Method, system and device for recommending points of interest based on spatiotemporal context perception

Abstract

The present invention relates to the field of electrical data processing technology, and specifically to a method, system and device for recommending points of interest based on spatiotemporal context perception. In order to solve the technical problem of low accuracy of recommendation methods in the prior art, the present invention first constructs a user social network graph based on friend users and target users to obtain target user characteristics containing social information; then, a user point of interest interaction bipartite graph is constructed based on the target user, friend users and several points of interest to obtain target user characteristics containing point of interest interaction preferences, as well as several point of interest interaction preference characteristics; then, a point of interest spatiotemporal transfer graph is constructed based on the temporal and spatial information of the target user visiting several points of interest to obtain several point of interest spatiotemporal preference characteristics; finally, the above features are subjected to probability mapping processing to obtain recommendation values, and the points of interest corresponding to the recommendation value ranking value less than the ranking threshold are recommended to the target user, thereby improving recommendation accuracy and user experience.

Description

Technical Field

[0001] The present invention relates to the field of electronic data processing technology, and in particular to a method, system and device for recommending points of interest based on spatiotemporal context perception. Background Art

[0002] With the rapid development of internet technology, social networking services have become increasingly common in our daily lives, with a rapidly growing number of users and a vast accumulation of check-in data. On these networks, users can leverage location-based services on smartphones and other mobile devices to explore nearby points of interest, record their visits, and share their real-life experiences with others. These platforms not only foster interaction between users but also significantly enrich people's lifestyles. As an extension of general point of interest recommendation research, next point of interest recommendation has become a research focus in the current service recommendation field.

[0003] Early POI recommendations relied primarily on traditional techniques such as content-based recommendations and collaborative filtering. In recent years, with the rapid development of advanced technologies such as machine learning, deep learning, natural language processing, and knowledge graphs, these methods have demonstrated significant advantages in multiple fields. However, these methods typically focus only on shallow interactions between features and fail to capture the deeper, implicit relationships between users and POIs. This results in low service recommendation accuracy, diminished user interest, and a poor user experience. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, system and device for recommending points of interest based on spatiotemporal context perception.

[0005] The technical solutions of the present invention are as follows:

[0006] A method for recommending points of interest based on spatiotemporal context awareness includes the following operations:

[0007] S1. Obtain users who have social connections with the target user as friend users; construct a user social network graph with all users as nodes and edges as social connection strength; transfer the friend user features in the user social network graph to the target user based on the multi-head attention mechanism to obtain the target user features containing social information;

[0008] S2. Based on the target user, friend users, and several points of interest, a bipartite graph of user point of interest interaction is constructed. In the bipartite graph of user point of interest interaction, the edges between users and the edges between users and points of interest are obtained based on the interaction information between users and points of interest, and the edges between points of interest are obtained based on the popularity difference between points of interest. The bipartite graph of user point of interest interaction is subjected to several node information mutual propagation and node propagation information aggregation processes to obtain a user point of interest interaction propagation graph. In the user point of interest interaction propagation graph, the node features corresponding to the target user and several points of interest are subjected to nonlinear mapping processing to obtain target user features containing point of interest interaction preferences, as well as several point of interest interaction preference features.

[0009] S3. Based on the temporal and spatial information of the target user visiting several points of interest within the historical neighborhood time range, a spatiotemporal transfer graph of points of interest is constructed, with points of interest as nodes and edges as the spatiotemporal intensity of visits. In the interest point transfer preference graph, all nodes are subjected to several graph convolutions and feature updates within the neighborhood range to obtain a spatiotemporal feature aggregation graph of points of interest. The node features corresponding to several points of interest in the spatiotemporal feature aggregation graph of points of interest are obtained to obtain several spatiotemporal preference features of points of interest.

[0010] S4. The target user features containing social information are fused with the target user features containing interest point interaction preferences to obtain target user fusion features; several interest point interaction preference features are fused with their corresponding interest point spatiotemporal preference features to obtain several interest point fusion features; the target user fusion features and several interest point fusion features are probabilistically mapped to obtain several recommendation values; the several recommendation values ​​are arranged in descending order, and the interest points corresponding to the ranking values ​​less than the ranking threshold are recommended to the target user.

[0011] The operation of obtaining the target user features containing social information in S1 is achieved through the following formula:

[0012]

[0013] Q u is the target user feature containing social information, w k is the weight of the kth attention head, K is the total number of attention heads, α u,v is the social connection strength between target user u and friend user v, N is the user set, q v is the information feature of the vth friend user.

[0014] The strength of the social connection between users in S1 is obtained by multiplying the information features of both users with their respective weights, and then performing splicing, feature mapping, nonlinear processing and normalization.

[0015] In the user interest point interaction bipartite graph of S2, if the user has visited an interest point, the edge between the user and the interest point is a positive edge; if the user has not visited the interest point, the edge between the user and the interest point is a negative edge; if the edge signs between users and the same interest point are the same, the edge between users is a positive edge; if there is no edge with the same sign as the same interest point between users, the edge between users is a negative edge; the edge between interest points is a vector edge, pointing from the first interest point to the second interest point, and the popularity of the first interest point is not less than that of the second interest point.

[0016] The popularity of a point of interest is obtained based on the number of times the point of interest has been visited by users within a historical neighborhood time range.

[0017] The operations of mutual propagation of the current node information and aggregation of node propagation information in S2 are respectively implemented by the following formulas:

[0018]

[0019] is the propagation characteristic from node x to node y for the l+1th time, β x,y is the weight corresponding to the positive edge, negative edge or vector edge between node x and node y, is the information aggregation feature of the l-th node x; is the aggregation coefficient of node x and node o at the lth time, which is obtained by multiplying the information aggregation features of node x at the l-1th time and the information aggregation features of node o at the l-1th time with their respective weights, and then performing splicing, feature mapping, nonlinear processing and normalization; is the propagation feature from node o to node x for the lth time, and O is the set of all nodes.

[0020] In S3, the graph convolution operation within the l+1th neighborhood is implemented by the following formula: is the graph convolution feature of the i-th node at the l+1th time, J is the set of nodes in the neighborhood of the i-th node, A ij is the spatiotemporal adjacency matrix of the graph structure composed of nodes within the neighborhood of the i-th node, c is the normalization coefficient, W l +1 is the l+1th graph convolution weight, Update the matrix for the jth node feature for the lth time; the feature update processing within the lth neighborhood range is implemented by the following formula:

[0021]

[0022] Update the matrix for the lth i-th node feature, is the update coefficient of the i-th node at the l-th time, Update the state value of the i-th node for the l-1th time, ⊙ is the Hadamard product operation, Update the state value of the i-th node for the l-th time, σ is the sigmoid function, W z Update the learning weight for the first node, U z Update the learning weight for the second node, b is the updated compensation value, is the graph convolution feature of the lth i-th node, W a Update the learning weights for the nodes.

[0023] A point of interest recommendation system based on spatiotemporal context awareness is used to implement the above-mentioned point of interest recommendation method based on spatiotemporal context awareness, comprising:

[0024] The target user feature generation module containing social information is used to obtain users who have social connections with the target user as friend users; construct a user social network graph with all users as nodes and edges as social connection strengths; and transfer the friend user features in the user social network graph to the target user based on the multi-head attention mechanism to obtain the target user features containing social information;

[0025] The module generates target user features and interest point interaction preference features with interest point interaction preferences, and is used to construct a user interest point interaction bipartite graph based on the target user, friend users, and several interest points. In the user interest point interaction bipartite graph, the edges between users and the edges between users and interest points are obtained based on the interaction information between users and interest points, and the edges between interest points are obtained based on the popularity difference between interest points. The user interest point interaction bipartite graph is subjected to several node information mutual propagation and node propagation information aggregation processes to obtain a user interest point interaction propagation graph. The node features corresponding to the target user and several interest points in the user interest point interaction propagation graph are respectively subjected to nonlinear mapping processes to obtain the target user features with interest point interaction preferences and several interest point interaction preference features.

[0026] The module for generating spatiotemporal preference features of points of interest is used to construct a spatiotemporal transfer graph of points of interest based on the temporal and spatial information of target users visiting several points of interest within a historical neighborhood time range, with points of interest as nodes and edges as the spatiotemporal intensity of visits. In the point of interest transfer preference graph, all nodes are subjected to several graph convolutions and feature updates within the neighborhood range to obtain a spatiotemporal feature aggregation graph of points of interest. The node features corresponding to several points of interest in the spatiotemporal feature aggregation graph of points of interest are obtained to obtain several spatiotemporal preference features of points of interest.

[0027] The recommendation module is used to fuse the target user features containing social information with the target user features containing interest point interaction preferences to obtain the target user fusion features; several interest point interaction preference features are respectively fused with their corresponding interest point spatiotemporal preference features to obtain several interest point fusion features; the target user fusion features and several interest point fusion features are respectively subjected to probability mapping processing to obtain several recommendation values; the several recommendation values ​​are arranged in descending order, and the interest points corresponding to the ranking values ​​less than the ranking threshold are recommended to the target user.

[0028] A device for recommending points of interest based on spatiotemporal context awareness includes a processor and a memory, wherein the processor implements the above-mentioned method for recommending points of interest based on spatiotemporal context awareness when executing a computer program stored in the memory.

[0029] A computer-readable storage medium is used to store a computer program, wherein when the computer program is executed by a processor, the method for recommending points of interest based on spatiotemporal context awareness is implemented.

[0030] The beneficial effects of the present invention are:

[0031] The present invention provides a method for recommending points of interest based on spatiotemporal context awareness. First, friend users who have social connections with a target user are obtained, and a user social network graph is constructed together with the target user. The friend user features in the user social network graph are transferred to the target user based on a multi-head attention mechanism, and the node features corresponding to the target user are extracted to obtain target user features containing social information. Then, based on the target user, friend users and several points of interest, a user point of interest interaction bipartite graph is constructed that can intuitively reflect the user's point of interest interaction preference and the point of interest popularity difference information. After several rounds of node information mutual propagation and node propagation information aggregation processing, nonlinear mapping processing is performed to obtain the target user features containing the point of interest interaction preference and several point of interest interaction preference features. Then, based on the time when the target user visits several points of interest, a target user feature containing the point of interest interaction preference and several point of interest interaction preference features are obtained. The temporal and spatial information of the target user is used to construct a spatiotemporal transfer graph of interest points that can intuitively reflect the spatiotemporal transfer preference of the target user's interest points. After several times of graph convolution and feature update processing within the neighborhood range, the complex spatiotemporal transfer relationship between interest points is captured, the context information of the target user's interest point transfer preference is extracted, and several spatiotemporal preference features of interest points are obtained. Finally, the target user features containing social information are probability mapped with the target user features containing interest point interaction preferences, several interest point interaction preference features, and several interest point spatiotemporal preference features to obtain the recommendation value. The interest points whose recommendation value ranking value is less than the ranking threshold are recommended to the target user, thereby improving the recommendation accuracy and user experience. Compared with the existing methods, the method of the present invention can improve the precision and recall rate, with an average improvement rate of 24.22% and 12.53%. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] By reading the detailed description of the preferred embodiment below, the solutions and advantages of the present application will become clear to those skilled in the art. The accompanying drawings are only for illustrating the preferred embodiment and are not to be considered as limiting the present invention.

[0033] In the attached figure:

[0034] Figure 1 In the embodiment, a schematic diagram of the process of the method of this embodiment;

[0035] Figure 2 Schematic diagram of the structure of the spatiotemporal transfer graph of interest points in the embodiment. DETAILED DESCRIPTION

[0036] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings.

[0037] This embodiment provides a method for recommending points of interest based on spatiotemporal context awareness. Figure 1 , including the following operations:

[0038] S1. Obtain users who have social connections with the target user as friend users; construct a user social network graph with all users as nodes and edges as social connection strength; transfer the friend user features in the user social network graph to the target user based on the multi-head attention mechanism to obtain the target user features containing social information;

[0039] S2. Based on the target user, friend users, and several points of interest, a bipartite graph of user point of interest interaction is constructed. In the bipartite graph of user point of interest interaction, the edges between users and the edges between users and points of interest are obtained based on the interaction information between users and points of interest, and the edges between points of interest are obtained based on the popularity difference between points of interest. The bipartite graph of user point of interest interaction is subjected to several node information mutual propagation and node propagation information aggregation processes to obtain a user point of interest interaction propagation graph. In the user point of interest interaction propagation graph, the node features corresponding to the target user and several points of interest are subjected to nonlinear mapping processing to obtain target user features containing point of interest interaction preferences, as well as several point of interest interaction preference features.

[0040] S3. Based on the temporal and spatial information of the target user visiting several points of interest within the historical neighborhood time range, a spatiotemporal transfer graph of points of interest is constructed, with points of interest as nodes and edges as the spatiotemporal intensity of visits. In the interest point transfer preference graph, all nodes are subjected to several graph convolutions and feature updates within the neighborhood range to obtain a spatiotemporal feature aggregation graph of points of interest. The node features corresponding to several points of interest in the spatiotemporal feature aggregation graph of points of interest are obtained to obtain several spatiotemporal preference features of points of interest.

[0041] S4. The target user features containing social information are fused with the target user features containing interest point interaction preferences to obtain target user fusion features; several interest point interaction preference features are fused with their corresponding interest point spatiotemporal preference features to obtain several interest point fusion features; the target user fusion features and several interest point fusion features are probabilistically mapped to obtain several recommendation values; the several recommendation values ​​are arranged in descending order, and the interest points corresponding to the ranking values ​​less than the ranking threshold are recommended to the target user.

[0042] S1. Obtain users who have social connections with the target user as friend users; construct a user social network graph with all users as nodes and edges as social connection strength; based on the multi-head attention mechanism, transfer the friend user features in the user social network graph to the target user to obtain the target user features containing social information.

[0043] Acquire friend users who have social connections with the target user, build a user social network graph with the target user, and transfer the friend user features in the user social network graph to the target user based on the multi-head attention mechanism, extract the corresponding node features of the target user, and obtain the target user features containing social information, thereby obtaining the target user's social tendencies, indirectly obtaining the target user's preference information, and increasing the target user's preference information content, which can solve the problem of sparse target user data and improve the accuracy of subsequent point of interest recommendations.

[0044] First, users who have social connections with the target user are obtained as friend users. Based on the social connections between the target user and the friend users, the target user's social tendencies are obtained, thereby indirectly obtaining the target user's preference information and increasing the target user's preference information content.

[0045] Then, a user social network graph is constructed with all users (including target users and friend users) as nodes and edges as social connection strengths, which facilitates intuitive reflection of user social tendencies.

[0046] Finally, based on the multi-head attention mechanism, the friend user features in the user social network graph are transferred to the target user, the corresponding node features of the target user are extracted, and the target user features containing social information are obtained.

[0047] The above operation of obtaining the target user characteristics containing social information is achieved through the following formula:

[0048]

[0049] Q u is the target user feature containing social information, w k is the weight of the kth attention head, K is the total number of attention heads, α u,vis the social connection strength between target user u and friend user v, N is the user set, and the user set N includes target user u, α u,u =1,q v is the information feature of the vth friend user.

[0050] The strength of social connections between users is obtained by multiplying the information features of both users by their respective weights, and then performing splicing, feature mapping, nonlinear processing and normalization.

[0051] The social connection strength is obtained by the following formula:

[0052]

[0053] w u is the target user u weight, q u is the target user u information feature, w v is the target user v weight, q v is the information feature of friend user v, a() is the feature mapping function, which maps the concatenated high-dimensional features to a real number (which can be achieved through convolutional layers and pooling layers), and LeakyReLU() is a nonlinear function.

[0054] S2. Based on the target user, friend users and several points of interest, a bipartite graph of user interest point interaction is constructed; the bipartite graph of user interest point interaction is subjected to several node information mutual propagation and node propagation information aggregation processes to obtain a user interest point interaction propagation graph; in the user interest point interaction propagation graph, the node features corresponding to the target user and several points of interest are subjected to nonlinear mapping processes to obtain target user features containing interest point interaction preferences, as well as several interest point interaction preference features.

[0055] Based on the target user, friend users and several points of interest, a bipartite graph of user point of interest interaction is constructed, which can intuitively reflect the user's point of interest interaction preference and the information on the popularity difference of the points of interest. After several rounds of node information mutual propagation and node propagation information aggregation, nonlinear mapping processing is performed to obtain the target user characteristics containing the point of interest interaction preference and several point of interest interaction preference characteristics.

[0056] First, a bipartite graph of user-POI interactions is constructed based on the target user, their friends, and several POIs to be recommended. This graph provides a more comprehensive understanding of the target user's POI interaction preferences. In this bipartite graph, edges between users and between users and POIs are derived from the interaction information between them, while edges between POIs are derived from the popularity differences between POIs.

[0057] The edge attributes between users and points of interest are defined as follows: if the user has visited a point of interest, it means that the user preference is closely related to the point of interest, and the edge between the user and the point of interest is a positive edge; if the user has not visited a point of interest, it means that the user preference is not closely related to the point of interest, and the edge between the user and the point of interest is a negative edge.

[0058] The edge attributes between users are defined as follows: if the sign of the edge between users is the same as that of the same point of interest, the edge between them is positive; if the sign of the edge between users is not the same as that of the same point of interest, the edge between them is negative. For example, if the edges between user u and user v and point of interest i are both positive or negative, it means that user u and user v are both interested in point of interest i or are not interested in point of interest i, indicating that user u and user v have similar preferences. Therefore, the edge between user u and user v is positive; otherwise, it is negative.

[0059] The edge attributes between POIs are defined as follows: an edge between POIs is a vector edge, pointing from the first POI to the second POI, and the popularity of the first POI is not less than that of the second POI. For example, if the popularity of POI i is 6 and the popularity of POI j is 4, the edge between POI i and POI j is a vector edge with an arrow pointing from POI i to POI j, reflecting the decreasing direction of popularity and facilitating analysis of POI popularity distribution. The vector edge weight is based on the popularity difference between POIs, and the popularity of POIs is based on the number of times the POI has been visited by users within the historical neighborhood time range.

[0060] Then, the user interest point interaction bipartite graph is subjected to several node information mutual propagation and node propagation information aggregation processes to comprehensively learn the interaction preferences of interest points and obtain the user interest point interaction propagation graph.

[0061] The current (l+1th) node information propagation is achieved through the following formula:

[0062]

[0063] is the propagation characteristic from node x to node y for the l+1th time, β x,y is the weight of the positive edge, negative edge, or vector edge between node x and node y. The weight of the edge between users is based on the edge attributes and / or the strength of the social connection between users and / or the total number of common interactive points of interest. The weight of the edge between users and points of interest is based on the edge attributes and / or the number of interactions. The vector edge weight is based on the popularity difference between points of interest. is the information aggregation feature of the l-th node x.

[0064] The operations of the l-th node propagation information aggregation processing are implemented by the following formulas:

[0065]

[0066] is the aggregation coefficient of node x and node o at the lth time, which is obtained by multiplying the information aggregation features of node x at the l-1th time and the information aggregation features of node o at the l-1th time with their respective weights, and then performing splicing, feature mapping, nonlinear processing and normalization; is the propagation feature from node o to node x for the first time, including the propagation feature from user node to interest point node, the propagation feature from user node to user node, and the propagation feature from the first interest point node to the second interest point node. O is the set of all nodes, including node x.

[0067] Finally, the target user and several interest point corresponding node features in the user interest point interaction propagation graph are subjected to nonlinear mapping processing respectively to obtain the target user features containing interest point interaction preferences and several interest point interaction preference features.

[0068] The nonlinear mapping process is achieved through the following formula:

[0069] R u =W r (σ(dropout(W1p u +d1)))+d2,

[0070] R u is the target user feature with interaction preference for points of interest, W r is the nonlinear mapping processing weight, σ is the sigmoid function, dropout is the parameter loss function, p u is the node feature (information aggregation feature) corresponding to the target user u, d1 is the first nonlinear mapping compensation, and d2 is the second nonlinear mapping compensation.

[0071] S3. Based on the temporal and spatial information of target users visiting several points of interest within the historical neighborhood time range, a spatiotemporal transfer graph of points of interest is constructed with points of interest as nodes and edges as the spatiotemporal intensity of visits; in the interest point transfer preference graph, all nodes are subjected to graph convolution and feature update processing within the neighborhood several times to obtain a spatiotemporal feature aggregation graph of points of interest; the corresponding node features of several points of interest in the spatiotemporal feature aggregation graph of points of interest are obtained to obtain several spatiotemporal preference features of points of interest.

[0072] Based on the temporal and spatial information of the target user's visits to several points of interest, a spatiotemporal transfer graph of interest points is constructed, which can intuitively reflect the spatiotemporal transfer preference of the target user's interest points. After several graph convolutions and feature updates within the neighborhood, the complex spatiotemporal transfer relationship between interest points is captured, the contextual information of the target user's interest point transfer preference is extracted, and several spatiotemporal preference features of interest points are obtained.

[0073] First, based on the temporal and spatial information of target users visiting several points of interest within the historical neighborhood time range, a spatiotemporal transfer graph of points of interest is constructed with points of interest as nodes and edges as the spatiotemporal intensity of visits, as shown in the following example: Figure 2 ,It is convenient to capture the spatial and temporal relationship of the target user preferences and obtain the target user preference transfer information.,The spatial and temporal intensity of visits is obtained based on the spatial distance between,nodes (points of interest) and the visit interval time.

[0074] Then, in the interest point transfer preference graph, all nodes are subjected to several graph convolution and feature update processes within the neighborhood range, and the node features containing similar target user preferences in time and space are aggregated to mine the key features reflecting the target user's interest point migration tendency, and obtain the interest point spatiotemporal feature aggregation graph.

[0075] The operation of graph convolution within the l+1th neighborhood is implemented by the following formula:

[0076]

[0077] is the graph convolution feature of the i-th node at the l+1th time, J is the set of nodes in the neighborhood of the i-th node, A ij is the spatiotemporal adjacency matrix of the graph structure (the spatiotemporal transition graph of interest points) composed of the nodes in the neighborhood of the i-th node, which is obtained by the Hadamard product operation of the temporal adjacency matrix and the spatial adjacency matrix of the corresponding graph structure. The neighborhood range is obtained based on the preset spatiotemporal intensity of the visit; c is the normalization coefficient, W l+1 is the l+1th graph convolution weight, Update the matrix for the lth jth node feature.

[0078] The feature update operation within the lth neighborhood is achieved through the following formula:

[0079]

[0080] Update the matrix for the lth i-th node feature, is the update coefficient of the i-th node at the l-th time, Update the state value of the i-th node for the l-1th time, ⊙ is the Hadamard product operation, Update the state value of the i-th node for the l-th time, σ is the sigmoid function, W z Update the learning weight for the first node, U z Update the learning weight for the second node, b is the updated compensation value, is the graph convolution feature of the lth i-th node, W a Update the learning weights for the nodes.

[0081] Finally, the node features corresponding to several points of interest in the spatiotemporal feature aggregation graph of points of interest are obtained, and the relevant information about the target user's preference transfer on the points of interest is extracted to obtain several spatiotemporal preference features of the points of interest.

[0082] S4. The target user features containing social information are fused with the target user features containing interest point interaction preferences to obtain target user fusion features; several interest point interaction preference features are fused with their corresponding interest point spatiotemporal preference features to obtain several interest point fusion features; the target user fusion features and several interest point fusion features are probabilistically mapped to obtain several recommendation values; the several recommendation values ​​are arranged in descending order, and the interest points corresponding to the ranking values ​​less than the ranking threshold are recommended to the target user.

[0083] The target user features containing social information are probabilistically mapped with the target user features containing interest point interaction preferences, several interest point interaction preference features, and several interest point spatiotemporal preference features to obtain recommendation values; and the interest points corresponding to the recommendation value whose ranking value is less than the ranking threshold are recommended to the target user to improve the recommendation accuracy.

[0084] First, the target user features containing social information are fused with the target user features containing interest point interaction preferences to obtain the target user fusion features that can fully reflect the target user preferences.

[0085] At the same time, several interest point interaction preference features are fused with their corresponding interest point spatiotemporal preference features respectively to obtain several interest point fusion features that can reflect the interest point preference transfer of the target user.

[0086] Then, the target user fusion feature and several interest point fusion features are respectively subjected to probability mapping processing (which can be achieved through the sigmoid function) to obtain several recommendation values.

[0087] Finally, several recommendation values ​​are arranged in descending order, and the points of interest corresponding to the ranking values ​​less than the ranking threshold, that is, the points of interest with larger recommendation values, are recommended to the target user to improve the recommendation accuracy and enhance the user experience.

[0088] To verify the effectiveness of the method recommended in this embodiment, the following experiment was conducted.

[0089] Dataset and experimental environment. Three sets of personalized POI recommendation datasets were selected in the experiment to verify the performance of the method of this embodiment, namely the Foursquare dataset, the Gowalla dataset, and the Yelp dataset. These three datasets contain rich user social data and POI interaction data. Each piece of user POI interaction data records the user ID and POI information in detail. In order to ensure the accuracy and effectiveness of the experiment, and to keep consistent with the experimental settings of the baseline model (existing method), the experiment referred to the existing method and performed data preprocessing operations on the original dataset. In the data preprocessing stage, those POIs that were visited less than 10 times and users who checked in less than 10 times were removed. The details of the processed dataset are shown in Table 1. Subsequently, 60% of each dataset was randomly selected as training data, 20% as verification data, and the remaining 20% ​​was allocated as test data.

[0090] Table 1 Summary of data information of Foursquare dataset, Gowalla dataset and Yelp dataset

[0091] Dataset Number of users Number of points of interest Number of sign-ins Foursquare Dataset 2551 13474 124993 Gowalla dataset 5628 31803 620683 Yelp Dataset 30887 18995 860888

[0092] The experimental operating environment is as follows: the GPU uses the NVIDIA GeForce RTX 3090 graphics card to ensure the efficiency of model training and inference; the operating system uses Windows 10 Professional Edition 64-bit to provide a stable computing environment; the memory configuration is 32GB RAM to meet the memory requirements of large neural network models; the CPU is the 12th generation Intel Core i9-12900K, with a main frequency of 3.19GHz, providing powerful computing power; the deep learning model framework uses Pytorch based on Python 3.8 version, which supports model building, training and deployment; the development environment uses the Pycharm integrated development environment to provide a convenient programming and debugging experience.

[0093] Experimental evaluation indicators. Three evaluation indicators are used in the experiment, namely Precision (P@K), Recall (R@K) and MAP (M@K). The formulas of these three indicators are defined as follows:

[0094]

[0095] Among them, R(g) represents the list of interest point recommendation results, T(g) is the real user selection interaction list, N is the length of the interest point recommendation list, and the value of K is set to K = [5, 10, 20].

[0096] Comparison with existing methods. Seven state-of-the-art personalized POI recommendation models are as follows.

[0097] HME method: The HME method constructs a hyperspace embedding model that includes information about points of interest, users, regions, and categories, and uses the ncare unit sphere model of points of interest to represent them. By jointly learning point-of-interest transitions, user preferences, regional relationships, and category relationships, the HME method can effectively capture the implicit hierarchical structure. In order to combine user preferences with the sequential transitions between points of interest, the HME method designs the Einstein midpoint aggregation method to improve the recommendation accuracy.

[0098] CLB method: The CLB method proposes a new next point of interest recommendation system based on the user's current location. The model constructs a user's check-in sequence graph, represents the user's check-in sequence as a directed graph, and calculates the current location of similar users based on time and distance factors to generate a recommendation list.

[0099] SUCP method: The SUCP method combines the spatiotemporal data of users' social networks and geographic activity centers, and uses matrix decomposition methods to predict users' preferences for points of interest. At the same time, the model also considers multiple factors such as the similarity between users and their friends, users' check-in habits, and the geographical distribution of points of interest.

[0100] PPR_IGCN method: The PPR_IGCN method combines the user's social relationships and the spatiotemporal information of the user's geographic activity center to predict the user's interest preferences, and comprehensively learns the user's interest preferences and behavior patterns through joint learning of multiple types of relationship graphs.

[0101] RSTSE method: The RSTSE method constructs a virtual trajectory map that integrates the spatiotemporal characteristics of users and points of interest. It evaluates the similarity between users by calculating direct and indirect trust between users. The model combines neural collaborative filtering technology to generate a recommended list of places that users may be interested in in the future.

[0102] RST-GCN method: The RST-GCN method effectively captures users' dynamic interests by combining a multi-head self-attention mechanism with regionalized subgraphs. The model utilizes users' check-in sequences, POI visit sequences, and geographic spatial features to classify users into different regional categories, reducing information propagation between dissimilar nodes. Through the multi-head self-attention mechanism, the RST-GCN method dynamically adjusts the importance of each time step to better reflect changes in user interests.

[0103] DeepPOF method: The DeepPOF method provides a comprehensive review of deep learning-based point of interest recommendation systems, explores the application of various neural network methods in capturing user check-in sequences, point of interest visit sequences and geographic spatial features, and discusses the advantages of these methods in solving data sparsity and cold start problems.

[0104] The experiment trained and optimized the method of this embodiment on three public datasets: the Gowalla dataset, the Foursquare dataset, and the Yelp dataset. For the users in each dataset, the study predicted their ratings for all points of interest and sorted them according to these ratings. According to different values ​​of the number of recommendations K (K=5, K=10, and K=20), the study generated three different sets of prediction results. Then, these prediction results were compared and analyzed with the test dataset to evaluate the accuracy and recall of the model. In addition, these evaluation indicators were also used to compare the performance with the selected benchmark model. The experimental results are shown in Tables 2, 3, and 4. Since the evaluation indicator K values ​​of the various existing methods are not exactly the same as those of the method of this embodiment, data is missing in some cases. These missing data are marked with the " / " symbol in the table.

[0105] Experimental results on the Foursquare dataset. The HME method, CLB method, RST-GCN method, PPR_IGCN method, and RSTSE method were selected as comparison methods on the Foursquare dataset with the method of this embodiment. The experimental results in Table 2 show that the method of this embodiment achieved an average improvement of 13.83% and 8.6% in the P@K and R@K indicators, respectively, and the overall performance of the method of this embodiment is superior to other existing methods.

[0106] Table 2 Experimental comparison results of the method in this embodiment and existing methods on the Foursquare dataset

[0107]

[0108]

[0109] Experimental results on the Gowalla dataset. The HME method, RST-GCN method, CLB method, RSTSE method, and SUCP method were selected for comparison with the method of this embodiment on the Gowalla dataset. The experimental results in Table 3 show that the method of this embodiment achieves an average improvement of 47% and 18% in the P@K and R@K metrics, respectively, and its overall performance is superior to other existing methods.

[0110] Table 3 Experimental comparison results of the method in this embodiment and existing methods on the Gowalla dataset

[0111]

[0112] Experimental results on the Yelp dataset. The SUCP method, PPR_IGCN method, and DeepPOF method were selected as comparison models for the method of this embodiment on the Yelp dataset. The experimental results in Table 4 show that the method of this embodiment achieved an average improvement of 11.83% and 11% in the P@K and R@K metrics, respectively, and the overall performance of the method of this embodiment is superior to other existing methods.

[0113] Table 4 Experimental comparison results of the method in this embodiment and existing methods on the Yelp dataset

[0114]

[0115] Overall, the method of this embodiment shows significant advantages in both accuracy and recall compared to existing advanced recommendation methods, with average improvements of 24.22% and 12.53% respectively, and the overall performance of the method of this embodiment surpasses other existing methods.

[0116] This embodiment further provides a point of interest recommendation system based on spatiotemporal context awareness, which is used to implement the above-mentioned point of interest recommendation method based on spatiotemporal context awareness, including:

[0117] The target user feature generation module containing social information is used to obtain users who have social connections with the target user as friend users; construct a user social network graph with all users as nodes and edges as social connection strengths; and transfer the friend user features in the user social network graph to the target user based on the multi-head attention mechanism to obtain the target user features containing social information;

[0118] The module generates target user features and interest point interaction preference features with interest point interaction preferences, and is used to construct a user interest point interaction bipartite graph based on the target user, friend users, and several interest points. In the user interest point interaction bipartite graph, the edges between users and the edges between users and interest points are obtained based on the interaction information between users and interest points, and the edges between interest points are obtained based on the popularity difference between interest points. The user interest point interaction bipartite graph is subjected to several node information mutual propagation and node propagation information aggregation processes to obtain a user interest point interaction propagation graph. The node features corresponding to the target user and several interest points in the user interest point interaction propagation graph are respectively subjected to nonlinear mapping processes to obtain the target user features with interest point interaction preferences and several interest point interaction preference features.

[0119] The module for generating spatiotemporal preference features of points of interest is used to construct a spatiotemporal transfer graph of points of interest based on the temporal and spatial information of target users visiting several points of interest within a historical neighborhood time range, with points of interest as nodes and edges as the spatiotemporal intensity of visits. In the point of interest transfer preference graph, all nodes are subjected to several graph convolutions and feature updates within the neighborhood range to obtain a spatiotemporal feature aggregation graph of points of interest. The node features corresponding to several points of interest in the spatiotemporal feature aggregation graph of points of interest are obtained to obtain several spatiotemporal preference features of points of interest.

[0120] The recommendation module is used to fuse the target user features containing social information with the target user features containing interest point interaction preferences to obtain the target user fusion features; several interest point interaction preference features are respectively fused with their corresponding interest point spatiotemporal preference features to obtain several interest point fusion features; the target user fusion features and several interest point fusion features are respectively subjected to probability mapping processing to obtain several recommendation values; the several recommendation values ​​are arranged in descending order, and the interest points corresponding to the ranking values ​​less than the ranking threshold are recommended to the target user.

[0121] This embodiment also provides a point of interest recommendation device based on spatiotemporal context awareness, including a processor and a memory, wherein the processor implements the above-mentioned point of interest recommendation method based on spatiotemporal context awareness when executing a computer program stored in the memory.

[0122] This embodiment further provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the above-mentioned method for recommending points of interest based on spatiotemporal context awareness.

[0123] This embodiment provides a method for recommending points of interest based on spatiotemporal context awareness. First, friend users who have social connections with a target user are obtained, and a user social network graph is constructed together with the target user. The friend user features in the user social network graph are transferred to the target user based on a multi-head attention mechanism, and the node features corresponding to the target user are extracted to obtain target user features containing social information. Then, based on the target user, friend users and several points of interest, a user point of interest interaction bipartite graph is constructed that can intuitively reflect the user's point of interest interaction preference and the point of interest popularity difference information. After several rounds of node information mutual propagation and node propagation information aggregation processing, nonlinear mapping processing is performed to obtain target user features containing point of interest interaction preference and several point of interest interaction preference features. Then, based on the target user's visits to several points of interest, a target user feature containing point of interest interaction preference and several point of interest interaction preference features are obtained. The proposed method combines temporal and spatial information to construct a spatiotemporal transfer graph of interest points that can intuitively reflect the spatiotemporal transfer preference of target user interest points, and through several graph convolution and feature update processes within the neighborhood, captures the complex spatiotemporal transfer relationship between interest points, extracts the contextual information of target user interest point transfer preference, and obtains several spatiotemporal preference features of interest points. Finally, the target user features containing social information are probability mapped with the target user features containing interest point interaction preferences, several interest point interaction preference features, and several interest point spatiotemporal preference features to obtain the recommendation value, and recommends the interest points whose recommended value ranking value is less than the ranking threshold to the target user, thereby improving the recommendation accuracy and user experience. Compared with the existing methods, the method of the present invention can improve the accuracy and recall rate, with an average improvement rate of 24.22% and 12.53%.

Claims

1. A method for recommending points of interest based on spatiotemporal context awareness, characterized in that: The following operations are included: S1. Obtain users who have social connections with the target user as friend users; construct a user social network graph with all users as nodes and edges as social connection strength; transfer the friend user features in the user social network graph to the target user based on the multi-head attention mechanism to obtain the target user features containing social information; S2. Based on the target user, friend users, and several points of interest, a bipartite graph of user-point-of-interest interactions is constructed. In the bipartite graph, the edges between users and the edges between users and points of interest are derived based on the interaction information between users and points of interest, and the edges between points of interest are derived based on the popularity difference between the points of interest. The user interest point interaction bipartite graph is processed by performing several node information mutual propagation and node propagation information aggregation processes to obtain the user interest point interaction propagation graph; the target user and several interest point corresponding node features in the user interest point interaction propagation graph are processed nonlinearly to obtain the target user features containing interest point interaction preferences and several interest point interaction preference features; S3. Based on the temporal and spatial information of the target user visiting several points of interest within the historical neighborhood time range, a spatiotemporal transfer graph of points of interest is constructed, with points of interest as nodes and edges as the spatiotemporal intensity of visits. In the interest point transfer preference graph, all nodes are subjected to several graph convolutions and feature updates within the neighborhood range to obtain a spatiotemporal feature aggregation graph of points of interest. The node features corresponding to several points of interest in the spatiotemporal feature aggregation graph of points of interest are obtained to obtain several spatiotemporal preference features of points of interest. S4, merging the target user features containing social information with the target user features containing interaction preferences for points of interest to obtain target user fusion features; Several interest point interaction preference features are fused with their corresponding interest point spatiotemporal preference features to obtain several interest point fusion features; The target user fusion features and several interest point fusion features are probabilistically mapped to obtain several recommendation values. The recommendation values ​​are arranged in descending order, and the interest points corresponding to the ranking values ​​less than the ranking threshold are recommended to the target user.

2. The method for recommending points of interest based on spatiotemporal context awareness according to claim 1, characterized in that: In S1, the operation of obtaining the target user features containing social information is achieved through the following formula: Q u is the target user feature containing social information, w k is the weight of the kth attention head, K is the total number of attention heads, α u,v is the social connection strength between target user u and friend user v, N is the user set, q v is the information feature of the vth friend user.

3. The method for recommending points of interest based on spatiotemporal context awareness according to claim 2, characterized in that: In S1, the social connection strength between users is obtained by multiplying the information features of both users by their respective weights, and then performing splicing, feature mapping, nonlinear processing and normalization.

4. The method for recommending points of interest based on spatiotemporal context awareness according to claim 1, characterized in that: In the user interest point interaction bipartite graph of S2, If the user has visited a point of interest, the edge between the user and the point of interest is a positive edge; if the user has not visited a point of interest, the edge between the user and the point of interest is a negative edge; If the edge between users has the same sign as the same point of interest, the edge between them is a positive edge; if the edge between users does not have the same sign as the same point of interest, the edge between them is a negative edge. The edge between the points of interest is a vector edge, which points from the first point of interest to the second point of interest, and the popularity of the first point of interest is not less than that of the second point of interest.

5. The method for recommending points of interest based on spatiotemporal context awareness according to claim 4, characterized in that: The popularity of a point of interest is obtained based on the number of times the point of interest has been visited by users within a historical neighborhood time range.

6. The method for recommending points of interest based on spatiotemporal context awareness according to claim 1, characterized in that: In S2, the operations of mutual propagation of the current secondary node information and aggregation processing of node propagation information are respectively implemented by the following formulas: is the propagation characteristic from node x to node y for the l+1th time, β x,y is the weight corresponding to the positive edge, negative edge or vector edge between node x and node y, is the information aggregation feature of the l-th node x; is the aggregation coefficient of node x and node o at the lth time, which is obtained by multiplying the information aggregation features of node x at the l-1th time and the information aggregation features of node o at the l-1th time with their respective weights, and then performing splicing, feature mapping, nonlinear processing and normalization; is the propagation feature from node o to node x for the lth time, and O is the set of all nodes.

7. The method for recommending points of interest based on spatiotemporal context awareness according to claim 1, characterized in that: In the S3, The operation of graph convolution within the l+1th neighborhood is implemented by the following formula: is the graph convolution feature of the i-th node at the l+1th time, J is the set of nodes in the neighborhood of the i-th node, A ij is the spatiotemporal adjacency matrix of the graph structure composed of nodes within the neighborhood of the i-th node, c is the normalization coefficient, W l+1 is the l+1th graph convolution weight, Update the matrix for the lth jth node feature; The feature update operation within the lth neighborhood is achieved through the following formula: Update the matrix for the lth i-th node feature, is the update coefficient of the i-th node at the l-th time, Update the state value of the i-th node for the l-1th time, ⊙ is the Hadamard product operation, Update the state value of the i-th node for the l-th time, σ is the sigmoid function, W z Update the learning weight for the first node, U z Update the learning weight for the second node, b is the updated compensation value, is the graph convolution feature of the lth i-th node, W a Update the learning weights for the nodes.

8. A point of interest recommendation system based on spatiotemporal context awareness, used to implement the point of interest recommendation method based on spatiotemporal context awareness according to claim 1, characterized in that: include: The target user feature generation module containing social information is used to obtain users who have social connections with the target user as friend users; Construct a user social network graph with all users as nodes and edges as social connection strengths; based on the multi-head attention mechanism, transfer the friend user features in the user social network graph to the target user to obtain the target user features containing social information; The module generates target user features and interest point interaction preference features with interest point interaction preferences, and is used to construct a user interest point interaction bipartite graph based on the target user, friend users, and several interest points. In the user interest point interaction bipartite graph, the edges between users and the edges between users and interest points are obtained based on the interaction information between users and interest points, and the edges between interest points are obtained based on the popularity difference between interest points. The user interest point interaction bipartite graph is subjected to several node information mutual propagation and node propagation information aggregation processes to obtain a user interest point interaction propagation graph. The node features corresponding to the target user and several interest points in the user interest point interaction propagation graph are respectively subjected to nonlinear mapping processes to obtain the target user features with interest point interaction preferences and several interest point interaction preference features. The module for generating spatiotemporal preference features of points of interest is used to construct a spatiotemporal transfer graph of points of interest based on the temporal and spatial information of target users visiting several points of interest within a historical neighborhood time range, with points of interest as nodes and edges as the spatiotemporal intensity of visits. In the point of interest transfer preference graph, all nodes are subjected to several graph convolutions and feature updates within the neighborhood range to obtain a spatiotemporal feature aggregation graph of points of interest. The node features corresponding to several points of interest in the spatiotemporal feature aggregation graph of points of interest are obtained to obtain several spatiotemporal preference features of points of interest. The recommendation module is used to fuse the target user features containing social information with the target user features containing interest point interaction preferences to obtain the target user fusion features; Several points of interest interaction preference features are fused with their corresponding points of interest spatiotemporal preference features to obtain several points of interest fusion features; the target user fusion features are probabilistically mapped with several points of interest fusion features to obtain several recommendation values; the several recommendation values ​​are arranged in descending order, and the points of interest corresponding to the ranking values ​​less than the ranking threshold are recommended to the target user.

9. A point of interest recommendation device based on spatiotemporal context perception, characterized in that: The method comprises a processor and a memory, wherein when the processor executes the computer program stored in the memory, the method for recommending points of interest based on spatiotemporal context awareness as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that Used to store a computer program, wherein when the computer program is executed by a processor, the method for recommending points of interest based on spatiotemporal context awareness according to any one of claims 1 to 7 is implemented.

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