evaluation.

[Other embodiments]
The present disclosure is not limited to the above embodiments, and can take various forms. For example, the graph Laplacian matrix may be a graph Laplacian matrix of a k-nearest neighbor graph. For example, the first graph Laplacian matrix L1 assigns each node of the first entity in the first set to one or more k first entity nodes in the first set in descending order of similarity R1. It may be a graph Laplacian matrix corresponding to a k-nearest neighbor graph connected to .

The second graph Laplacian matrix L2 connects each node of the second entity in the second set with one or more nodes of k second entities in the second set in descending order of similarity R2. It may be a graph Laplacian matrix corresponding to the k-nearest neighbor graph. The k-nearest neighbor graph may be a directed graph or an undirected graph. Similarly, the graph Laplacian matrix L3 may be a k-nearest neighborhood graph.

In the embodiment described above, comparison of neighborhood graphs is performed through comparison of eigenvalues of graph Laplacian matrices. However, comparison of neighborhood graphs is not limited to this example. The structures of the neighborhood graphs may be expressed numerically using any method, and the structures of the neighborhood graphs may be compared by comparing the numerical values corresponding to the two approximate graphs. By comparing the structures of such neighborhood graphs, the similarity of the data structures of two corresponding data sets may be evaluated. The numerical value here may include a vector.

The function that one component in the above embodiment has may be distributed and provided to multiple components. Functions possessed by multiple components may be integrated into one component. A part of the configuration of the above embodiment may be omitted. At least a part of the configuration of the embodiment described above may be added to or replaced with the configuration of other embodiments described above. All aspects included in the technical idea specified from the words described in the claims are embodiments of the present disclosure.

[Technical idea disclosed in this specification]
It can be understood that the following technical ideas are disclosed in this specification.
[Item 1]
a first acquisition unit configured to acquire, with respect to a plurality of first elements in a first set, a first data set comprising data describing characteristics of each of the plurality of first elements;
a second acquisition unit configured to acquire, with respect to a plurality of second elements in a second set, a second data set comprising data describing characteristics of each of the plurality of second elements;
a neighborhood graph of the first set based on the similarity between the plurality of first elements determined from the first data set; and the plurality of second elements determined from the second data set. and a neighborhood graph of the second set based on the similarity between the first dataset and the second dataset. an evaluation department to be
An information processing system equipped with.
[Item 2]
a first acquisition unit configured to acquire, with respect to a plurality of first elements in a first set, a first data set comprising data describing characteristics of each of the plurality of first elements;
a second acquisition unit configured to acquire, with respect to a plurality of second elements in a second set, a second data set comprising data describing characteristics of each of the plurality of second elements;
a first similarity calculation unit configured to calculate the similarity between the plurality of first elements based on the first data set;
a second similarity calculation unit configured to calculate the similarity between the plurality of second elements based on the second data set;
Based on the degree of similarity between the plurality of first elements, in the neighborhood graph, each of the plurality of first elements is divided into a group in the first set whose high degree of similarity satisfies a predetermined condition. a first eigenvalue calculation unit configured to calculate a group of eigenvalues of a first graph Laplacian matrix corresponding to the neighborhood graph connected to the first element as a group of first eigenvalues;
Based on the degree of similarity between the plurality of second elements, in the neighborhood graph, each of the plurality of second elements is selected from among the second set whose degree of similarity satisfies the predetermined condition. a second eigenvalue calculation unit configured to calculate a group of eigenvalues of a second graph Laplacian matrix corresponding to a neighborhood graph connected to one or more second elements as a group of second eigenvalues;
evaluating similarity in data structure between the first data set and the second data set based on a comparison between the first set of eigenvalues and the second set of eigenvalues; an evaluation section consisting of;
An information processing system equipped with.
[Item 3]
The evaluation unit may rank each of the plurality of first eigenvalues included in the first eigenvalue group based on the size of the eigenvalue, and Based on the rank of each of the plurality of second eigenvalues included in the group of second eigenvalues, each of the plurality of first eigenvalues is assigned to the second eigenvalue of the plurality of second eigenvalues. The information processing system according to item 2, wherein similarity regarding the data structure is evaluated by comparing with eigenvalues of the same rank.
[Item 4]
The evaluation unit may compare each of the eigenvalues from the first to a predetermined rank in descending order of the eigenvalues included in the group of first eigenvalues with an eigenvalue of the same rank among the plurality of second eigenvalues. The information processing system according to item 3, wherein the similarity regarding the data structure is evaluated by:
[Item 5]
The evaluation unit is configured to calculate an evaluation value of similarity regarding the data structure by a sum of squares of errors, and each of the errors is calculated by calculating the first eigenvalue of the corresponding rank among the plurality of first eigenvalues. The information processing system according to item 3 or 4, which is a difference between the eigenvalue of the eigenvalue and the second eigenvalue of the corresponding rank among the plurality of second eigenvalues.
[Item 6]
The first graph Laplacian matrix is a graph Laplacian matrix of a nearest neighbor graph in which each of the plurality of first elements is connected to the first element having the highest degree of similarity in the first set,
The second graph Laplacian matrix is a graph Laplacian matrix of a nearest neighbor graph in which each of the plurality of second elements is connected to the second element having the highest degree of similarity in the second set. The information processing system according to any one of item 5.
[Item 7]
The second acquisition unit acquires a plurality of evaluation target datasets as the second dataset,
Each of the plurality of evaluation target data sets is a data set including data describing characteristics of each of the plurality of elements with respect to the plurality of elements in the corresponding set,
The plurality of data sets to be evaluated are data sets related to different sets, or data sets with different described characteristics,
The second similarity calculation unit calculates the similarity between the plurality of elements in the corresponding set for each of the plurality of evaluation target data sets,
The second eigenvalue calculation unit calculates, for each of the plurality of evaluation target data sets, a neighborhood graph in which each of the plurality of elements in the corresponding set has a high degree of similarity that satisfies the predetermined condition. calculating a group of eigenvalues of a graph Laplacian matrix corresponding to a neighborhood graph connected to one or more elements in the corresponding corresponding set as a group of eigenvalues to be compared;
The evaluation unit, for each of the plurality of evaluation target data sets, based on a comparison between the group of comparison target eigenvalues based on the corresponding evaluation target data set and the first group of eigenvalues, The information processing system according to any one of items 2 to 6, wherein similarity regarding the data structure between the first data set and the corresponding evaluation target data set is evaluated.
[Item 8]
a selection unit configured to select, as a combination target, a data set with the highest evaluation of similarity regarding the data structure among the plurality of evaluation target data sets;
The first data set and the second data set selected to be combined are associated with data describing characteristics of similar elements between the first set and the corresponding set. a coupling portion configured to couple to;
The information processing system according to item 7, comprising:
[Item 9]
The first data set is a data set that describes characteristics of a plurality of people in the first set as a first element of the plurality, and the second data set is a data set that describes the characteristics of a plurality of people in the first set. The information processing system according to any one of items 1 to 8, wherein the element is a data set that describes characteristics of a plurality of people in the second set.
[Item 10]
A computer program for causing a computer to function as the first acquisition unit, the second acquisition unit, and the evaluation unit in the information processing system according to item 1.
[Item 11]
In the information processing system according to any one of items 2 to 7, the first acquisition unit, the second acquisition unit, the first similarity calculation unit, the second similarity calculation unit, the first A computer program for causing a computer to function as the first eigenvalue calculation section, the second eigenvalue calculation section, and the evaluation section.
[Item 12]
An information processing method performed by a computer, the method comprising:
obtaining, with respect to a plurality of first elements in a first set, a first data set that includes data describing characteristics of each of the plurality of first elements;
obtaining, with respect to a plurality of second elements in a second set, a second data set that includes data describing characteristics of each of the plurality of second elements;
a neighborhood graph of the first set based on the similarity between the plurality of first elements determined from the first data set; and the plurality of second elements determined from the second data set. and a neighborhood graph of the second set based on the similarity between the first data set and the second data set.
Information processing methods including.
[Item 13]
An information processing method performed by a computer, the method comprising:
obtaining, with respect to a plurality of first elements in a first set, a first data set that includes data describing characteristics of each of the plurality of first elements;
obtaining, with respect to a plurality of second elements in a second set, a second data set that includes data describing characteristics of each of the plurality of second elements;
Calculating the degree of similarity between the plurality of first elements based on the first data set;
Calculating the degree of similarity between the plurality of second elements based on the second data set;
Based on the degree of similarity between the plurality of first elements, in the neighborhood graph, each of the plurality of first elements is divided into a group in the first set whose high degree of similarity satisfies a predetermined condition. Calculating a group of eigenvalues of a first graph Laplacian matrix corresponding to the neighborhood graph connected to the first element as a group of first eigenvalues;
Based on the degree of similarity between the plurality of second elements, in the neighborhood graph, each of the plurality of second elements is selected from among the second set whose degree of similarity satisfies the predetermined condition. calculating a group of eigenvalues of a second graph Laplacian matrix corresponding to a neighborhood graph connected to one or more second elements as a group of second eigenvalues;
Evaluating similarity in data structure between the first data set and the second data set based on a comparison between the first set of eigenvalues and the second set of eigenvalues; ,
Information processing methods including.

DESCRIPTION OF SYMBOLS 1... Information processing system, 11... Processor, 13... Memory, 15... Storage, 17... User interface, 19... Communication interface, Pr... Computer program.

Claims (13)

a first acquisition unit configured to acquire, with respect to a plurality of first elements in a first set, a first data set comprising data describing characteristics of each of the plurality of first elements;
a second acquisition unit configured to acquire, with respect to a plurality of second elements in a second set, a second data set comprising data describing characteristics of each of the plurality of second elements;
a neighborhood graph of the first set based on the similarity between the plurality of first elements determined from the first data set; and the plurality of second elements determined from the second data set. and a neighborhood graph of the second set based on the similarity between the first dataset and the second dataset. an evaluation department to be
An information processing system equipped with. a first acquisition unit configured to acquire, with respect to a plurality of first elements in a first set, a first data set comprising data describing characteristics of each of the plurality of first elements;
a second acquisition unit configured to acquire, with respect to a plurality of second elements in a second set, a second data set comprising data describing characteristics of each of the plurality of second elements;
a first similarity calculation unit configured to calculate the similarity between the plurality of first elements based on the first data set;
a second similarity calculation unit configured to calculate the similarity between the plurality of second elements based on the second data set;
Based on the degree of similarity between the plurality of first elements, in the neighborhood graph, each of the plurality of first elements is divided into a group in the first set whose high degree of similarity satisfies a predetermined condition. a first eigenvalue calculation unit configured to calculate a group of eigenvalues of a first graph Laplacian matrix corresponding to the neighborhood graph connected to the first element as a group of first eigenvalues;
Based on the degree of similarity between the plurality of second elements, in the neighborhood graph, each of the plurality of second elements is selected from among the second set whose degree of similarity satisfies the predetermined condition. a second eigenvalue calculation unit configured to calculate a group of eigenvalues of a second graph Laplacian matrix corresponding to a neighborhood graph connected to one or more second elements as a group of second eigenvalues;
evaluating similarity in data structure between the first data set and the second data set based on a comparison between the first set of eigenvalues and the second set of eigenvalues; An evaluation section consisting of;
An information processing system equipped with. The evaluation unit may rank each of the plurality of first eigenvalues included in the first eigenvalue group based on the size of the eigenvalue, and Based on the rank of each of the plurality of second eigenvalues included in the group of second eigenvalues in the group of second eigenvalues, each of the plurality of first eigenvalues is assigned to the second eigenvalue of the plurality of second eigenvalues. 3. The information processing system according to claim 2, wherein similarity regarding the data structure is evaluated by comparing with eigenvalues of the same rank. The evaluation unit may compare each of the eigenvalues from the first to a predetermined rank in descending order of the eigenvalues included in the group of first eigenvalues with an eigenvalue of the same rank among the plurality of second eigenvalues. 4. The information processing system according to claim 3, wherein the similarity regarding the data structure is evaluated by: The evaluation unit is configured to calculate an evaluation value of similarity regarding the data structure by a sum of squares of errors, and each of the errors is calculated by calculating the first eigenvalue of the corresponding rank among the plurality of first eigenvalues. 4. The information processing system according to claim 3, wherein the difference is the difference between the eigenvalue of the second eigenvalue and the second eigenvalue of the corresponding rank among the plurality of second eigenvalues. The first graph Laplacian matrix is a graph Laplacian matrix of a nearest neighbor graph in which each of the plurality of first elements is connected to the first element having the highest degree of similarity in the first set,
2. The second graph Laplacian matrix is a graph Laplacian matrix of a nearest neighbor graph in which each of the plurality of second elements is connected to a second element having the highest degree of similarity in the second set. The information processing system described. The second acquisition unit acquires a plurality of evaluation target datasets as the second dataset,
Each of the plurality of evaluation target data sets is a data set including data describing characteristics of each of the plurality of elements with respect to the plurality of elements in the corresponding set,
The plurality of data sets to be evaluated are data sets related to different sets, or data sets with different described characteristics,
The second similarity calculation unit calculates the similarity between the plurality of elements in the corresponding set for each of the plurality of evaluation target data sets,
The second eigenvalue calculation unit calculates, for each of the plurality of evaluation target data sets, a neighborhood graph in which each of the plurality of elements in the corresponding set has a high degree of similarity that satisfies the predetermined condition. calculating a group of eigenvalues of a graph Laplacian matrix corresponding to a neighborhood graph connected to one or more elements in the corresponding corresponding set as a group of eigenvalues to be compared;
The evaluation unit, for each of the plurality of evaluation target data sets, based on a comparison between the group of comparison target eigenvalues based on the corresponding evaluation target data set and the first group of eigenvalues, The information processing system according to claim 2, wherein similarity regarding the data structure between the first data set and the corresponding evaluation target data set is evaluated. a selection unit configured to select, as a combination target, a data set with the highest evaluation of similarity regarding the data structure among the plurality of evaluation target data sets;
The first data set and the second data set selected to be combined are associated with data describing characteristics of similar elements between the first set and the corresponding set. a coupling portion configured to couple to;
The information processing system according to claim 7, comprising: The first data set is a data set that describes characteristics of a plurality of people in the first set as a first element of the plurality, and the second data set is a data set that describes the characteristics of a plurality of people in the first set. The information processing system according to any one of claims 1 to 8, wherein the elements are data sets that describe characteristics of a plurality of people in the second set. A computer program for causing a computer to function as the first acquisition section, the second acquisition section, and the evaluation section in the information processing system according to claim 1. In the information processing system according to any one of claims 2 to 7, the first acquisition unit, the second acquisition unit, the first similarity calculation unit, the second similarity calculation unit, A computer program for causing a computer to function as the first eigenvalue calculation section, the second eigenvalue calculation section, and the evaluation section. An information processing method performed by a computer, the method comprising:
obtaining, with respect to a plurality of first elements in a first set, a first data set that includes data describing characteristics of each of the plurality of first elements;
obtaining, with respect to a plurality of second elements in a second set, a second data set that includes data describing characteristics of each of the plurality of second elements;
a neighborhood graph of the first set based on the similarity between the plurality of first elements determined from the first data set; and the plurality of second elements determined from the second data set. and a neighborhood graph of the second set based on the similarity between the first data set and the second data set.
Information processing methods including. An information processing method performed by a computer, the method comprising:
obtaining, with respect to a plurality of first elements in a first set, a first data set that includes data describing characteristics of each of the plurality of first elements;
obtaining, with respect to a plurality of second elements in a second set, a second data set that includes data describing characteristics of each of the plurality of second elements;
Calculating the degree of similarity between the plurality of first elements based on the first data set;
Calculating the degree of similarity between the plurality of second elements based on the second data set;
Based on the degree of similarity between the plurality of first elements, in the neighborhood graph, each of the plurality of first elements is divided into a group in the first set whose high degree of similarity satisfies a predetermined condition. Calculating a group of eigenvalues of a first graph Laplacian matrix corresponding to the neighborhood graph connected to the first element as a group of first eigenvalues;
Based on the degree of similarity between the plurality of second elements, in the neighborhood graph, each of the plurality of second elements is selected from among the second set whose degree of similarity satisfies the predetermined condition. calculating a group of eigenvalues of a second graph Laplacian matrix corresponding to a neighborhood graph connected to one or more second elements as a group of second eigenvalues;
Evaluating similarity in data structure between the first data set and the second data set based on a comparison between the first set of eigenvalues and the second set of eigenvalues; ,
Information processing methods including.

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