Data determination method and device and computer program product

Abstract

The invention provides a data determination method and device, electronic equipment, a storage medium and a computer program product, relates to the technical field of artificial intelligence, in particular to the technical fields of large models, data recommendation, natural language understanding and the like, and can be applied to a data recommendation scene. The specific implementation scheme is as follows: determining a geographic position corresponding to a target user; generating an interest tree representing interest information of the target user according to the user data of the target user through a large language model; and through a pre-trained data recommendation model, according to the interest tree, determining to-be-recommended data related to the geographic position. Based on the interest tree generated by the large language model and the geographic position corresponding to the target user, the accuracy of the to-be-recommended data is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, specifically to technical fields such as large models, data recommendation, and natural language understanding, and in particular to a data determination method, device, electronic device, storage medium, and computer program product, which can be applied in data recommendation scenarios. Background Art

[0002] Data recommendation can predict and push personalized content or services to users. It is widely used in e-commerce, social media, video platforms and other fields to improve user experience and enhance user stickiness. Currently, the accuracy of recommendation data for users is insufficient. Summary of the invention

[0003] The present disclosure provides a data determination method, apparatus, electronic device, storage medium, and computer program product.

[0004] According to a first aspect, a data determination method is provided, including: determining a geographic location corresponding to a target user; generating an interest tree representing the interest information of the target user based on user data of the target user through a large language model; and determining data to be recommended related to the geographic location based on the interest tree through a pre-trained data recommendation model.

[0005] According to the second aspect, a data determination device is provided, including: a location determination unit, configured to determine the geographical location corresponding to the target user; an interest tree generation unit, configured to generate an interest tree representing the interest information of the target user according to the user data of the target user through a large language model; and a data determination unit, configured to determine the data to be recommended related to the geographical location according to the interest tree through a pre-trained data recommendation model.

[0006] According to a third aspect, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method as described in any implementation of the first aspect.

[0007] According to a fourth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute the method described in any implementation of the first aspect.

[0008] According to a fifth aspect, a computer program product is provided, comprising: a computer program, which implements the method described in any implementation manner of the first aspect when executed by a processor.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0011] Figure 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure may be applied;

[0012] Figure 2 is a flow chart of an embodiment of a data determination method according to the present disclosure;

[0013] Figure 3 is a schematic diagram of an overview display control according to the present embodiment;

[0014] Figure 4 is a schematic diagram of a detail display interface in an animation display mode according to this embodiment;

[0015] Figure 5 is a schematic diagram of a detailed display interface of route recommendation data according to this embodiment;

[0016] Figure 6 is a schematic diagram of an application scenario of the data determination method according to this embodiment;

[0017] Figure 7 is a flow chart of yet another embodiment of a data determination method according to the present disclosure;

[0018] Figure 8 is a structural diagram of an embodiment of a data determination device according to the present disclosure;

[0019] Fig. 9 It is a schematic diagram of the structure of a computer system suitable for implementing the embodiments of the present disclosure. DETAILED DESCRIPTION

[0020] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0021] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0022] Figure 1 An exemplary architecture 100 to which the data determination method and apparatus of the present disclosure may be applied is shown.

[0023] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The communication connection between the terminal devices 101, 102, 103 constitutes a topological network, and the network 104 is used to provide a medium for the communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0024] Terminal devices 101, 102, 103 may be hardware devices or software that support network connection for data interaction and data processing. When terminal devices 101, 102, 103 are hardware, they may be various electronic devices that support network connection, information acquisition, interaction, display, processing and other functions, including but not limited to smart phones, tablet computers, e-book readers, laptop computers and desktop computers, etc. When terminal devices 101, 102, 103 are software, they may be installed in the electronic devices listed above. They may be implemented as multiple software or software modules for providing distributed services, or they may be implemented as a single software or software module. No specific limitation is made here.

[0025] The server 105 may be a server that provides various services, for example, a background processing server that obtains user data of a target user based on the terminal devices 101, 102, and 103, generates an interest tree through a large language model, and determines the data to be recommended related to the geographic location corresponding to the target user based on the interest tree. As an example, the server 105 may be a cloud server.

[0026] It should be noted that the server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules (for example, software or software modules used to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0027] It should also be noted that the data determination method provided in the embodiments of the present disclosure is generally executed by a server, but the possibility of being executed by a terminal device, or being executed by a server and a terminal device in cooperation with each other is not excluded. Accordingly, the various parts (such as various units) included in the data determination device can be all set in the server, or all set in the terminal device, or can be set in the server and the terminal device respectively.

[0028] It should be understood that Figure 1 The number of terminal devices, networks, and servers in the system architecture is only illustrative. Depending on the implementation requirements, there may be any number of terminal devices, networks, and servers. When the electronic device on which the data determination method is running does not need to perform data transmission with other electronic devices, the system architecture may only include the electronic device (e.g., terminal device or server) on which the data determination method is running.

[0029] Please refer to Figure 2 , Figure 2 A flowchart of a data determination method provided in an embodiment of the present disclosure. In process 200, the following steps are included:

[0030] Step 201: determine the geographic location corresponding to the target user.

[0031] In this embodiment, the execution subject of the data determination method (for example, Figure 1 The server in the target user can determine the geographic location corresponding to the target user.

[0032] As an example, the execution subject may determine the geographical location corresponding to the target user based on the geographical location of the terminal device corresponding to the target user. For example, in response to the time duration that the terminal device corresponding to the target user is in a geographical location exceeding a preset time duration, the geographical location is determined as the geographical location corresponding to the target user, that is, the geographical location where the target user is located.

[0033] As another example, the geographical location searched or specified by the target user is determined as the geographical location corresponding to the target user. For example, the geographical location searched or specified by the target user in an electronic map.

[0034] Step 202: Generate an interest tree representing the interest information of the target user according to the user data of the target user through the large language model.

[0035] In this embodiment, the execution subject may generate an interest tree representing the interest information of the target user according to the user data of the target user through a large language model.

[0036] User data may be basic attribute data, behavior data, preference data, transaction data, etc. of the target user. With authorization from the target user, the above execution entity may obtain the user data of the target user.

[0037] The big language model specifically refers to an artificial intelligence pre-trained big language model with large or ultra-large parameters. The big language model includes two meanings, one is "pre-training" and the other is "big model". The combination of the two produces a new artificial intelligence model, that is, after the model is pre-trained on a large data set, it can directly support various applications without or with only a small amount of data fine-tuning. The big artificial intelligence model has excellent context understanding, language generation, learning and transferability.

[0038] In this embodiment, user data is input into a large language model, and with the help of the powerful natural language understanding ability of the large language model, the user's points of interest (e.g., items, attractions, food, etc.) and the associations between the points of interest are determined to generate an interest tree representing the interest information of the target user. The interest tree represents the user's interest preferences through a tree structure. Each node of the tree represents a point of interest, and the connection between the nodes reflects the association or hierarchical relationship between these points of interest.

[0039] In order to improve the efficiency and effectiveness of data processing, before generating the interest tree, the above execution script may pre-process the user data, including but not limited to data cleaning, formatting and normalization.

[0040] In order to make the interest tree accurately reflect the current interest information of the user, an interest tree representing the current interest information of the target user may be generated through a large language model according to the user data up to the current preset time period.

[0041] This embodiment does not limit the execution order of step 201 and step 202, that is, step 201 can be executed before step 202, step 201 can also be executed after step 202, and even step 201 can be executed simultaneously with step 202.

[0042] In some optional implementations of this embodiment, the execution subject may perform step 202 in the following manner:

[0043] The first step is to determine target user data corresponding to each of the multiple time intervals from the user data, and obtain multiple user data groups corresponding to the multiple time intervals one by one.

[0044] The durations of the multiple time intervals are different. The multiple time intervals may overlap or be independent of each other. The number of the multiple time intervals and the corresponding time lengths may be set according to actual conditions. In some implementations, different time intervals may be set for different time units such as days, months, and years, and the length of the time interval increases as the time unit increases.

[0045] As an example, the time interval includes a first time interval and a second time interval, the first time interval is a first time period ending at present (for example, 5 days), and the second time interval is a second time period ending at present (for example, one month).

[0046] For each time interval of the multiple time intervals, a user data group is generated based on the target user data of the target user in the time interval, thereby obtaining multiple user data groups corresponding to the multiple time intervals one by one.

[0047] In the second step, a large language model is used to generate a plurality of interest trees corresponding to the plurality of user data groups in a one-to-one manner according to the plurality of user data groups.

[0048] As an example, for each user data group in the multiple user data groups, the data in the user data group is input into the large language model to generate an interest tree corresponding to the user data group, thereby obtaining multiple interest trees corresponding to the multiple user data groups one by one.

[0049] In this implementation, a large language model is used to generate multiple interest trees representing the interest information of target users in different time intervals based on multiple user data groups, which provides rich interest basis data for the subsequent data recommendation process and helps to improve the flexibility and accuracy of the data recommendation process.

[0050] In some optional implementations of this embodiment, the execution subject may perform the second step in the following manner: for each user data group in the multiple user data groups, perform the following operations:

[0051] First, through a large language model, an interest prompt text is generated according to preset prompt data and the user data group.

[0052] The interest prompt text represents the interest information of the target user in the time interval corresponding to the user data set. The preset prompt data is used to guide the large model to determine the user's interest points from the user data set, for example, "using *** format to output the target user's interest points".

[0053] Then, the interest prompt text is expanded through a large language model to obtain an expanded text.

[0054] In this implementation, a series of interest prompt texts can be generated based on the above first step. For example:

[0055] "Technology" -> "Artificial Intelligence", "Machine Learning", "Programming Language"

[0056] "Sports" -> "Football", "Basketball", "Tennis"

[0057] “Entertainment” -> “Movies”, “Music”, “Games”.

[0058] For each interest prompt text, it can be expanded through a large language model.

[0059] For the interest prompt text of "technology" -> "artificial intelligence", "machine learning", "programming language", the expanded text is, for example:

[0060]

[0061]

[0062] For the interest prompt text of "Entertainment" -> "Movies", "Music", and "Games", the expanded text is, for example:

[0063] According to the expanded text, an interest tree corresponding to the user data group is generated.

[0064] The expanded text includes the user's interest points, and the associations or hierarchical relationships between the interest points; based on the interest points, associations or hierarchical relationships in the expanded text, an interest tree corresponding to the user data group can be generated.

[0065] In this implementation, a specific method for generating an interest tree based on a large language model is provided, which improves the accuracy and generation efficiency of the interest tree.

[0066] Step 203: Determine the data to be recommended related to the geographic location according to the interest tree through the pre-trained data recommendation model.

[0067] In this embodiment, the execution subject may determine the data to be recommended related to the geographic location according to the interest tree through a pre-trained data recommendation model.

[0068] The interest tree and geographic location are used as inputs of the data recommendation model to generate the target user's recommended data. As an example, within a preset range centered on the geographic location, various types of data (such as stores, attractions, etc.) are determined to construct a data set; then, based on the interest tree, the data that the target user is interested in is selected and determined as the recommended data.

[0069] The data recommendation model can adopt various models that can realize the data recommendation function, such as recurrent neural network, convolutional neural network, graph neural network, large language model, etc.

[0070] In some optional implementations of this embodiment, the execution subject may perform step 203 in the following manner:

[0071] For each interest tree among the multiple interest trees, within a recommendation period corresponding to the interest tree, a to-be-recommended data group related to the geographical location is determined according to the interest tree through a data recommendation model.

[0072] The duration of recommendation cycles corresponding to different interest trees is different, the duration of the recommendation cycle corresponding to the interest tree is positively correlated with the duration of the time interval corresponding to the interest tree, and the to-be-recommended data group includes at least one to-be-recommended data.

[0073] Continuing with the above-mentioned first time interval and second time interval as examples, the recommendation cycle corresponding to the interest tree obtained based on the user data in the first time interval is one day. In the day dimension. For example, recommending restaurants near the geographical location to the target user at meal time, and recommending the most convenient means of transportation to users during commuting time. The recommendation cycle corresponding to the interest tree obtained based on the user data in the second time interval is one month. In the month dimension, for example, before the holidays, push data such as transportation ticket grabbing reminders and hotel reservation reminders; during the holidays, recommend data such as popular attractions with high traffic, or niche attractions with low traffic.

[0074] In this implementation, different interest trees are used to recommend data to be recommended in different recommendation periods to target users, thereby further improving the accuracy of the data to be recommended and the richness of the data to be recommended.

[0075] In some optional implementations of this embodiment, the above-mentioned execution entity can execute the process of determining the data to be recommended in the following manner: within the recommendation period corresponding to the interest tree, through the data recommendation model, according to the interest tree and the real-time operation data of the target user, determine the data group to be recommended related to the geographic location.

[0076] The real-time operation data can be the real-time operation data in the target application of the target user, and the real-time operation data can further reflect the real-time interests of the target user. The interest tree, real-time operation data and geographic location are input into the data recommendation model to generate the target user's data to be recommended. The target application is, for example, e-commerce applications, social media applications, video applications, electronic map applications, etc.

[0077] In this implementation, based on the interest tree and the geographical location, combined with the real-time operation data of the target user, the accuracy of the data to be recommended is further improved.

[0078] In some optional implementations of this embodiment, the execution subject may perform the process of determining the data to be recommended in the following manner:

[0079] First, a first feature vector representing the interest tree, a second feature vector representing the real-time operation data, and a third feature vector representing the geographic location are fused to obtain a fused vector.

[0080] As an example, the above-mentioned execution entity can respectively perform feature encoding on the interest tree, real-time operation data and geographic location through a coding network to obtain a first feature vector, a second feature vector and a third feature vector; then, the first feature vector, the second feature vector and the third feature vector are fused through vector splicing, vector addition and the like to obtain a fused vector.

[0081] Then, the fused vector is input into the data recommendation model to determine the data group to be recommended.

[0082] As an example, the above-mentioned execution entity can determine the similarity between the fused vector and the data vectors of each data in the data set corresponding to the geographic location, and sort the data in the data set in order of similarity from large to small, and then combine multiple data with the highest similarity ranking into a data group to be recommended.

[0083] In this implementation, the data recommendation model uses the fused vector of the first eigenvector, the second eigenvector, and the third eigenvector as input to determine the data group to be recommended, which helps to further improve the accuracy and efficiency of data recommendation.

[0084] In some optional implementations of this embodiment, the above execution subject may also perform the following operations:

[0085] First, the summary data corresponding to the data group to be recommended is displayed through the target display device based on the summary display control in the initial display interface.

[0086] The initial display interface may be a display interface that the target user enters by operating the target display device. An overview display control is added to the initial display interface to display overview data corresponding to the data group to be recommended.

[0087] Continue to refer Figure 3 , a schematic diagram outlining the display controls is shown.

[0088] The initial display interface includes multiple controls, including an overview display control 301. For the food-related data group to be recommended, the overview display control displays, for example, "At noon, 10 popular foods are recommended for you." The overview data can be flexibly set, and the overview data in the overview display control can also be, for example, "Lunch recommendation."

[0089] Then, in response to receiving a trigger operation for the overview display control, at least one to-be-recommended data in the to-be-recommended data group is displayed based on at least one data display control in the detail display interface through the target display device.

[0090] In response to the target user triggering the overview display control by clicking, touching or other triggering operations, a detail display interface is displayed to the target user, wherein the detail display interface includes at least one data display control for displaying at least one data to be recommended in the data group to be recommended.

[0091] In this implementation, a data recommendation and display method is provided to improve the operation experience of the target users.

[0092] In some optional implementations of the present embodiment, the above-mentioned execution subject may perform the above-mentioned data display process in the following manner: first, according to the type of at least one data to be recommended, determine the display method of at least one data to be recommended; then, in response to receiving a trigger operation for the overview display control, determine at least one data display control to adopt the display method; finally, in the details display interface, display at least one data to be recommended through at least one data display control.

[0093] In this implementation, for different types of data to be recommended, corresponding display methods can be set for them.

[0094] For example, for the recommended data of food and scenic spots, the display method is animation display, which can be an animation related to food or scenic spots with a preset duration (e.g., 5 seconds). The animation can be generated based on multiple food images corresponding to the store, or based on multiple scenery images corresponding to the scenic spot.

[0095] Continue to refer Figure 4 , shows a schematic diagram of a detail display interface in an animation display mode. At least one data display control 401 in the detail display interface can display at least one data to be recommended in an animation mode.

[0096] For another example, for the recommended data of traffic routes, a display method combining the route and route attribute information may be used.

[0097] Continue to refer Figure 5 , showing a schematic diagram of a detailed display interface of route recommendation data. The data display control further includes a route display control 501 and an attribute display control 502. For each recommended route, the route display control displays the specific track of the route, and the attribute display control displays the time, length and other attribute information of the route.

[0098] In this implementation, different display methods are used for different types of data to be recommended, which improves the data display effect and helps to improve the user's information acquisition efficiency and experience.

[0099] Continue to see Figure 6 , Figure 66 is a schematic diagram of an application scenario 600 of the data determination method according to this embodiment. The server 601 first obtains the geographic location and user data of the target user from the terminal device 603 of the target user 602. After determining the geographic location and user data, first, an interest tree representing the interest information of the target user is generated based on the user data of the target user through a large language model; then, the pre-trained data recommendation model is used to determine the data to be recommended related to the geographic location based on the interest tree, and the data to be recommended is fed back to the terminal device.

[0100] In this embodiment, a data determination method is provided, by determining the geographical location corresponding to the target user; using a large language model, generating an interest tree representing the interest information of the target user based on the user data of the target user; using a pre-trained data recommendation model, determining the data to be recommended related to the geographical location based on the interest tree, thereby providing a method for determining the data to be recommended, and improving the accuracy of the data to be recommended based on the interest tree generated by the large language model and the geographical location corresponding to the target user.

[0101] Continue to refer Figure 7 , shows a schematic process 700 of another embodiment of the data determination method according to the present disclosure. In the process 700, the following steps are included:

[0102] Step 701: determine the geographic location corresponding to the target user.

[0103] Step 702: Determine target user data corresponding to each of the multiple time intervals from the user data, and obtain multiple user data groups corresponding to the multiple time intervals one by one.

[0104] Among them, the durations of multiple time intervals are different.

[0105] Step 703: for each user data group in the plurality of user data groups, perform the following operations:

[0106] Step 7031, generating interest prompt text through a large language model according to preset prompt data and the user data group.

[0107] The interest prompt text represents the interest information of the target user in the time interval corresponding to the user data group.

[0108] Step 7032: Expand the interest prompt text through the large language model to obtain the expanded text.

[0109] Step 7033: Generate an interest tree corresponding to the user data group based on the expanded text.

[0110] Step 704: for each interest tree among the plurality of interest trees, within a recommendation period corresponding to the interest tree, determine a data group to be recommended related to the geographical location according to the interest tree by using a data recommendation model.

[0111] The duration of the recommendation cycle corresponding to different interest trees is different, and the duration of the recommendation cycle corresponding to the interest tree is positively correlated with the duration of the time interval corresponding to the interest tree. The to-be-recommended data group includes at least one to-be-recommended data.

[0112] Step 705 : displaying summary data corresponding to the data group to be recommended based on the summary display control in the initial display interface through the target display device.

[0113] Step 706: In response to receiving a trigger operation for the overview display control, at least one to-be-recommended data in the to-be-recommended data group is displayed based on at least one data display control in the detail display interface via the target display device.

[0114] Compared with process 200, process 700 of the data determination method in this embodiment specifically describes the process of generating the interest tree, the process of determining and displaying the data to be recommended, and further improves the accuracy of the data to be recommended and the user experience.

[0115] Continue to refer Figure 8 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a data determination device. The system embodiment is Figure 2 Corresponding to the method embodiment shown, the system can be specifically applied to various electronic devices.

[0116] like Figure 8 As shown, the data determination device 800 includes: a location determination unit 801, configured to determine the geographical location corresponding to the target user; an interest tree generation unit 802, configured to generate an interest tree representing the interest information of the target user according to the user data of the target user through a large language model; a data determination unit 803, configured to determine the data to be recommended related to the geographical location according to the interest tree through a pre-trained data recommendation model.

[0117] In some optional implementations of this embodiment, the interest tree generation unit 802 is further configured to: determine the target user data corresponding to each of the multiple time intervals from the user data, and obtain multiple user data groups corresponding one-to-one to the multiple time intervals, wherein the durations of the multiple time intervals are different; and generate multiple interest trees corresponding one-to-one to the multiple user data groups based on the multiple user data groups through a large language model.

[0118] In some optional implementations of this embodiment, the interest tree generation unit 802 is further configured to: for each user data group in the multiple user data groups, perform the following operations: generate an interest prompt text through a large language model according to preset prompt data and the user data group, wherein the interest prompt text represents the interest information of the target user within the time interval corresponding to the user data group; expand the interest prompt text through the large language model to obtain an expanded text; and generate an interest tree corresponding to the user data group according to the expanded text.

[0119] In some optional implementations of the present embodiment, the data determination unit 803 is further configured to: for each interest tree among the multiple interest trees, within the recommendation period corresponding to the interest tree, determine the data group to be recommended related to the geographic location according to the interest tree through the data recommendation model, wherein different interest trees correspond to different durations of the recommendation period, the duration of the recommendation period corresponding to the interest tree is positively correlated with the duration of the time interval corresponding to the interest tree, and the data group to be recommended includes at least one data to be recommended.

[0120] In some optional implementations of this embodiment, the data determination unit 803 is further configured to: within the recommendation period corresponding to the interest tree, determine the data group to be recommended related to the geographic location through the data recommendation model according to the interest tree and the real-time operation data of the target user.

[0121] In some optional implementations of this embodiment, the data determination unit 803 is further configured to: fuse the first feature vector representing the interest tree, the second feature vector representing the real-time operation data, and the third feature vector representing the geographic location to obtain a fused vector; input the fused vector into the data recommendation model to determine the data group to be recommended.

[0122] In some optional implementations of this embodiment, the above-mentioned device also includes: a data display unit (not shown in the figure), which is configured to: display the overview data corresponding to the data group to be recommended based on the overview display control in the initial display interface through the target display device; and display at least one data to be recommended in the data group to be recommended based on at least one data display control in the details display interface through the target display device in response to receiving a trigger operation for the overview display control.

[0123] In some optional implementations of this embodiment, the data display unit is further configured to: determine a display method for at least one data to be recommended based on a type of at least one data to be recommended; determine at least one data display control to adopt a display method in response to receiving a trigger operation for an overview display control; and display at least one data to be recommended in a details display interface through at least one data display control.

[0124] In the present embodiment, a data determination device is provided, which determines the geographical location corresponding to the target user; generates an interest tree representing the interest information of the target user according to the user data of the target user through a large language model; and determines the data to be recommended related to the geographical location according to the interest tree through a pre-trained data recommendation model, thereby providing a method for determining the data to be recommended, which improves the accuracy and determination efficiency of the data to be recommended based on the interest tree generated by the large language model and the geographical location corresponding to the target user.

[0125] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the data determination method described in any of the above embodiments can be implemented when the at least one processor executes.

[0126] According to an embodiment of the present disclosure, the present disclosure further provides a readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to implement the data determination method described in any of the above embodiments when executed.

[0127] An embodiment of the present disclosure provides a computer program product. When the computer program is executed by a processor, the data determination method described in any of the above embodiments can be implemented.

[0128] Fig. 9 A schematic block diagram of an example electronic device 900 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0129] like Fig. 9 As shown, the device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0130] A number of components in the device 900 are connected to the I / O interface 905, including: an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0131] The computing unit 901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 901 performs the various methods and processes described above, such as data determination methods. For example, in some embodiments, the data determination method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 900 via ROM 902 and / or communication unit 909. When the computer program is loaded into RAM 903 and executed by the computing unit 901, one or more steps of the data determination method described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the data determination method in any other appropriate manner (e.g., by means of firmware).

[0132] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0133] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data determining device, so that the program code, when executed by the processor or controller, causes the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0134] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0135] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0136] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0137] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services; it may also be a server for a distributed system, or a server combined with a blockchain.

[0138] According to the technical solution of the embodiments of the present disclosure, a data determination method and device are provided, which determine the geographical location corresponding to the target user; generate an interest tree representing the interest information of the target user according to the user data of the target user through a large language model; and determine the data to be recommended related to the geographical location according to the interest tree through a pre-trained data recommendation model, thereby providing a method for determining the data to be recommended, which improves the accuracy and determination efficiency of the data to be recommended based on the interest tree generated by the large language model and the geographical location corresponding to the target user.

[0139] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution provided by this disclosure can be achieved, and this document does not limit this.

[0140] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A data determination method, comprising: Determine the geographic location of the target users; Generate an interest tree representing the interest information of the target user according to the user data of the target user through a large language model; The pre-trained data recommendation model is used to determine the data to be recommended that is related to the geographic location according to the interest tree.

2. The method according to claim 1, wherein: The generating, by using the large language model and according to the user data of the target user, an interest tree representing the interest information of the target user comprises: Determine, from the user data, target user data corresponding to each of the multiple time intervals, and obtain multiple user data groups corresponding to the multiple time intervals one by one, wherein the multiple time intervals have different durations; A plurality of interest trees corresponding to the plurality of user data groups are generated based on the plurality of user data groups by using the large language model.

3. The method according to claim 2, wherein: The step of generating, by using the large language model and according to the plurality of user data groups, a plurality of interest trees corresponding to the plurality of user data groups one by one includes: For each user data group in the plurality of user data groups, perform the following operations: Generate an interest prompt text through the large language model according to preset prompt data and the user data group, wherein the interest prompt text represents the interest information of the target user in the time interval corresponding to the user data group; Expanding the interest prompt text by using the large language model to obtain an expanded text; An interest tree corresponding to the user data group is generated according to the expanded text.

4. The method according to claim 2, wherein: The determining, using the pre-trained data recommendation model and according to the interest tree, the data to be recommended that is related to the geographical location includes: For each of the multiple interest trees, within the recommendation period corresponding to the interest tree, the data recommendation model is used to determine a data group to be recommended related to the geographic location according to the interest tree, wherein different interest trees correspond to different durations of the recommendation period, and the duration of the recommendation period corresponding to the interest tree is positively correlated with the duration of the time interval corresponding to the interest tree, and the data group to be recommended includes at least one data to be recommended.

5. The method according to claim 4, wherein: The step of determining, within a recommendation period corresponding to the interest tree, a to-be-recommended data group related to the geographic location according to the interest tree by using the data recommendation model includes: In a recommendation period corresponding to the interest tree, a to-be-recommended data group related to the geographic location is determined through the data recommendation model according to the interest tree and the real-time operation data of the target user.

6. The method according to claim 5, wherein: Determining the to-be-recommended data group related to the geographic location according to the interest tree and the real-time operation data of the target user through the data recommendation model includes: Fusing a first feature vector representing the interest tree, a second feature vector representing the real-time operation data, and a third feature vector representing the geographic location to obtain a fused vector; The fused vector is input into the data recommendation model to determine the data group to be recommended.

7. The method according to any one of claims 1 to 6, wherein: Also includes: Displaying summary data corresponding to the to-be-recommended data group based on a summary display control in an initial display interface through a target display device; In response to receiving a trigger operation for the overview display control, the target display device displays at least one data to be recommended in the data group to be recommended based on at least one data display control in the detail display interface.

8. The method according to claim 7, wherein: The displaying, in response to receiving a trigger operation for the overview display control, at least one to-be-recommended data in the to-be-recommended data group based on at least one data display control in the detail display interface comprises: Determining, according to the type of the at least one data to be recommended, a display mode of the at least one data to be recommended; In response to receiving a trigger operation for the overview display control, determining at least one data display control using the presentation mode; In the detail display interface, the at least one data to be recommended is displayed through the at least one data display control.

9. A data determination device, comprising: A location determination unit, configured to determine a geographical location corresponding to a target user; An interest tree generating unit is configured to generate an interest tree representing the interest information of the target user according to the user data of the target user through a large language model; The data determination unit is configured to determine the to-be-recommended data related to the geographic location according to the interest tree through a pre-trained data recommendation model.

10. The device according to claim 9, wherein: The interest tree generating unit is further configured to: From the user data, target user data corresponding to each of the multiple time intervals are determined to obtain multiple user data groups corresponding one-to-one to the multiple time intervals, wherein the multiple time intervals have different durations; and through the large language model, multiple interest trees corresponding one-to-one to the multiple user data groups are generated according to the multiple user data groups.

11. The device according to claim 10, wherein: The interest tree generating unit is further configured to: For each user data group in the plurality of user data groups, perform the following operations: Through the large language model, an interest prompt text is generated according to preset prompt data and the user data group, wherein the interest prompt text represents the interest information of the target user in the time interval corresponding to the user data group; through the large language model, the interest prompt text is expanded to obtain an expanded text; and according to the expanded text, an interest tree corresponding to the user data group is generated.

12. The device according to claim 10, wherein: The data determination unit is further configured to: For each of the multiple interest trees, within the recommendation period corresponding to the interest tree, the data recommendation model is used to determine a data group to be recommended related to the geographic location according to the interest tree, wherein different interest trees correspond to different durations of the recommendation period, and the duration of the recommendation period corresponding to the interest tree is positively correlated with the duration of the time interval corresponding to the interest tree, and the data group to be recommended includes at least one data to be recommended.

13. The device according to claim 12, wherein: The data determination unit is further configured to: In a recommendation period corresponding to the interest tree, a to-be-recommended data group related to the geographic location is determined through the data recommendation model according to the interest tree and the real-time operation data of the target user.

14. The device according to claim 13, wherein: The data determination unit is further configured to: A first feature vector representing the interest tree, a second feature vector representing the real-time operation data, and a third feature vector representing the geographic location are fused to obtain a fused vector; and the fused vector is input into the data recommendation model to determine the data group to be recommended.

15. The device according to any one of claims 9 to 14, wherein: Also includes: The data display unit is configured as follows: Through the target display device, based on the overview display control in the initial display interface, the summary data corresponding to the data group to be recommended is displayed; through the target display device, in response to receiving a trigger operation for the overview display control, based on at least one data display control in the details display interface, at least one data to be recommended in the data group to be recommended is displayed.

16. The device according to claim 15, wherein: The data display unit is further configured to: Determine a display method for the at least one data to be recommended based on the type of the at least one data to be recommended; determine at least one data display control using the display method in response to receiving a trigger operation for the overview display control; and display the at least one data to be recommended in the detail display interface through the at least one data display control.

17. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

18. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 8.

19. A computer program product comprising: A computer program which, when executed by a processor, implements the method according to any one of claims 1 to 8.

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