CN115408621A - Interest point recommendation method considering linear and nonlinear interaction of auxiliary information features - Google Patents

Interest point recommendation method considering linear and nonlinear interaction of auxiliary information features Download PDF

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CN115408621A
CN115408621A CN202210968531.4A CN202210968531A CN115408621A CN 115408621 A CN115408621 A CN 115408621A CN 202210968531 A CN202210968531 A CN 202210968531A CN 115408621 A CN115408621 A CN 115408621A
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李晓燕
徐胜华
姜涛
刘纪平
王勇
罗安
车向红
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Chinese Academy of Surveying and Mapping
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Abstract

The invention relates to the technical field of computers, in particular to a point-of-interest recommendation method considering auxiliary information feature linear and nonlinear interaction, which comprises POI auxiliary information, user auxiliary information, auxiliary information construction based on a convolutional attention mechanism and a neural matrix decomposition model. And learning linear and nonlinear interaction relations between the user and potential feature vectors of the POI by using a neural matrix decomposition model, and calculating a preference score of the user to the POI.

Description

Interest point recommendation method considering auxiliary information characteristic linear and nonlinear interaction
Technical Field
The invention relates to the technical field of computers, in particular to a point of interest recommendation method considering linear and nonlinear interaction of auxiliary information features.
Background
Point of interest (POI) recommendations are becoming a research focus in the fields of geographic information systems, web information retrieval, and the like, as one of important services of location-based social networks. POI recommendation is various life patterns and personal preferences of a user hidden after large-scale data mining, so as to help the user to find a most interesting personalized place, and further enrich the life experience of the user. Meanwhile, related service providers are helped to provide intelligent, precise and personalized services for potential users, and the use viscosity of the users on the social network service platform based on the position is greatly enhanced, so that the economic benefit is improved.
However, in the prior art, a single matrix decomposition or a deep neural network is mostly used, which cannot effectively capture a complex structure of interaction between a user and a POI, and cannot effectively deal with the implicit feedback problem. In addition, in order to alleviate the problem of data sparseness, the current technology mainly introduces auxiliary information of users and POIs, and the auxiliary information is often judged to have the same value, which enables some valuable information to be resolved.
Therefore, in order to solve the above problems, the present application provides a point of interest recommendation method considering the linear and nonlinear interaction of auxiliary information features, which utilizes a position clustering and TF-IDE algorithm to mine and construct auxiliary information of a user and a POI from user sign-in data, enriches the expression of potential feature vectors of the user and the POI, learns potential representations from the auxiliary information of the user and the POI by utilizing a convolutional neural network, introduces an attention mechanism to distinguish the importance of the constructed auxiliary information of the user and the POI, constructs a neural matrix decomposition model, learns the linear and nonlinear interaction between the user and the POI, and calculates a preference score of the user to the POI.
Disclosure of Invention
The invention aims to fill the blank of the prior art, provides a point-of-interest recommendation method considering the linear and nonlinear interaction of auxiliary information features, utilizes a position clustering and TF-IDE algorithm to mine and construct auxiliary information of a user and a POI from user sign-in data, enriches the expression of potential feature vectors of the user and the POI, utilizes a convolutional neural network to learn potential representation from the auxiliary information of the user and the POI, introduces an attention mechanism to distinguish the importance of the constructed auxiliary information of the user and the POI, constructs a neural matrix decomposition model, learns the linear and nonlinear interaction between the user and the POI, and calculates the preference score of the user on the POI. In order to achieve the purpose, the invention provides a point of interest recommendation method considering the linear and nonlinear interaction of auxiliary information characteristics, which mainly comprises POI auxiliary information, user auxiliary information, auxiliary information construction based on a convolution attention mechanism and a neural matrix decomposition model;
the POI auxiliary information comprises a POI category, a POI popularity and a POI belonging area;
the POI category is inherent information;
the POI popularity is the popularity of the POI to the user, and the check-in data in the POI is evaluated through check-in data set, wherein the check-in data not only contains information about inherent attributes of the POI, but also contains the check-in times of the POI;
POI popularity is calculated by TF-IDE algorithm based on check-in data, formula as follows:
Figure RE-GDA0003921812620000021
sizeof(v i ) Denotes POIv i Number of check-ins of (Sv) i ) Is represented by v i Check-in times, sizeof (Cv), for all POIs of the same class i ) Is represented by the formula i The number of all POIs in the same category, n represents the number of interest points;
the method comprises the following steps of clustering POI (point of interest) by utilizing a position clustering algorithm according to position information of the POI to obtain a proper region block, and enabling each POI to be allocated with a position label of a corresponding region, wherein the processing steps are as follows:
s1, randomly selecting k POIs from a POI set to serve as initial clustering centers;
s2, calculating the Euclidean distance rho from the residual POI to the clustering center, and putting the closest interest points into corresponding categories to form new categories, wherein the rho calculation method comprises the following steps:
Figure RE-GDA0003921812620000031
lat i 、lat j respectively represent POIv i 、v j Latitude information of (lo) i 、lon j Respectively represent POIv i 、v j Longitude information of (a);
s3, taking the mean value of the longitude and latitude of all POI in the current cluster as a new central point, and updating the POI closest to the cluster center;
s4, until the target function is converged or the clustering center is not changed, otherwise, transferring to the step S2;
s5, outputting a POI clustering result;
the user auxiliary information comprises user favorite categories and user high activity positions, wherein the user favorite categories refer to POI categories which are frequently signed in by a user;
the auxiliary information based on the convolution attention mechanism is constructed as follows:
the user ID and the interest point ID are subjected to unique heat coding, in order to apply auxiliary attribute information of the user and the POI in the model, the numerical type variable is subjected to normalization processing, and the category type variable is subjected to unique heat coding processing. User potential feature vector U u POI latent feature vector V v User auxiliary information U a And POI auxiliary information V a Are fed into the embedding layer, U, respectively u 、V v 、U a And V a The potential feature vector of (a) is calculated as follows:
Figure RE-GDA0003921812620000032
f. g, h and l are representational functions;
learning potential representations from user and POI assistance information using a convolutional neural network, which consists of convolutional and pooling layers that can extract deeper level features, and is performed as follows:
Figure RE-GDA0003921812620000041
* Is a convolution operator, w is a filter, b is the bias of w, g is a nonlinear activation function, and posing is a pooling function;
the importance of user and POI assistance information is distinguished by adding an attention mechanism that uses the softmax (g) function to compute a score as a probability distribution of assistance information output, and the output is summed with
Figure RE-GDA0003921812620000042
Figure RE-GDA0003921812620000043
In combination, by element multiplication
Figure RE-GDA0003921812620000044
The final output of the attention mechanism is obtained, and the attention mechanism is shown as the following formula:
Figure RE-GDA0003921812620000045
the softmax (g) function is defined as:
Figure RE-GDA0003921812620000046
u jc representing one of the attributes;
user feature vector
Figure RE-GDA0003921812620000047
And user assistance information feature vector
Figure RE-GDA0003921812620000048
Fusing to obtain a complete user feature vector H U The POI feature vector
Figure RE-GDA0003921812620000049
And POI assistance information feature vector
Figure RE-GDA00039218126200000410
Fusing to obtain complete POI characteristic vector G V As shown in the following formula:
Figure RE-GDA00039218126200000411
the neural matrix decomposition model is:
after obtaining the complete potential feature vectors of the user and the POI, the generalized matrix decomposition simulates the potential feature interaction between the user and the POI by using a linear kernel, the multi-layer perception machine learns the interaction function between the user and the POI by using a non-linear kernel, in order to effectively integrate the generalized matrix decomposition and the multi-layer perception machine, the generalized matrix decomposition and the multi-layer perception machine can be mutually enhanced, the complex interaction between the user and the POI is learned, the generalized matrix decomposition and the multi-layer perception machine are independently embedded, and the two models are combined by connecting a last hidden layer, wherein the formula is as follows:
Figure RE-GDA0003921812620000051
W x 、b x and a x Respectively representing the weight matrix, the offset vector and the activation function of the x-th layer perceptron, h representing the edge weight of the output layer,
Figure RE-GDA0003921812620000052
and
Figure RE-GDA0003921812620000053
respectively represent the generalized matrix decomposition and the complete user characteristic vector of the multi-layer perceptron,
Figure RE-GDA0003921812620000054
and
Figure RE-GDA0003921812620000055
respectively representing POI characteristic vectors of generalized matrix decomposition and multi-layer perceptron integrity, sigma representing sigmoid activation function, e representing element product of vectors, phi MF-CAA And phi MLP-CAA Respectively representing the prediction vectors processed by the generalized matrix decomposition and the multi-layer perceptron,
Figure RE-GDA0003921812620000056
representing the predicted scores, the model combines the linear of the generalized matrix decomposition and the non-linear of the multi-layer perceptron to model the underlying structure between the user and the POI. The POI inherent attribute information includes longitude, latitude, category, and other information.
The user high activity location is the user's living area.
Compared with the prior art, the method and the device have the advantages that the auxiliary information of the user and the POI is constructed from the sign-in data by using the position clustering and TF-IDE algorithm, the potential representation is learned from the auxiliary information of the user and the POI by using the convolutional neural network, and the importance of the auxiliary information is distinguished by introducing an attention mechanism, so that the expression of the potential feature vectors of the user and the POI is enriched. And learning the interaction relation between the user and the potential feature vector of the POI by using a neural matrix decomposition model, and calculating the preference score of the user to the POI.
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FIG. 1 is a schematic diagram of a process framework of the present invention;
Detailed Description
The invention will now be further described with reference to the accompanying drawings.
Referring to fig. 1, the invention discloses a point of interest recommendation method considering the linear and nonlinear interaction of auxiliary information features, comprising the following steps:
as shown in FIG. 1, firstly, the user and POI auxiliary information is constructed from the check-in data by using a position clustering and TF-IDE algorithm, potential representations are learned from the user and POI auxiliary information by using a convolutional neural network, and an attention mechanism is introduced to distinguish the importance of the auxiliary information, so that the expression of potential feature vectors of the user and the POI is enriched. And learning the interaction relation between the user and the potential feature vector of the POI by using a neural matrix decomposition model, and calculating the preference score of the user to the POI.
The method mainly comprises POI auxiliary information, user auxiliary information, auxiliary information construction based on a convolution attention mechanism and a neural matrix decomposition model;
the POI auxiliary information comprises a POI category, a POI popularity and a POI belonging area;
the POI category is inherent information;
the POI popularity is the popularity of the POI to the user, and the check-in data in the POI is evaluated through check-in data set, wherein the check-in data not only contains information about inherent attributes of the POI, but also contains the check-in times of the POI;
it is not sufficient to use the number of visitors to the POI directly for calculation of the popularity of the POI. Research indicates that the category information of the POI plays an important role in the point of interest recommendation process, because the category information of the POI can be used to characterize the POI.
POI popularity is calculated by TF-IDE algorithm based on check-in data, formula as follows:
Figure RE-GDA0003921812620000071
sizeof(v i ) Denotes POIv i Number of check-ins of (Sv) i ) Is represented by v i Check-in times, sizeof (Cv), for all POIs of the same class i ) Is represented by v i The number of all POIs in the same category, n represents the number of interest points;
the method comprises the following steps that position information of an area to which a POI belongs is basic attributes of the POI and is also a key factor which must be considered by a POI recommendation algorithm, the POI is clustered according to the position information of the POI by utilizing a position clustering algorithm to obtain a proper area block, so that each POI is assigned with a position label of the corresponding area, and the processing steps are as follows:
s1, randomly selecting k POIs from a POI set as an initial clustering center;
s2, calculating the Euclidean distance rho from the residual POI to the clustering center, and putting the closest interest points into corresponding categories to form new categories, wherein the rho calculation method comprises the following steps:
Figure RE-GDA0003921812620000072
lat i 、lat j respectively represent POIv i 、v j Latitude information of (lo) lon i 、lon j Respectively represent POIv i 、v j Longitude information of (a);
s3, taking the mean value of longitude and latitude of all POI in the current cluster as a new central point, and updating the POI closest to the cluster center;
s4, until the objective function is converged or the clustering center is unchanged, otherwise, transferring to the step S2;
s5, outputting a POI clustering result;
the user assistance information includes categories of user preferences, and a user's high activity location (area) constitutes the user assistance information. Where the categories of user preferences refer to the POI categories that the user most frequently signs in. In the real world, the user's high activity location may be the user's living area. Thus, the POIs most frequently checked in by the user are used to infer a high activity location for the user.
The auxiliary information based on the convolution attention mechanism is constructed as follows:
carrying out unique hot coding on the user ID and the interest point ID, carrying out normalization processing on numerical variables in order to apply auxiliary attribute information of the user and the POI in a model, carrying out unique hot coding processing on category variables, and carrying out unique hot coding on the user potential feature vector U u POI latent feature vector V v User auxiliary information U a And POI auxiliary information V a Are fed into the embedding layer, U, respectively u 、V v 、U a And V a The potential feature vector of (a) is calculated as follows:
Figure RE-GDA0003921812620000081
f. g, h and l are representational functions;
because of the widespread use and good performance of convolutional neural networks, learning potential representations from user and POI assistance information using convolutional neural networks, which consist of convolutional and pooling layers that can extract deeper level features, is performed as follows:
Figure RE-GDA0003921812620000082
* For convolution operators, w is the filter, b is the bias of w, g is the nonlinear activation function, posing is the pooling function (e.g., maximum pooling or average pooling)
The importance of user and POI assistance information is distinguished by adding an attention mechanism that uses the softmax (g) function to compute a score as a probability distribution of assistance information output, and the output is summed with
Figure RE-GDA0003921812620000083
Figure RE-GDA0003921812620000084
In combination, by element multiplication
Figure RE-GDA0003921812620000085
The final output of the attention mechanism is obtained, and the attention mechanism is shown as follows:
Figure RE-GDA0003921812620000086
the softmax (g) function is defined as:
Figure RE-GDA0003921812620000087
u jc representing one of the attributes;
user feature vector
Figure RE-GDA0003921812620000091
And user assistance information feature vector
Figure RE-GDA0003921812620000092
Fusing to obtain a complete user feature vector H U Feature vector of POI
Figure RE-GDA0003921812620000093
And POI assistance information feature vector
Figure RE-GDA0003921812620000094
Fusing to obtain complete POI characteristic vector G V As shown in the following formula:
Figure RE-GDA0003921812620000095
the neural matrix decomposition model is:
after obtaining the complete potential feature vectors of the user and the POI, the generalized matrix decomposition simulates the potential feature interaction between the user and the POI by using a linear kernel, the multi-layer perception machine learns the interaction function between the user and the POI by using a non-linear kernel, in order to effectively integrate the generalized matrix decomposition and the multi-layer perception machine, the generalized matrix decomposition and the multi-layer perception machine can be mutually enhanced, the complex interaction between the user and the POI is learned, the generalized matrix decomposition and the multi-layer perception machine are independently embedded, and the two models are combined by connecting a last hidden layer, wherein the formula is as follows:
Figure RE-GDA0003921812620000096
W x 、b x and a x Respectively represent the x-th layerWeight matrix, bias vector, activation function of the perceptron, h represents the edge weight of the output layer,
Figure RE-GDA0003921812620000097
and
Figure RE-GDA0003921812620000098
respectively represent the generalized matrix decomposition and the complete user characteristic vector of the multi-layer perceptron,
Figure RE-GDA0003921812620000099
and
Figure RE-GDA00039218126200000910
respectively representing POI characteristic vectors of generalized matrix decomposition and multi-layer perceptron integrity, sigma representing sigmoid activation function, e representing element product of vectors, phi MF-CAA And phi MLP-CAA Respectively representing the prediction vectors processed by the generalized matrix decomposition and the multi-layer perceptron,
Figure RE-GDA00039218126200000911
representing the predicted scores, the model combines the linear of the generalized matrix decomposition and the non-linear of the multi-layer perceptron to model the underlying structure between the user and the POI. The POI inherent attribute information includes longitude, latitude, category, and other information.
The user high activity location is the user's living area.
The above is only a preferred embodiment of the present invention, and is only used to help understand the method and the core idea of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention. The method integrally relieves the problems of data sparseness and implicit feedback in interest point recommendation, the auxiliary information of the user and the POI is mined and constructed from the user sign-in data by utilizing position clustering and a TF-IDE algorithm, the potential representation is learned from the auxiliary information of the user and the POI by utilizing a convolutional neural network, and the importance of the constructed auxiliary information of the user and the POI is distinguished by introducing an attention mechanism so as to relieve the problem of data sparseness; a neural matrix decomposition model is constructed, linear interaction of a user and a POI characteristic vector is captured by using generalized matrix decomposition, nonlinear interaction of the user and the POI characteristic vector is captured by using a multilayer perceptron, and the two parts are fused by connecting the last hidden layer so as to relieve the hidden feedback problem.

Claims (3)

1. The interest point recommendation method considering the linear and nonlinear interaction of the auxiliary information features is characterized by mainly comprising POI auxiliary information, user auxiliary information, auxiliary information construction based on a convolution attention mechanism and a neural matrix decomposition model;
the POI auxiliary information comprises a POI category, a POI popularity and a POI belonging area;
the POI category is intrinsic information;
the POI popularity refers to the popularity of the POI to the user, and is evaluated through check-in data of the user in the POI, wherein the check-in data not only contains information about the inherent attribute of the POI, but also contains the check-in times of the POI;
the POI popularity is calculated through a TF-IDE algorithm based on the check-in data, and the formula is as follows:
Figure RE-FDA0003921812610000011
the sizeof (v) i ) Denotes POIv i Number of check-ins of (Sv) i ) Is represented by the formula i Check-in times, sizeof (Cv), for all POIs of the same class i ) Is represented by the formula i The number of all POIs in the same category, n represents the number of interest points;
the method comprises the following steps of clustering POI (point of interest) by utilizing a position clustering algorithm according to position information of the POI to obtain a proper region block, and enabling each POI to be allocated with a position label of a corresponding region, wherein the processing steps are as follows:
s1, randomly selecting k POIs from a POI set as an initial clustering center;
s2, calculating the Euclidean distance rho from the residual POI to the clustering center, and putting the closest interest points into corresponding categories to form new categories, wherein the rho calculation method comprises the following steps:
Figure RE-FDA0003921812610000012
the lat i 、lat j Respectively represent POIv i 、v j Latitude information of (lo) i 、lon j Respectively represent POIv i 、v j Longitude information of (a);
s3, taking the mean value of longitude and latitude of all POI in the current cluster as a new central point, and updating the POI closest to the cluster center;
s4, until the objective function is converged or the clustering center is unchanged, otherwise, transferring to the step S2;
s5, outputting a POI clustering result;
the user auxiliary information comprises a user favorite category and a user high activity position, wherein the user favorite category refers to a POI category which is frequently signed in by a user;
the auxiliary information based on the convolution attention mechanism is constructed as follows:
carrying out unique hot coding on the user ID and the interest point ID, carrying out normalization processing on numerical variables in order to apply auxiliary attribute information of the user and the POI in a model, carrying out unique hot coding processing on category variables, and carrying out unique hot coding on the user potential feature vector U u POI latent feature vector V v User auxiliary information U a And POI auxiliary information V a Are fed into the embedding layer, U, respectively u 、V v 、U a And V a The potential feature vector of (a) is calculated as follows:
Figure RE-FDA0003921812610000021
the f, g, h and l are representative functions;
learning potential representations from user and POI assistance information using a convolutional neural network, which consists of convolutional and pooling layers that can extract deeper level features, and is performed as follows:
Figure RE-FDA0003921812610000022
the x is a convolution operator, w is a filter, b is the bias of w, g is a nonlinear activation function, and posing is a pooling function;
the importance of user and POI assistance information is distinguished by adding an attention mechanism that uses the softmax (g) function to compute a score as a probability distribution of assistance information output, and the output is summed with
Figure RE-FDA0003921812610000031
In combination, by element multiplication
Figure RE-FDA0003921812610000032
The final output of the attention mechanism is obtained, and the attention mechanism is shown as the following formula:
Figure RE-FDA0003921812610000033
the softmax (g) function is defined as:
Figure RE-FDA0003921812610000034
u jc representing one of the attributes;
user feature vector
Figure RE-FDA0003921812610000035
And user assistance information feature vector
Figure RE-FDA0003921812610000036
Fusing to obtain a complete user feature vector H U Feature vector of POI
Figure RE-FDA0003921812610000037
And POI assistance information feature vector
Figure RE-FDA0003921812610000038
Fusing to obtain complete POI characteristic vector G V As shown in the following formula:
Figure RE-FDA0003921812610000039
the neural matrix decomposition model is as follows:
after obtaining the complete potential feature vectors of the user and the POI, the generalized matrix decomposition simulates the potential feature interaction between the user and the POI by using a linear kernel, the multi-layer perception machine learns the interaction function between the user and the POI by using a non-linear kernel, in order to effectively integrate the generalized matrix decomposition and the multi-layer perception machine, the generalized matrix decomposition and the multi-layer perception machine can be mutually enhanced, the complex interaction between the user and the POI is learned, the generalized matrix decomposition and the multi-layer perception machine are independently embedded, and the two models are combined by connecting a last hidden layer, wherein the formula is as follows:
Figure RE-FDA0003921812610000041
w is x 、b x And a x Respectively representing the weight matrix, the offset vector and the activation function of the x-th layer perceptron, h representing the edge weight of the output layer,
Figure RE-FDA0003921812610000042
and
Figure RE-FDA0003921812610000043
respectively represent the generalized matrix decomposition and the complete user characteristic vector of the multi-layer perceptron,
Figure RE-FDA0003921812610000044
and
Figure RE-FDA0003921812610000045
respectively representing POI characteristic vectors of generalized matrix decomposition and multi-layer perceptron integrity, sigma representing sigmoid activation function, e representing element product of vectors, phi MF-CAA And phi MLP-CAA Respectively representing the prediction vectors processed by the generalized matrix decomposition and the multi-layer perceptron,
Figure RE-FDA0003921812610000046
representing the predicted scores, the model combines the linear of the generalized matrix decomposition and the non-linear of the multi-layer perceptron to model the underlying structure between the user and the POI.
2. The method of claim 1, wherein the POI intrinsic attribute information includes longitude, latitude, category, and other information.
3. The method of point of interest recommendation accounting for auxiliary information feature linear and non-linear interactions as claimed in claim 1, wherein the user high activity location is a residential area of the user.
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CN117390300A (en) * 2023-10-09 2024-01-12 中国测绘科学研究院 Construction method and device of multi-channel interactive learning interest point recommendation model

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