Information presentation method, electronic device, and program product

Abstract

The present disclosure provides an information presentation method, an electronic device and a computer program product. Current user information of a current user is obtained; wherein the current user information is used to determine a first similar person; target information is displayed in a display interface according to a target category; wherein the target category is used to represent a content display category of the display interface; and the target information is obtained by processing historical behavior data of the first similar person according to the target category.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more particularly to an information presentation method, electronic device, and program product. Background Technology

[0002] With the development of computer technology, more and more algorithms are being used for product recommendation. For example, users can enter a keyword in an application and then receive recommendations for related products.

[0003] In existing technologies, recommendation results are often presented to users by directly searching for products based on their needs or keywords. If the recommended results are not what the user wants, they need to re-enter their search keywords and search again. For users, this requires gradually adding search keywords to focus on a specific product (e.g., a neighborhood). Although users can click on search results to view details, adding other search criteria or viewing other products similar to their needs requires a new search. This increases the user's search burden and prevents them from quickly finding the products they want. Therefore, a solution that can accurately present rich search information is needed. Summary of the Invention

[0004] This disclosure provides an information presentation method, electronic device, and program product.

[0005] According to a first aspect of this disclosure, an information presentation method is provided. The method specifically includes: obtaining current user information of the current user; wherein the current user information is used to determine a first similar person; displaying target information in a display interface according to a target category; wherein the target category is used to characterize the content display category of the display interface; the target information is obtained by processing historical behavioral data of the first similar person according to the target category.

[0006] According to at least one embodiment of the present disclosure, displaying target information in a display interface according to a target category includes: displaying at least one of an interactive map and / or an information list and / or a card in the display interface; presenting target information in the interactive map, and / or presenting target information in the information list, and / or presenting target information in the card.

[0007] According to at least one embodiment of the present disclosure, displaying target information in a display interface according to a target category includes: displaying target information representing the category of demand object in the display interface; wherein the target information representing the category of demand object is obtained by aggregating the historical behavior data of multiple first similar people based on the demand object category contained in the historical behavior data of first similar people.

[0008] According to at least one embodiment of the present disclosure, displaying target information in a display interface according to a target category includes: displaying target information representing similar human categories in the display interface; wherein the target information representing similar human categories is obtained by organizing the demand objects contained in the historical behavior data according to the historical behavior data of the first similar person.

[0009] According to at least one embodiment of this disclosure, obtaining current user information of the current user includes: displaying a user information collection control on a display interface; and obtaining the current user information in response to the current user's triggering operation on the user information collection control.

[0010] According to at least one embodiment of this disclosure, the method further includes: displaying a user information collection control on a display interface; updating the first similar person in response to a triggering operation by the current user on the user information collection control on the display interface for request change control and / or threshold change control; updating and displaying target information on the display interface according to the target category; wherein the target information is obtained by processing the historical behavior data of the updated first similar person according to the target category.

[0011] According to at least one embodiment of this disclosure, the method further includes: in response to a current user's current user information update request, updating the first similar person based on the updated current user information; updating and displaying target information in the display interface according to the target category; wherein the target information is obtained by processing the historical behavior data of the updated first similar person according to the target category.

[0012] According to at least one embodiment of this disclosure, the method further includes: in response to a triggering operation on target information, determining a first similar person corresponding to the triggered target information; determining a second similar person based on the first similar person; performing aggregation processing on the demand object category in the historical behavior data of the second similar person to obtain target information; and displaying the target information corresponding to the second similar person in the display interface according to the object category.

[0013] According to at least one embodiment of this disclosure, the method further includes: in response to a triggering operation on target information, determining a first similar person corresponding to the triggered target information; determining a demand object based on the first similar person; and displaying the demand objects in a display interface according to object categories.

[0014] According to at least one embodiment of this disclosure, the method for generating target information includes: acquiring historical behavior data and user attribute data of a first similar person; and performing desensitization processing on the historical behavior data and user attribute data to obtain target information of the first similar person.

[0015] According to at least one embodiment of the present disclosure, displaying target information representing similar human categories on a display interface includes: analyzing behavioral paths and object access and selection decision patterns in the historical behavioral data of the first similar person; filling the behavioral paths and object access and selection decision patterns into a narrative template based on the narrative template, and generating narrative content; and displaying the narrative content on the display interface.

[0016] According to a second aspect of this disclosure, an electronic device is provided, comprising: a memory storing execution instructions; and a processor executing the execution instructions stored in the memory, such that the processor performs the method described in the first aspect of any embodiment of this disclosure.

[0017] According to a third aspect of this disclosure, a readable storage medium is provided, wherein executable instructions are stored therein, which, when executed by a processor, are used to implement the method described in the first aspect of any embodiment of this disclosure.

[0018] According to a fourth aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method described in the first aspect of any embodiment of this disclosure. Attached Figure Description

[0019] The accompanying drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0020] Figure 1 This is a flowchart illustrating the information presentation method provided in an embodiment of the present disclosure.

[0021] Figure 2 This is a flowchart illustrating a target information presentation method as exemplified by an embodiment of the present disclosure.

[0022] Figure 3 This is a schematic diagram illustrating the target information display effect as an example of an embodiment of this disclosure.

[0023] Figure 4 This is a schematic diagram illustrating the aggregation of demand objects as an example for this disclosure.

[0024] Figure 5 A schematic diagram is shown to illustrate similar persons as examples in this disclosure.

[0025] Figure 6 This is a flowchart illustrating the current user information acquisition method provided in this embodiment of the disclosure.

[0026] Figure 7This is a schematic diagram illustrating the display effect of a user information collection control as an example of an embodiment of this disclosure.

[0027] Figure 8 This is a flowchart illustrating a current user information update method provided in an embodiment of this disclosure.

[0028] Figure 9 This is a flowchart illustrating another current user information update method provided in an embodiment of this disclosure.

[0029] Figure 10 A flowchart illustrating a second similar person display method provided in an embodiment of this disclosure.

[0030] Figure 11 A schematic diagram showing a second similar person provided in an embodiment of this disclosure.

[0031] Figure 12 A flowchart illustrating a second similar person display method provided in an embodiment of this disclosure.

[0032] Figure 13 This is a schematic diagram illustrating the effect of displaying a desired object by showing similar people, as provided in an embodiment of this disclosure.

[0033] Figure 14 This is a flowchart illustrating the target information generation method provided in an embodiment of the present disclosure.

[0034] Figure 15 This is a flowchart illustrating the method for displaying narrative content provided in an embodiment of this disclosure.

[0035] Figure 16 This is a schematic diagram illustrating the effect of the narrative content provided in the embodiments of this disclosure.

[0036] Figures 17a to 17e This is a schematic diagram of the user interface of some other embodiments of this disclosure.

[0037] Figures 18a to 18e This is a schematic diagram of the user interface after the target information acquisition control of some embodiments of this disclosure is triggered.

[0038] Figures 19a to 19e This is a schematic diagram of the user interface after the target information acquisition control is triggered, according to some other embodiments of this disclosure.

[0039] Figures 20a to 20c This is a schematic diagram of the user interface after the target information acquisition control is triggered, according to some other embodiments of this disclosure.

[0040] Figure 21 This is a schematic block diagram of the structure of an information presentation device according to one embodiment of the present disclosure.

[0041] Figure 22 This is a schematic block diagram of an electronic device according to one embodiment of the present disclosure. Detailed Implementation

[0042] The present disclosure will now be described in further detail with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the accompanying drawings.

[0043] It should be noted that, where there is no conflict, the embodiments and features described in this disclosure can be combined with each other. The technical solutions of this disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0044] The implementation process of this solution will be described below through specific embodiments.

[0045] like Figure 1 This is a flowchart illustrating the information presentation method provided in an embodiment of this disclosure. Figure 1 As shown, the method includes: step S101: obtaining the current user information of the current user; wherein, the current user information is used to determine the first similar person.

[0046] It should be noted that the "current user" mentioned here refers to the entity currently using the application software to browse or interact with information. "Current user information" refers to a data set describing the characteristics of the current user, including but not limited to user-initiated preferences, automatically collected behavioral records, or attribute tags; its core function is to serve as a screening criterion. The "first similar person" refers to a user identified based on the current user information who has similar needs and characteristics to the current user; this person can be a virtualized user. In an alternative approach, the "first similar person" can also be a virtualized individual derived from a group of users with similar needs and characteristics to the current user, identified based on the current user information.

[0047] In practical applications, after the application software starts, the first step is to obtain the current user's information. This acquisition process can be in response to the current user's active input on the interface, such as filling in information like housing budget and regional preferences through a form; or it can be automatically collected by the system background, such as reading the current user's browsing history, geographic location information, or registration attribute data. After obtaining the current user information, the system uses it as a matching benchmark to search the user database for users whose similarity to the current user information is higher than a preset threshold, thereby identifying the first similar person. Essentially, this identification process constructs a reference group that highly overlaps with the current user's profile; this group represents a set of people with similar decision-making logic to the current user.

[0048] Step S102: Display target information in the display interface according to the target category; wherein, the target category is used to characterize the content display category of the display interface; the target information is obtained by processing the historical behavior data of the first similar person according to the target category.

[0049] It should be noted that the display interface referred to here refers to the visual window on the screen of an electronic device used to display information to the current user. The target category refers to a classification identifier used to distinguish the types of content displayed on the display interface, such as dividing content into similar person information or target audience information. The target information refers to the content ultimately presented on the display interface, which includes both similar person information and target audience information, and is the result of data processing. The historical behavioral data refers to the operation records of the first similar person within a past time period, such as browsing, saving, showing, and purchasing behavior logs.

[0050] In practical applications, the system acquires historical behavioral data of the first similar person and processes this data according to the target category to obtain target information. Specifically, the target category represents the content display category of the interface, and the system categorizes and aggregates historical behavioral data according to different content dimensions. For example, when the target category is similar people, the system statistically analyzes the attribute distribution and behavioral commonalities of the first similar person to generate information reflecting group characteristics; when the target category is a demand object (e.g., housing, neighborhood, restaurant, attraction, etc.), the system statistically analyzes the frequency and popularity of specific objects (e.g., neighborhood, housing) that the first similar person pays attention to, generating information reflecting the object's popularity. This ensures that the target information includes both macro-level characteristics of the group profile and micro-level data of specific decision-making objects.

[0051] Finally, the target information is displayed in the interface according to the target category. Based on the target category division, the system renders the generated target information into different areas or modules of the display interface. For example, the first area of ​​the interface displays a statistical chart of similar people's information, while the second area displays a list or map markers of the target audience.

[0052] Based on the above-mentioned publicly available scheme, it is known that by using people with the first similarity as an intermediary, an indirect connection is established between the current user and the target information, rather than directly matching based on object attributes. This makes the target information generated based on the historical behavioral data of people with the first similarity have stronger reference value and credibility, and utilizes the group of similar people to assist the current user in making decisions.

[0053] In one or more embodiments of this disclosure, such as Figure 2 This is a flowchart illustrating a target information presentation method as exemplified by an embodiment of this disclosure. Figure 2As shown, displaying target information in the display interface according to target category includes: Step S201: Displaying at least one of an interactive map and / or an information list and / or cards in the display interface. Step S202: Presenting target information in the interactive map, and / or, presenting target information in the information list, and / or, presenting target information in the cards.

[0054] It should be noted that the interactive map mentioned here refers to a map visualization control that supports interactive operations such as zooming, dragging, and clicking. The information list mentioned here refers to a control that displays multiple information records arranged in rows or columns. The card mentioned here refers to a control that displays independent information units in a modular block format.

[0055] For example, such as Figure 3 This is a schematic diagram illustrating the target information display effect as an example of an embodiment of this disclosure. For example... Figure 3 As shown, the system evaluates the matching relationship between the type of target information and the target category. If the target category focuses on geographical distribution, the interactive map is loaded first; if the target category focuses on detailed parameter comparison, the information list is loaded first; if the target category focuses on group statistical summaries, the card is loaded first. Subsequently, the system presents the target information on the interactive map, and / or in the information list, and / or in the card.

[0056] For data containing geographic location attributes (such as community coordinates) in the information of the demand object, the system maps it to specific points on the interactive map and presents it in the form of heat map or marker points. Then, the current user can view the information they need through the interactive map.

[0057] In one alternative approach, for data containing a detailed list of attributes (such as property price and floor plan) within the demand object information, the system maps it to an information list for structured display. The current user can view the specific information of the demand object by scrolling through the list.

[0058] In one alternative approach, for data containing statistical summaries in the information on similar individuals (such as age distribution and preference ratios of similar groups), the system maps them to cards for a general presentation.

[0059] It should be noted that the presentation format of the target information can be any one or more of those mentioned above. For example, in a real estate application scenario, if the target category is information about desired individuals, the system can mark the locations of neighborhoods frequently viewed by the first similar person on the interactive map, and simultaneously list the detailed parameters of these neighborhoods in the information list; if the target category is information about similar individuals, the system can display the statistical conclusion that "80% of the first similar people prefer three-bedroom apartments" in a card. This process avoids the limitations of a single display format, adapting to different types of target information through the combination of multiple controls.

[0060] Based on the publicly available solutions described above, by supporting the presentation of target information in at least one of interactive maps, information lists, and cards, the system can select the optimal visualization scheme according to the characteristics of the target category, transforming abstract historical behavioral data into intuitive visual elements. For current users, this diverse display method allows them to obtain more comprehensive information from multiple dimensions.

[0061] In one or more embodiments of this disclosure, displaying target information in a display interface according to target categories includes: displaying target information representing the category of the demand object in the display interface. The target information representing the category of the demand object is obtained by aggregating the historical behavior data of multiple first-similar individuals based on the demand object categories contained in the historical behavior data of first-similar individuals.

[0062] It should be noted that the category of demand object referred to here refers to the specific object classification involved in the historical behavioral data of the first similar people. For example, in the real estate scenario, it may include specific demand categories such as neighborhoods and housing listings. The target information representing the category of demand object refers to statistical information reflecting the distribution, popularity trend, or selection preference of the category of demand object in the first similar people group. In essence, it is the visualized data that transforms individual behavior into group patterns.

[0063] In practical applications, such as Figure 4 This is a schematic diagram illustrating the aggregation of demand objects as an example of this disclosure. For example... Figure 4 As shown, when the system displays target information according to target categories on the display interface, it first identifies whether the target category is a demand object category. If it is determined that target information representing the demand object category needs to be displayed, the system extracts the demand object category contained in the historical behavior data of the identified first-similar people. Specifically, the system traverses the historical behavior data of multiple first-similar people, analyzes the specific object associated with each behavior record and its category attributes, for example, identifying that a certain browsing record corresponds to a "three-bedroom" apartment in "Community A".

[0064] Subsequently, the system aggregates the historical behavioral data of multiple people with the same primary similarity. This means grouping behavioral records with the same category of desired objects together and calculating the number, proportion, or temporal distribution of records in each group to generate target information representing the category of desired objects. For example, if it is found that 60% of people with the same primary similarity have viewed "Community A" or 40% have saved "three-bedroom" apartment layouts, these statistical results constitute the target information representing the category of desired objects.

[0065] Finally, the system renders the generated target information representing the categories of demand objects onto the display interface for presentation. For example, it displays top-ranked neighborhoods in a list format or shows the distribution of apartment type preferences in a chart format. This process avoids the clutter of directly displaying raw behavioral logs, and uses aggregation processing to organize massive amounts of individual data into group trend information with decision-making reference value. The aggregation processing mentioned here can be understood as the process of summarizing, statistically analyzing, and calculating the historical behavioral data of multiple people with first-degree similarity, including but not limited to frequency statistics, proportion calculation, sorting and filtering, etc. Its purpose is to extract representative group characteristics from discrete behavioral records.

[0066] Based on the publicly available solutions described above, by aggregating the historical behavioral data of multiple individuals with first-degree similarity, the target information representing the category of desired objects can objectively reflect the common selection tendencies of similar groups, providing current users with verified decision-making references and reducing the information costs and trial-and-error risks associated with independent selection of desired objects. Furthermore, since the target information is based on statistical results obtained through aggregation rather than specific behavioral records of individual users, data anonymization is naturally achieved during the display process, effectively protecting the privacy and security of individuals with first-degree similarity. In addition, this aggregation-based display method based on desired object categories enables current users to quickly identify desired objects with high interest and recognition among similar groups. Especially in high-decision-cost scenarios such as real estate, it helps users quickly identify potential target communities or apartment types, significantly improving the efficiency of information acquisition and the accuracy of decision-making, ensuring the relevance and practicality of the displayed content.

[0067] In one or more embodiments of this disclosure, displaying target information in the display interface according to target categories includes: displaying target information representing similar person categories in the display interface; wherein, the target information representing similar person categories is obtained by organizing the demand objects contained in the historical behavior data according to the historical behavior data of the first similar person.

[0068] It should be noted that the target information representing similar people categories mentioned here refers to information used to describe the group type or characteristic labels to which the first similar people belong. It reflects the commonalities of the group in terms of demand tendencies, behavioral patterns or attribute distribution. For example, it could be a first-time homebuyer group, a school district demand group or an improvement demand group.

[0069] In practical applications, such as Figure 5 Illustrations are provided to illustrate similar individuals as exemplified in this disclosure. For example... Figure 5 As shown, when the system displays target information in the display interface according to the target category, if the target category points to the similar people category, the system displays target information representing the similar people category in the display interface.

[0070] Specifically, the system first acquires the historical behavior data of the most similar person and extracts the target users contained within that data. Then, the system organizes these target users according to their historical behavior data. In one optional approach, the target users in the historical behavior data can be organized either chronologically or based on their proximity to the current user.

[0071] The system analyzes the common characteristics of the target groups. For example, if the target groups mostly purchase small apartments priced below 3 million yuan, it infers that the first similar individuals belong to the "first-time homebuyer group"; if the target groups mostly purchase properties near key schools, it infers that the first similar individuals belong to the "school district demand group." The system uses these inferred group characteristic labels or statistical distributions as target information to characterize the categories of similar individuals. For example, the system can generate descriptive information such as "80% of the current similar group are first-time homebuyers" or "this group mainly focuses on properties along subway lines." This enables the inference of group attributes through behavior.

[0072] The system renders the generated target information representing similar human categories onto the display interface, typically in the form of a tag cloud, group profile cards, or statistical charts, allowing the current user to intuitively understand the basic characteristics of the reference group. This process avoids directly displaying the personal privacy information of the most similar individuals; instead, it abstracts group categories by organizing the target audience, achieving a transformation from specific behaviors to abstract group characteristics.

[0073] Based on the publicly available solutions described above, by displaying target information representing similar user categories, users can clearly understand which similar group the system's recommendations are based on. For example, they can learn that the reference group consists of first-time homebuyers of the same age, thus judging the reference value of that group's behavioral data for their own decision-making. This transparent display of group characteristics solves the trust deficiency problem caused by black-box recommendations in recommendation systems, enabling users to assess the credibility of target information based on the matching degree of group categories.

[0074] In one or more embodiments of this disclosure, such as Figure 6 This is a flowchart illustrating the current user information acquisition method provided in this embodiment of the disclosure. Figure 6 As shown, obtaining the current user's current user information includes: Step S601: Displaying a user information collection control on the display interface. Step S602: Responding to the current user's trigger operation on the user information collection control, obtaining the current user's current user information.

[0075] like Figure 7 This is a schematic diagram illustrating the display effect of a user information collection control, as exemplified in an embodiment of this disclosure. Figure 7As shown, when the application software in the electronic device starts and enters the display interface, the system first displays a user information collection control on the display interface. The specific form of this control can be configured according to the application scenario; for example, in a real estate application, it can be represented as a "Requirements Fill" button or a floating requirement configuration panel. The purpose of displaying the user information collection control is to provide the current user with an entry point to actively express their needs, ensuring that the system can obtain the current user information that reflects the current user's true intentions. Subsequently, the system listens for interaction events on the display interface and, in response to the current user's trigger operation on the user information collection control, obtains the current user's current user information.

[0076] Specifically, when the current user clicks the user information collection control, the system can display detailed information input controls (such as apartment type selection controls, residential attribute controls, etc.). Alternatively, it can directly read the parameters configured by the current user on the control (such as the price range of properties selected via a slider) and use this data as the current user information. After obtaining the current user information, the system uses it as a key input parameter to determine the first similar person, that is, to match real users with similar characteristics in the user database using the current user information. Obtaining the current user information through explicit control interaction, compared to relying entirely on passively collected behavioral data from the system backend, can more accurately capture the current user's real-time changes in needs. Through the user information collection control, the current user can actively intervene in the generation process of the current user information, thereby directly affecting the determination of the first similar person, and thus affecting the target information generated subsequently based on the historical behavioral data of the first similar person.

[0077] Based on the publicly available solutions described above, by displaying user information collection controls on the interface and responding to trigger actions to obtain current user information, the system can ensure that the benchmark data used to match similar groups is explicitly known and approved by the current user. This avoids the problem of inaccurate identification of the first similar person due to data biases automatically collected by the system (e.g., browsing history generated by accidental operations). Simultaneously, this proactive collection method enhances the current user's sense of participation and privacy control, as the current user clearly understands which information is used to identify similar groups, complying with the privacy protection principles of data minimization and informed consent. Furthermore, when the current user's needs change, the current user information can be updated again through trigger actions, thereby dynamically updating the first similar person and subsequent target information, ensuring that the information presentation results are always synchronized with the current user's latest needs.

[0078] In one alternative, in addition to collecting user information through user collection controls, user information can also be obtained through databases or servers without the current user actively providing or participating in the process.

[0079] In one or more embodiments of this disclosure, such as Figure 8 This is a flowchart illustrating a current user information update method provided in an embodiment of this disclosure. Figure 8 As shown, the method further includes: Step S801: Displaying a user information collection control on the display interface. Step S802: In response to the current user's triggering operation on the user information collection control on the display interface, updating the first similar person. Step S803: Updating and displaying target information on the display interface according to the target category; wherein, the target information is obtained by processing the historical behavior data of the updated first similar person according to the target category.

[0080] In practical applications, a user information collection control is displayed on the interface. This control serves as the main entry point for the current user to interact with the system regarding their needs. It integrates a requirement change control and / or a threshold change control.

[0081] When the current user wishes to adjust the reference group, they can modify specific demand parameters through the demand change control, such as adjusting the housing budget from 3 million to 4 million, or adjust the similarity judgment criteria through the threshold change control, such as adjusting the similarity threshold from 0.8 to 0.9. In response to the current user's triggering operation on the demand change control and / or threshold change control in the user information collection controls on the display interface, the system immediately initiates the process of updating the first similar person.

[0082] If a requirement change control is triggered, the system re-filters users in the database based on the changed requirement parameters. If a threshold change control is triggered, the system redefines the boundaries of the user group based on the changed similarity criteria. In either case, the final result is to generate the updated first similar person, ensuring that the reference group is consistent with the current user's latest intent.

[0083] Furthermore, the system updates and displays target information according to target category on the interface. Specifically, the system obtains the updated historical behavior data of the first similar person, processes the historical behavior data according to the target category, and obtains new target information.

[0084] For example, if the target category is a demand object category, the system recalculates the popularity of the neighborhoods followed by the first similar person after the update; if the target category is a similar person category, the system reanalyzes the group characteristics of the first similar person after the update. Finally, the system renders the new target information onto the display interface, replacing the original display content. This process achieves a closed-loop linkage from user input change to interface information update, ensuring that the content displayed on the interface is always based on the latest data of the first similar person.

[0085] Based on the publicly available solutions described above, the user can actively intervene in the process of determining the first similar person through the demand change control and threshold change control. This allows for precise control over the scope and characteristics of the reference group, avoiding potential biases from automatic system matching. Simultaneously, the updated target information reflects the collective wisdom resulting from changes in the user's needs in real time, making decision-making more timely and targeted. Furthermore, this instant update mechanism enhances the user experience; the user can intuitively see the changes in information after parameter adjustments on the display interface without refreshing the page or re-entering the application, significantly reducing the operational costs of exploring different demand solutions.

[0086] In one or more embodiments of this disclosure, such as Figure 9 This is a flowchart illustrating another current user information update method provided in an embodiment of this disclosure. Figure 9 As shown, it also includes: Step S901: In response to the current user's current user information update request, update the first similar person based on the updated current user information. Step S902: Update and display target information in the display interface according to the target category; wherein, the target information is obtained by processing the historical behavior data of the updated first similar person according to the target category.

[0087] In practical applications, the system continuously monitors the data status related to the current user. When changes are detected in the current user's information—for example, when the system backend automatically improves the user profile based on the current user's new behavior logs, or when the current user's preferences on other devices are synchronized to the current device—the system generates a current user information update request. In response to this request, the system updates the first similar person based on the updated current user information. This update is completed without the current user's active intervention.

[0088] The system uses the updated current user information as the new matching benchmark, re-searches the user database for users who meet the similarity criteria, and thus obtains the updated first similar person. This ensures that the reference group reflects the current user's latest status in real time, rather than remaining in a historical state.

[0089] Furthermore, the system updates and displays target information according to the target category on the interface. Specifically, the system obtains the updated historical behavior data of the first similar person and processes the historical behavior data according to the target category to obtain new target information. For example, if the target category is the demand object category, the system recalculates the popularity of the neighborhoods followed by the first similar person after the update; if the target category is the similar person category, the system reanalyzes the group characteristics of the first similar person after the update.

[0090] The system renders the new target information onto the display interface, replacing the original content. This process achieves automatic linkage between changes in backend data and updates to interface information, ensuring that the content displayed on the interface is always based on the latest data of the most similar person.

[0091] Based on the publicly available solutions described above, the automatic triggering of the update process in response to current user information update requests reduces the operational burden on the current user. This is especially true in scenarios where current user information evolves naturally due to the accumulation of system backend data, allowing the user to obtain the latest target information without active intervention. Furthermore, by reprocessing the historical behavioral data of the first similar person after the update to obtain the target information, the recommendation results are ensured to highly match the current user's actual needs, avoiding recommendation bias caused by outdated user information.

[0092] In one or more embodiments of this disclosure, such as Figure 10 This is a flowchart illustrating a method for displaying second similar people provided in an embodiment of this disclosure. Figure 10 As shown, the method further includes: Step S1001: In response to a triggering operation on target information, determine the first similar person corresponding to the triggered target information. Step S1002: Determine the second similar person based on the first similar person. Step S1003: Aggregate the target information based on the demand object category in the historical behavior data of the second similar person. Step S1004: Display the target information corresponding to the second similar person in the display interface according to the object category.

[0093] In practical applications, such as Figure 11 A schematic diagram showing a second similar person provided in an embodiment of this disclosure. (See attached diagram.) Figure 11 As shown, the display interface shows target information generated based on people with the first similarity. This target information can be presented in the form of an interactive map, an information list, or cards. This trigger action can be executed when the current user wants to gain a deeper understanding of the group characteristics corresponding to specific information. Specifically, if the target information is displayed on an interactive map, the current user can click on an icon on the map (e.g., a property name or icon). If the target information is displayed in an information list, the current user can click on a specific property entry in the list. If the target information is displayed in cards, the current user can swipe or click on a specific property card.

[0094] In response to a triggering operation on target information, the system first identifies the first similar person corresponding to the triggered target information. The system then identifies a specific subset of the first similar people on which the triggered target information is based; for example, it determines which similar users contributed the community's popularity data. Subsequently, the system determines the second similar people based on the first similar people.

[0095] For example, the first similar person can be used as a new benchmark to search the database for users with higher similarity to the subset, or users with more specific needs can be selected based on the feature tags of the subset, thus forming the second similar person.

[0096] Next, the system aggregates the target information based on the historical behavioral data of the second most similar individuals to obtain new target information, thus achieving a deeper understanding of the information hierarchy. For example, it can statistically analyze the specific apartment types or related neighborhoods preferred by the second most similar individuals. Finally, the system displays the target information corresponding to the second most similar individuals in the interface, differentiated by object category. The system can render the new target information to a new interface level or an extended area of ​​the current interface, and display it separately according to the target object category. For example, it can distinguish the neighborhood information after the lower-level analysis from ordinary neighborhood information using different visual styles. By constructing a multi-level similarity group lower-level analysis link, through the path from the first most similar person, target information, trigger, second most similar person to the new target information, more reference ideas can be provided to the current user.

[0097] Based on the publicly available solutions described above, by supporting triggering operations based on target information to determine the second most similar individuals and generate new target information, the current user can gain a multi-level understanding of the behavioral patterns of similar groups, thus solving the problem of insufficient information depth in a single recommendation. When exploring more target information at multiple levels, the system still uses the first most similar individuals to find the second most similar individuals, and then uses the second most similar individuals to find the corresponding target information, rather than directly using the current user's information for exploration. This results in a more diverse range of target users that are closer to the current user's needs.

[0098] In one or more embodiments of this disclosure, such as Figure 12 This is a flowchart illustrating a method for displaying second similar people provided in an embodiment of this disclosure. Figure 12 As shown, it also includes: Step S1201: In response to the triggering operation on the target information, determine the first similar person corresponding to the triggered target information. Step S1202: Determine the demand object based on the first similar person. Step S1203: Display the demand objects in the display interface according to object category.

[0099] In practical applications, such as Figure 13 This is a schematic diagram illustrating the effect of displaying a desired object by showing similar people, as provided in an embodiment of this disclosure. Figure 13 As shown, the target information generated based on the first similar person is displayed in the display interface. The target information can be presented in the form of interactive maps, information lists or cards, providing the current user with macro-level insights into group behavior.

[0100] When the current user wants to view specific recommended properties, a trigger action can be executed. Specifically, if the target information is displayed on an interactive map, the current user can click on the property icon or symbol on the map (e.g., a specific heatmap location). If the target information is displayed in an information list, the current user can click on a specific item in the list. If the target information is displayed in a card, the current user can swipe or click on the corresponding property card. In response to the trigger action on the target information, the system determines the first similar person to the triggered target information.

[0101] The system identifies a specific subset of the first similar individuals upon which the triggered target information is based, such as determining which similar users contributed the community's popularity data or statistical results. The system then determines the target of the demand (e.g., a specific community) based on these first similar individuals.

[0102] Furthermore, specific objects associated with the first similar person are extracted. For example, the most frequently occurring specific neighborhoods or properties in the historical behavior of the corresponding first similar person are statistically analyzed, and these entities are identified as demand objects. This realizes the transformation from group statistical information to specific entity objects, providing the current user with options that can be directly used for decision-making.

[0103] The system renders the identified demand objects onto the display interface and differentiates them according to object category. For example, it distinguishes different neighborhoods in different areas with different colored markers, or displays properties in different price ranges through different group lists. It constructs a multi-level similarity group exploration path, from the first similar person, target information, trigger, second similar person, to new target information, providing the current user with more reference ideas.

[0104] As can be seen from the above-disclosed solution, this embodiment directly determines the target of demand based on people with the first similarity, shortens the information access link, and enables the current user to obtain specific recommended housing or communities more quickly. The target of demand found through people with the first similarity is more in line with the current user's needs.

[0105] In one or more embodiments of this disclosure, such as Figure 14 This is a flowchart illustrating the target information generation method provided in an embodiment of this disclosure. Figure 14 As shown, the method for generating target information includes: Step S1401: Obtaining the historical behavior data and user attribute data of the first similar person. Step S1402: De-identifying the historical behavior data and user attribute data to obtain the target information of the first similar person.

[0106] It should be noted that the historical behavioral data mentioned here refers to the operation records of the first similar person within a past time period, such as browsing history, search history, favorites history, or transaction history. The user attribute data mentioned here refers to data describing the static or dynamic characteristics of the first similar person, such as age, occupation, family structure, income range, or geographic location tags. The anonymization process mentioned here refers to the technical process of processing data to remove or modify information that can identify a specific individual, including but not limited to removing personal identifiers, data generalization, noise addition, or statistical aggregation.

[0107] In practical applications, when generating target information representing similar user categories, the system first acquires the historical behavioral data and user attribute data of the first similar individuals. The core principle of this step lies in multi-source data fusion, because relying solely on historical behavioral data may not fully characterize group features (e.g., knowing what a user bought, but not knowing what the user is like), while combining user attribute data can more accurately define group categories (e.g., "first-time homebuyers around 30 years old"). The system retrieves the historical behavioral data and user attribute data corresponding to the users constituting the first similar individuals from the database, forming the original dataset.

[0108] Subsequently, the system anonymizes historical behavioral data and user attribute data. Specifically, this involves deleting unnecessary data from user attribute data and replacing necessary data with virtual data expressing similar meanings to obtain virtual attribute data. Based on the virtual attribute data and historical behavioral data, similar person target information is generated. For user attribute data, the system removes personal identification information (such as name, mobile phone number, and ID card number) and converts continuous features into interval features (e.g., converting a specific age "32 years old" into a "30-35 years old" interval). For historical behavioral data, the system severs the direct association between the data and specific user IDs, retaining only anonymized fields such as behavior type and timestamp. This process ensures that no individual user's complete privacy information is leaked during processing.

[0109] Finally, the system obtains target information about the first similar person based on the anonymized data. For example, the system statistically analyzes the distribution of anonymized attributes to generate a label such as "80% of this group are 30-35 years old," or it generates a conclusion such as "this group prefers three-bedroom apartments" based on anonymized behavioral data. These results constitute target information representing similar person categories and can ultimately be displayed on the interface.

[0110] Based on the publicly available solutions described above, by acquiring historical behavioral data and user attribute data and performing anonymization processing, the system can utilize rich real-world data to generate highly valuable target information representing similar human categories. This allows users to gain a deeper understanding of the characteristics of similar groups while strictly avoiding the risk of disclosing the personal privacy of the first similar person. This approach complies with data security laws and regulations, enhancing users' trust in the application software because they know that the displayed information is a securely processed group statistical result, not individual privacy.

[0111] In one or more embodiments of this disclosure, such as Figure 15 This is a flowchart illustrating the method for displaying narrative content provided in an embodiment of this disclosure. Figure 15 As shown, the target information representing similar person categories is displayed on the display interface, including: Step S1501: Analyzing the behavioral paths and object access and selection decision patterns in the historical behavioral data of the first similar person. Step S1502: Based on the narrative template, the behavioral paths and object access and selection decision patterns are filled into the narrative template, and narrative content is generated. Step S1503: The narrative content is displayed on the display interface.

[0112] It should be noted that the behavioral path referred to here is the sequence of operations performed by the first similar person in the process of selecting a desired object, such as the order of access from searching keywords to viewing the list and then to the details page. Object access and selection decision patterns refer to the regular logic exhibited by the first similar person when accessing and selecting desired objects, such as decision-making tendencies that prioritize price ranges, geographical locations, or apartment layouts—logic that is difficult to quantify when viewing and purchasing property. The narrative template referred to here is a predefined text structure framework containing placeholders for filling data, used to transform structured data into natural language text. The narrative content referred to here is natural language text generated based on the narrative template, describing the behavioral characteristics or decision-making logic of the first similar person.

[0113] In practical applications, such as Figure 16 This is a schematic diagram illustrating the effects of the narrative content provided in the embodiments of this disclosure. For example... Figure 16 As shown, when the system needs to display target information representing similar user categories on the display interface, it first analyzes the behavioral paths and object access and selection decision patterns in the historical behavioral data of the most similar users. Specifically, it traverses the historical behavioral data of the most similar users to identify the operation sequences that appear in chronological order as behavioral paths. For example, it finds that the user first views properties along the subway line and then views properties in the school district. At the same time, it analyzes the key influencing factors when the user finally selects an object as the object access and selection decision pattern. For example, it finds that the user ultimately chooses the one with the lowest price after comparing three neighborhoods.

[0114] The system uses narrative templates to populate behavioral paths and object access and selection decision patterns, generating narrative content. Specifically, the system selects a narrative template that matches the group characteristics of people with the highest similarity, such as the template "Similar groups typically focus on [path nodes] first, and ultimately choose [objects] due to [decision patterns]." The system then fills in the analyzed path nodes and decision logic into placeholders, generating fluent natural language text, i.e., the narrative content. For example, it generates the text "Users similar to you mostly focus on communities along the subway line first, and ultimately choose community A due to commuting convenience." Finally, the system displays the narrative content on the interface. The system renders the generated narrative content in specific areas of the display interface, such as as supplementary information representing similar user categories or as a standalone card, allowing the current user to directly read a description of the behavioral logic of similar groups. By transforming abstract statistical data into concrete, story-like expressions and using narrative templates to generate data from text, the system ensures that the narrative content is both based on real data and readable.

[0115] Based on the publicly available solutions described above, by displaying narrative content on the interface, the system transforms complex historical behavioral data into natural language descriptions that are easy for the current user to understand, reducing the cognitive load on the user's interpretation of statistical data. The narrative content not only tells the current user what similar groups did, but also explains why these groups acted this way through behavioral paths and object access and selection decision-making patterns, providing a deeper level of decision-making logic and helping the current user verify the rationality of their own decision-making process. Furthermore, the method of generating narrative content based on narrative templates has good scalability. The system can adapt to different scenarios or group characteristics by updating the narrative templates, ensuring that the content displayed on the interface always aligns with the current user's reading habits. This significantly enhances the user experience and the persuasiveness of the target information, making the target information representing similar human categories more practically meaningful.

[0116] In one or more embodiments of this disclosure, the method for generating target information of demand objects includes: classifying the historical behavior data of people with first similarity according to the demand object category; calculating the frequency data of user behavior in each demand object category; and generating target information of demand objects that reflects the popularity distribution of demand objects based on the frequency data.

[0117] In practical applications, when generating target information for the target audience, the system first categorizes the historical behavior data of the most similar individuals according to the target audience category. The system groups the massive amounts of discrete historical behavior data generated by these individuals based on preset target audience categories. For example, all browsing records related to "Community A" are grouped into one category, and all collection records related to "Three-Bedroom Apartment" are grouped into another, thus forming data subsets organized by category. Subsequently, the system calculates the frequency data of user behavior within each target audience category. Specifically, the system counts the number of behavior records under each category, such as calculating the total number of times "Community A" was viewed by the most similar individuals, or the total number of people who collected "Three-Bedroom Apartment," generating quantified frequency data.

[0118] The system generates target information reflecting the popularity distribution of demand objects based on frequency data. It sorts or categorizes demand objects according to the magnitude of frequency data; for example, it marks the most frequent communities as "high-popularity" and the least frequent as "low-popularity," or directly generates a popularity ranking list, forming a distribution of demand object popularity, and encapsulates this into target information. By objectively reflecting the popularity of objects through the frequency of group behavior, the system ensures that the generated target information is based on real behavioral statistics rather than subjective speculation. Furthermore, the entire process is based on aggregated calculations of group data of people with the highest similarity, without involving the specific privacy details of individual users.

[0119] Based on the publicly available solutions described above, by classifying the historical behavioral data of the most similar individuals according to their target audience categories and calculating frequency data, a distribution of target audience popularity reflecting the preferences of a real group can be generated. This allows current users to intuitively understand which targets are more popular among similar groups, thereby reducing decision-making uncertainty. Furthermore, the method of generating target audience information based on frequency data has good interpretability. Current users can clearly understand that the popularity ranking is based on actual behavioral frequency, enhancing the credibility and persuasiveness of the target audience information. This helps current users quickly identify high-popularity target audiences, significantly improving the efficiency of information presentation and the accuracy of decision support.

[0120] Figures 17a to 17e This is a schematic diagram of the user interface of some other embodiments of this disclosure.

[0121] refer to Figures 17a to 17c This displays the user's status after logging into the application. The application provides entry controls for software functions, which users can trigger to view collected user demand information, execute demand information collection processes, and view one or more target information determined based on similar people (i.e., the first similar person described above).

[0122] refer to Figures 17a to 17cThe entry control can be in the form of a card, which may include one or more sub-controls. Different sub-controls are configured to trigger different functions. For example, triggering the "My Home Purchase Plan" (or "Fill in My Needs") sub-control can display the filled-in needs information or trigger the needs information collection function. Triggering the "Similar People" sub-control can display target information determined based on similar people.

[0123] Figure 17d This is a schematic diagram illustrating the triggering of the "similar person" control in one embodiment of this disclosure.

[0124] refer to Figure 17d For example, it displays the user's profile information (e.g., a rendered image including a cartoon character), and also displays an overall overview of similar people. Figure 17d The displayed interface can be swiped down or up.

[0125] The display interface provides one or more target information retrieval controls in response to the triggering of the "Similar People" control. In response to the triggering of different target information retrieval controls, the target information can be aggregated and displayed from different target information aggregation dimensions.

[0126] Figures 18a to 18e This is a schematic diagram of the user interface after the target information acquisition control of some embodiments of this disclosure is triggered.

[0127] For example, for Figure 17d After the "View All Users" control in the target information retrieval function is triggered, it can display... Figure 18a The displayed interface aggregates target information such as housing listings from different dimensions of similar people.

[0128] Continue to refer to Figure 18a The system can aggregate and display target information by different types of related information (e.g., number of properties shown, number of properties under negotiation, and information on properties sold), and can display it in the form of a list.

[0129] Figure 18a The display interface shown can also display an interactive map, which shows the neighborhood to which the target information such as the property belongs (the neighborhood can be understood as aggregated property information).

[0130] In some implementations, in Figure 18a Based on this, users can trigger the interactive map (e.g., by clicking), or they can trigger property aggregation information controls on the interactive map (e.g., "Jinyu Huafu"), allowing the interactive map to fill the entire display interface, such as... Figure 18bAs shown, aggregated information of the target information, such as housing aggregate information, can also be displayed in the form of cards or overlays. These housing aggregate information in the form of cards or overlays can be swiped left or right to display different housing aggregate information on the display interface.

[0131] In some implementations, in Figure 18a Based on this, users can swipe up to manipulate the target information control (which can be a card, a floating layer, or a list) to display... Figure 18c The display interface shown.

[0132] exist Figure 18c Scroll up further to see the aggregated property listings displayed as a list.

[0133] In some embodiments of this disclosure, the aforementioned target information set information, such as housing aggregation information, provides various "similar people" sub-controls. Users can trigger each "similar person" sub-control to display the target information associated with that "similar person". The associated target information can be house viewing trajectory information, such as the house viewing trajectory information of the "similar person", the house viewing of the house with the negotiated price, etc.

[0134] Figure 18e The system displays the house-viewing trajectory information of a "similar person (Zhuge Liang #123)", and can provide one or more ways to display house-viewing trajectory information, such as... Figure 18e It provides viewing route information in both interactive map and list card format.

[0135] Continue to refer to Figure 17d Users can swipe up on the currently displayed screen to reveal more content, such as... Figure 17e As shown, more target information acquisition controls are displayed. When these target information controls are triggered, they will aggregate and display the target information according to different target information aggregation dimensions.

[0136] Figures 19a to 19e This is a schematic diagram of the user interface after the target information acquisition control is triggered, according to some other embodiments of this disclosure.

[0137] For example, for Figure 17e Once the "Where did they buy?" target information retrieval control is triggered, it can display... Figure 19a The display interface shown aggregates and displays target information such as housing listings from different dimensions (e.g., housing listings, i.e., neighborhoods).

[0138] Continue to refer to Figure 19aIt can aggregate and display target information based on different types of related information (such as the number of similar people, mainstream apartment types, transaction cycles, etc.), and can be displayed in the form of a list.

[0139] Figure 19a The display interface shown can also display an interactive map, which shows the neighborhood to which the target information such as the property belongs. The neighborhood can be understood as aggregated property information.

[0140] In some implementations, in Figure 19a Based on this, users can trigger the interactive map (e.g., by clicking), or they can trigger property aggregation information controls on the interactive map (e.g., "Jinyu Huafu"), allowing the interactive map to fill the entire display interface, such as... Figure 19b As shown, aggregated information of the target information, such as housing aggregate information, can also be displayed in the form of cards or overlays. These housing aggregate information in the form of cards or overlays can be swiped left or right to display different housing aggregate information on the display interface.

[0141] In some implementations, in Figure 19b Based on this, users can trigger (first type of trigger, such as clicking) the aggregated control for each property listing, thereby displaying... Figure 19c The displayed interface is shown for reference. Figure 19c On the interactive map, one or more property information aggregation controls that are related to the triggered property information aggregation control are displayed. For example, clicking on "Jinyu Huafu" can display other properties that similar people who bought in the "Jinyu Huafu" community have also viewed, such as "Mingkeyuan", etc.

[0142] In some implementations, in Figure 19a Based on this, the target information control can be swiped up or down. For example, after swiping up, it can display... Figure 19d The interface shown is designed to display more target information in list format.

[0143] exist Figure 19a , 19b Based on one of 19c and 19d, in some implementations, the aforementioned target information set, such as housing aggregation information, provides various "housing aggregation information" controls. Users can trigger each "housing aggregation information" control (second type trigger, such as double-click) to display the target information associated with that "housing aggregation information." The associated target information, such as housing information, can be displayed in a list card format, such as... Figure 19e As shown.

[0144] Figures 20a to 20c This is a schematic diagram of the user interface after the target information acquisition control is triggered, according to some other embodiments of this disclosure.

[0145] refer to Figure 17e For example, for Figure 17e After the "What are you all looking at?" target information retrieval control is triggered, it can display... Figure 20a The display interface shown aggregates and displays target information such as housing listings from different dimensions (e.g., housing listings, i.e., neighborhoods).

[0146] Continue to refer to Figure 20a The system can display target information in different types of related information (e.g., property information, number of viewers, current stage, etc.), and can be displayed in list form.

[0147] Figure 20a The display interface shown can also display an interactive map, which shows the neighborhood to which the target information such as the property belongs. The neighborhood can be understood as aggregated property information.

[0148] In some implementations, in Figure 20a Based on this, users can trigger the interactive map (e.g., by clicking), or trigger property aggregation information controls on the interactive map (e.g., "Jinyu Huafu"), allowing the interactive map to fill the entire display screen. (See reference...) Figure 20b It can also display aggregated information of target information, such as housing aggregated information, in the form of cards or overlays. These housing aggregated information in the form of cards or overlays can be swiped left or right to display different housing aggregated information on the display interface.

[0149] In some implementations, in Figure 20b Based on this, users can zoom in and out of the interactive map, which can display aggregated housing information, such as building information in a community, and display target information, i.e., housing information, on the building information.

[0150] exist Figure 20a Based on this, users can swipe up or down on the target information control, allowing the list-style target information to fill the entire screen (e.g., swiping up), such as... Figure 20c As shown; or to make the interactive map fill the entire interface (e.g., by swiping down).

[0151] exist Figures 17a to 20c Based on the above and related implementation methods, this disclosure also provides the following information presentation methods for technical solutions.

[0152] Based on the information presentation methods of the above-mentioned related embodiments, the step of displaying target information in the display interface according to target category includes: The display interface provides multiple target information acquisition controls; In response to the triggering of different target information acquisition controls, the target information is aggregated and displayed according to different target information aggregation dimensions.

[0153] Based on the information presentation methods of the above-mentioned related implementation methods, the different target information aggregation dimensions include at least the similar people dimension and the demand object aggregation dimension; Under the dimension of similar people, the target information is aggregated according to different first similar people, and then displayed in the display interface according to the first similar people. Under the aforementioned demand object aggregation dimension, the target information is aggregated according to the demand object aggregation information, and then displayed in the display interface according to the demand object aggregation information.

[0154] Based on the information presentation methods of the above-mentioned related embodiments, a similar person sub-control is provided in the target information displayed according to the first similar person distinction; In response to the trigger operation of the similar person sub-control, the house viewing trajectory information of the corresponding first similar person is displayed.

[0155] Based on the information presentation methods of the above-mentioned related implementation methods, a demand object aggregation information control is provided in the target information that is displayed differently according to the demand object aggregation information. In response to the triggering operation of the demand object aggregation information control, other demand object aggregation information that is related to the demand object aggregation information is displayed on the interactive map.

[0156] Based on the information presentation methods of the above-mentioned related embodiments, in response to the second type of triggering operation on the aggregated information of the demand object, the housing information associated with the aggregated information of the demand object is displayed.

[0157] Based on the information presentation methods of the above-mentioned related embodiments, it also includes: In response to a zoom operation on the interactive map, the next level of information on the aggregated information of the demand object is displayed on the interactive map, and the target information is displayed on the next level of information.

[0158] Based on the information presentation methods of the above-mentioned related embodiments, the step of displaying target information in the display interface according to target category includes displaying target information in one or more forms, such as list form and interactive map form; In response to a first-direction swipe operation on the display interface, the target information in list form fills the display interface; In response to a second-direction swipe operation on the display interface, the interactive map fills the display interface.

[0159] It should be noted that the text content, control shapes, interface colors, and renderings of cartoon characters shown in the accompanying drawings are all exemplary and intended to explain the technical solutions of the various embodiments of this disclosure. They should not be construed as limitations on the technical solutions of the various embodiments of this disclosure.

[0160] Based on any of the above embodiments, this disclosure also provides an information presentation device. Figure 21 This is a schematic block diagram of the structure of an information presentation device according to one embodiment of the present disclosure. Figure 21 As shown, the information presentation device includes: an acquisition module 171, used to acquire current user information of the current user; wherein the current user information is used to determine the first similar person; and a display module 172, used to display target information in the display interface according to the target category; wherein the target category is used to characterize the content display category of the display interface; and the target information is obtained by processing the historical behavior data of the first similar person according to the target category.

[0161] Display module 172 is used to display at least one of an interactive map and / or an information list and / or a card in a display interface; to present target information in the interactive map, and / or, to present target information in the information list, and / or, to present target information in the card.

[0162] Display module 172 is used to display target information representing the category of demand object in the display interface; wherein, the target information representing the category of demand object is obtained by aggregating the historical behavior data of multiple first-similar people based on the demand object category contained in the historical behavior data of first-similar people.

[0163] Display module 172 is used to display target information representing similar human categories in the display interface; wherein, the target information representing similar human categories is obtained by organizing the demand objects contained in the historical behavior data according to the historical behavior data of the first similar person.

[0164] The acquisition module 171 is used to display a user information collection control on the display interface; in response to the current user's trigger operation on the user information collection control, it acquires the current user's current user information.

[0165] Display module 172 is used to display user information collection controls on the display interface; in response to the current user's trigger operation on the user information collection controls on the display interface, the first similar person is updated; the target information is updated and displayed on the display interface according to the target category; wherein, the target information is obtained by processing the historical behavior data of the updated first similar person according to the target category.

[0166] Display module 172 is used to respond to the current user's current user information update request, update the first similar person based on the updated current user information; and update and display target information in the display interface according to the target category; wherein, the target information is obtained by processing the historical behavior data of the updated first similar person according to the target category.

[0167] Display module 172 is used to respond to a trigger operation on target information, determine the first similar person corresponding to the triggered target information; determine the second similar person based on the first similar person; perform aggregation processing on the demand object category in the historical behavior data of the second similar person to obtain target information; and display the target information corresponding to the second similar person in the display interface according to the object category.

[0168] Display module 172 is used to respond to a trigger operation on target information, determine the first similar person corresponding to the triggered target information; determine the demand object based on the first similar person; and display the demand object in the display interface according to the object category.

[0169] The acquisition module 171 is used to acquire the historical behavior data and user attribute data of the first similar person; and to perform anonymization processing on the historical behavior data and user attribute data to obtain the target information of the first similar person.

[0170] Display module 172 is used to analyze the behavioral paths and object access and selection decision patterns in the historical behavioral data of the first similar person; based on the narrative template, the behavioral paths and object access and selection decision patterns are filled into the narrative template and the narrative content is generated; the narrative content is displayed on the display interface.

[0171] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0172] The entity executing the information presentation method in the specific embodiments of this disclosure may be an electronic device such as a server (including a local server or a cloud computing platform).

[0173] Therefore, based on any of the above embodiments, this disclosure also provides an electronic device that can execute the information presentation method of any of the embodiments described above.

[0174] Figure 22 This is a schematic block diagram of an electronic device according to one embodiment of the present disclosure.

[0175] The hardware structure of electronic device 1000 can be implemented using a bus architecture. The bus architecture can include any number of interconnect buses and bridges, depending on the specific application and overall design constraints of the hardware. Bus 1100 connects various circuits including one or more processors 1200, memory 1300, and / or hardware modules. Bus 1100 can also connect various other circuits 1400 such as peripherals, voltage regulators, power management circuits, external antennas, etc. Bus 1100 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one connection line is used in this figure, but this does not indicate that there is only one bus or one type of bus.

[0176] This disclosure also provides a readable storage medium storing a computer program that, when executed by a processor, is used to implement the methods described above. A "readable storage medium" can be any means capable of containing, storing, communicating, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples of a readable storage medium include: an electrical connection with one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable read-only memory (CDROM), etc.

[0177] This disclosure also provides a computer program product, the methods of which can be implemented wholly or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented wholly or partially as a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed, all or part of the processes or functions of this disclosure are performed.

[0178] Computer programs or instructions can be stored in a readable storage medium or transferred from one readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The readable storage medium can be any available medium capable of access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video optical disc; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be a volatile or non-volatile storage medium, or it can include both volatile and non-volatile types of storage media.

[0179] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0180] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0181] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0182] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0183] It is understood that before using the technical solutions disclosed in the embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained. Simultaneously, sensitive information should be de-identified (e.g., de-identification, anonymization, etc.). For example, in response to receiving a user's active request, a prompt message can be sent to the user to clearly inform them that the requested operation will require the acquisition and use of their personal information. This allows the user to choose whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media that perform the operations of this disclosure's technical solutions, based on the prompt message. As an optional but non-limiting implementation, the method of sending a prompt message to the user in response to receiving a user's active request can be, for example, a pop-up window, where the prompt message can be presented in text form. Furthermore, the pop-up window can also include a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0184] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0185] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0186] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0187] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0188] At the same time, it is understood that the data involved in this disclosed technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0189] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., refer to specific features, structures, or characteristics described in connection with that embodiment / mode or example, which are included in at least one embodiment / mode or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0190] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0191] Those skilled in the art should understand that the above embodiments are merely for illustrating the present disclosure and are not intended to limit the scope of the disclosure. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present disclosure.

Claims

1. An information presentation method, characterized in that, include: Obtain the current user information of the current user; wherein, the current user information is used to identify the first similar person; The target information is displayed in the display interface according to the target category; wherein, the target category is used to characterize the content display category of the display interface; the target information is obtained by processing the historical behavior data of the first similar person according to the target category.

2. The information presentation method according to claim 1, characterized in that, The step of displaying target information in the display interface according to target category includes: The display interface displays at least one of an interactive map and / or an information list and / or cards; The target information is presented on the interactive map, and / or in the information list, and / or in the card.

3. The information presentation method according to claim 1 or 2, characterized in that, The step of displaying target information in the display interface according to target category includes: The display interface displays target information that represents the category of the required object; The target information representing the category of demand object is obtained by aggregating the historical behavior data of multiple first similar people based on the demand object category contained in the historical behavior data of the first similar people.

4. The information presentation method according to claim 1 or 2, characterized in that, The step of displaying target information in the display interface according to target category includes: The display interface displays target information representing similar human categories; The target information representing similar human categories is obtained by organizing the demand objects contained in the historical behavior data according to the historical behavior data of the first similar person.

5. The information presentation method according to claim 1, characterized in that, The step of obtaining the current user's current user information includes: The display interface shows a user information collection control; In response to the current user's trigger operation on the user information collection control, the current user's current user information is obtained.

6. The information presentation method according to claim 1 or 5, characterized in that, Also includes: The display interface shows a user information collection control; In response to the current user's triggering operation on the user information collection control on the display interface, the first similar person is updated; The target information is updated and displayed in the display interface according to the target category; wherein, the target information is obtained by processing the historical behavior data of the first similar person after the update according to the target category.

7. The information presentation method according to claim 1, characterized in that, Also includes: In response to the current user's current user information update request, update the first similar person based on the updated current user information; The target information is updated and displayed in the display interface according to the target category; wherein, the target information is obtained by processing the historical behavior data of the first similar person after the update according to the target category.

8. The information presentation method according to claim 1 or 2, characterized in that, Also includes: In response to a triggering operation on the target information, the first similar person corresponding to the triggered target information is determined; Based on the first similar person, identify the second similar person; The target information is obtained by aggregating the demand object categories from the historical behavior data of the second similar person; The target information corresponding to the second similar person is displayed in the display interface according to the object category.

9. An electronic device, characterized in that, include: The memory stores execution instructions; as well as A processor that executes execution instructions stored in the memory, causing the processor to perform the method of any one of claims 1 to 8.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 8.

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