Information display method, apparatus, device, storage medium, and program product

Abstract

The application relates to an information display method, device, equipment, storage medium and program product. The method comprises the following steps: acquiring subscription information published by a plurality of objects subscribed by a user; respectively matching each subscription information with a preset hot event model to determine a target event type corresponding to each subscription information; the hot event model comprises different hot event types and event content corresponding to each hot event type; determining a same event type in each target event type; and controlling a user terminal to fold and display the subscription information corresponding to the same event type. The method can effectively avoid information display confusion.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to an information display method, apparatus, device, storage medium, and program product. Background Technology

[0002] Information flow refers to the flow of large amounts of information published by individuals, organizations, or other entities through various media and information platforms in the internet age. Whether it's social media, news websites, blogs, forums, or other online platforms, they all provide opportunities and channels for individuals or organizations to publish information. At the same time, individuals or organizations can obtain news or events of interest from the information flow through subscription or push services of information platforms.

[0003] In related technologies, the backend can first collect the relevant messages subscribed to by the user, and then push the relevant messages subscribed to by the user to the user terminal in the order of the message publication time. After that, the user terminal displays each subscription information to the user in the order of receipt time.

[0004] However, the way the above information is displayed is rather confusing. Summary of the Invention

[0005] Therefore, it is necessary to provide an information display method, apparatus, device, storage medium, and program product that can avoid information display confusion in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides an information display method, including:

[0007] Retrieves subscription information published by multiple objects that the user has subscribed to;

[0008] Each subscription message is matched with a preset hot event model to determine the target event type corresponding to each subscription message; the hot event model includes different hot event types and the event content corresponding to each hot event type;

[0009] Identify the common event types among the target event types;

[0010] Control the user terminal to collapse the display of subscription information corresponding to the same event type.

[0011] In one embodiment, the above-mentioned matching of each subscription information with a preset hot event model to determine the target event type corresponding to each subscription information includes:

[0012] Calculate the matching degree between each subscription information and each hot event type in the hot event model to obtain multiple matching degrees corresponding to each subscription information;

[0013] Based on the multiple matching degrees corresponding to each subscription information, the target event type corresponding to each subscription information is determined.

[0014] In one embodiment, determining the target event type corresponding to each subscription information based on multiple matching degrees corresponding to each subscription information includes:

[0015] Determine the maximum matching degree among multiple matching degrees corresponding to each subscription information;

[0016] The hot event type corresponding to the maximum match degree of each subscription message is determined as the target event type of the subscription message.

[0017] In one embodiment, determining the target event type corresponding to each subscription information based on multiple matching degrees corresponding to each subscription information includes:

[0018] Determine whether multiple matching degrees corresponding to the subscription information are all less than a preset threshold;

[0019] If multiple matching degrees are all less than the preset threshold, then the target event type corresponding to the subscription information is determined to be a non-hotspot event.

[0020] In one embodiment, the above-mentioned hotspot event model is constructed in the following ways:

[0021] Obtain prompts input by the user and construct an initial hotspot event model based on the prompts; the initial hotspot event model includes at least one model element;

[0022] Get multiple trending events published by the information platform;

[0023] A hotspot event model is constructed based on the initial hotspot event model and multiple hotspot events.

[0024] In one embodiment, the above-mentioned construction of the hotspot event model based on the initial hotspot event model and multiple hotspot events includes:

[0025] Analyze each trending event to obtain at least one keyword corresponding to each trending event;

[0026] Based on the element information corresponding to the keywords of each hot event, each hot event is filled into the initial hot event model according to the corresponding model elements, and the hot event model is determined.

[0027] In one embodiment, the process of filling each hotspot event into the initial hotspot event model according to the corresponding model elements to determine the hotspot event model includes:

[0028] Each hot event is populated into the initial hot event model according to the corresponding model elements to determine the intermediate hot event model;

[0029] Clustering is performed on the event content of each hot event in the intermediate hot event model to determine multiple hot event types;

[0030] A hot topic event model is obtained based on the types of hot topics and their corresponding content.

[0031] In one embodiment, the event content of each hot event in the intermediate hot event model is clustered to determine multiple hot event types, including:

[0032] For each hot event in the intermediate hot event model, the hot events are clustered according to the number of model elements and the event content included in the model elements to determine multiple hot event types;

[0033] The event content included in the model elements corresponding to different types of hot events varies.

[0034] Secondly, this application also provides an information display device, comprising:

[0035] The acquisition module is used to retrieve subscription information published by multiple objects that the user has subscribed to;

[0036] The matching module is used to match each subscription information with a preset hot event model to determine the target event type corresponding to each subscription information; the hot event model includes different hot event types and the event content corresponding to each hot event type;

[0037] The determination module is used to identify the same event type among various target event types;

[0038] The display module is used to control the user terminal to collapse the display of subscription information corresponding to the same event type.

[0039] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0040] Retrieves subscription information published by multiple objects that the user has subscribed to;

[0041] Each subscription message is matched with a preset hot event model to determine the target event type corresponding to each subscription message; the hot event model includes different hot event types and the event content corresponding to each hot event type;

[0042] Identify the common event types among the target event types;

[0043] Control the user terminal to collapse the display of subscription information corresponding to the same event type.

[0044] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0045] Retrieves subscription information published by multiple objects that the user has subscribed to;

[0046] Each subscription message is matched with a preset hot event model to determine the target event type corresponding to each subscription message; the hot event model includes different hot event types and the event content corresponding to each hot event type;

[0047] Identify the common event types among the target event types;

[0048] Control the user terminal to collapse the display of subscription information corresponding to the same event type.

[0049] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0050] Retrieves subscription information published by multiple objects that the user has subscribed to;

[0051] Each subscription message is matched with a preset hot event model to determine the target event type corresponding to each subscription message; the hot event model includes different hot event types and the event content corresponding to each hot event type;

[0052] Identify the common event types among the target event types;

[0053] Control the user terminal to collapse the display of subscription information corresponding to the same event type.

[0054] The aforementioned information display method, apparatus, device, storage medium, and program product acquire subscription information published by multiple objects subscribed to by the user, then match each subscription information with a preset hotspot event model to determine the target event type corresponding to each subscription information, then identify the common event types among the target event types, and control the user terminal to collapse and display the subscription information corresponding to the same event type. The hotspot event model includes different hotspot event types and the event content corresponding to each hotspot event type. In this method, because the subscription information can be classified by event type using the preset hotspot event model, each subscription information can be assigned to its own event type, thus avoiding the problem of multiple identical hotspot events repeatedly appearing in the user's subscription information, causing the subscription information to become mixed and chaotic. Meanwhile, subscription information corresponding to the same target event type is collapsed and displayed according to the same target event type. In this way, the subscription information obtained by users is displayed according to the same event type. Related subscription information of the same hot event is collapsed and displayed as a group of content, which allows users to focus their attention on the specific hot event, instead of having to repeatedly look at the subscription information of multiple hot events in a cluttered subscription information. Therefore, it can improve the user's reading experience and further avoid the problem of the user's subscription information being displayed in a mess. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is an internal structural diagram of a computer device in one embodiment;

[0057] Figure 2 This is a flowchart illustrating an information display method in one embodiment;

[0058] Figure 3 This is a flowchart illustrating the information display method in another embodiment;

[0059] Figure 4 This is a flowchart illustrating the information display method in another embodiment;

[0060] Figure 5 This is a flowchart illustrating the information display method in another embodiment;

[0061] Figure 6 This is a flowchart illustrating the information display method in another embodiment;

[0062] Figure 7 This is a structural block diagram of an information display device in one embodiment. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0064] In the internet age, any individual or organization can become an information source and publish messages, resulting in an explosive increase in the amount of information flowing online. Users typically obtain messages or events of interest through subscription or push services on information platforms. Hot topics refer to important and attention-grabbing events that are currently attracting widespread attention and discussion, and are usually the focus of public opinion. After a user subscribes to multiple information sources, all messages about all events published by these sources are sorted according to uniform rules in the user's information stream, such as chronological order, read status, or other algorithms. When all information sources publish massive amounts of information on multiple hot topics, a large amount of redundant and repetitive information about these topics appears in the user's information stream. The information in the multi-source information streams subscribed to by the user becomes mixed and chaotic, leading to a confusing display of information. Therefore, embodiments of this application provide an information display method, apparatus, device, storage medium, and program product to solve the above-mentioned technical problems.

[0065] The information display method provided in this application embodiment can be applied to computer devices, which can be terminals or servers. Taking a server as an example, its internal structure diagram can be as follows: Figure 1 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores information data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements an information display method.

[0066] Those skilled in the art will understand that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0067] In one exemplary embodiment, an information display method is provided. This embodiment relates to the specific process of displaying user subscription information according to the same event type, such as... Figure 2 As shown, this method is applied to Figure 1 Taking a computer device as an example, the method may include the following steps:

[0068] S202, retrieve subscription information published by multiple objects that the user has subscribed to.

[0069] The subscription information published by multiple entities subscribed to by the user can be information published by individual users or media organizations on an information publishing platform. This subscription information can be one or more events, one or more notifications, or other forms, which are not specifically limited in this embodiment. Furthermore, the subscription information can include text, letters, images, links, etc.

[0070] It should be noted that the subscription information published by multiple objects subscribed to by a user includes all information published by multiple objects, and the subscription information may include multiple messages published by different objects for the same event.

[0071] Alternatively, the computer device can obtain the aforementioned subscription information by storing the user's subscription information in a database, allowing the computer device to retrieve the user's subscribed objects and corresponding subscription information by querying the database. Alternatively, the computer device can store the user's subscription information in a cache for quick retrieval, or the subscription information can be transmitted to the computer device through an information queue system, from which the computer device receives the subscription information. Of course, other methods can also be used for the computer device to obtain subscription information, and this embodiment does not specifically limit these methods.

[0072] Specifically, after multiple objects subscribed to by a user publish their subscription information on an information publishing platform, the computer device can obtain the subscription information published by the multiple objects subscribed to by the user.

[0073] For example, subscription information could be related information published on an information publishing platform by bloggers, news media, and entertainment media that the user follows.

[0074] S204, match each subscription information with a preset hot event model to determine the target event type corresponding to each subscription information; the hot event model includes different hot event types and the event content corresponding to each hot event type.

[0075] The preset hot topic event model is a model related to hot topics, which refer to important or influential events that attract widespread attention and discussion within a specific time period. Furthermore, the hot topic event model includes different hot topic event types, such as hot topics related to rising oil prices or hot topics related to natural disasters. Simultaneously, the hot topic event model includes event content corresponding to each hot topic event type; for example, content related to rising oil prices in hot topics related to rising oil prices, and content related to natural disasters in hot topics related to natural disasters.

[0076] In addition, the matching method for matching each subscription information with the preset hot event model can be to match the keywords in the subscription information with the hot event model, or to match the topics in the hot event model, or to use natural language processing technology to calculate the semantic similarity between the subscription information and the hot event model for matching, or other matching methods can be used. This embodiment does not specifically limit these methods.

[0077] Furthermore, the target event type is the hot event type that matches the content of each subscription message with the hot event content in the hot event model. It should be noted that multiple subscription messages can be matched to the same hot event type. For example, if the subscription messages include multiple subscription messages about natural disasters, then each subscription message related to natural disasters can be matched to the hot event type related to natural disasters.

[0078] Specifically, after obtaining the subscription information published by multiple objects subscribed to by the user, each subscription information is matched with a preset hot event model. The hot event model includes different hot event types and the event content corresponding to each hot event type. After matching each subscription information with the event content corresponding to different hot events, the target hot event type corresponding to each subscription information can be obtained.

[0079] For example, subscription information can include information published by different bloggers and news media on the same or different events. For instance, different bloggers and news media might publish information about the same natural disaster event. Or, after publishing information about a natural disaster event, a blogger might also publish information about rising oil prices. Each subscription message is then matched with a trending event model. For example, matching a subscription message for a natural disaster event with a trending event model identifies that natural disaster event as a type of natural disaster event within the trending event model.

[0080] S206, Identify the same event type among the target event types.

[0081] Here, "target event type" refers to the target event type corresponding to each subscription message. In other words, each subscription message corresponds to a specific target event type, and multiple subscription messages can have the same or different target event types. Additionally, "same event type" refers to event types that share the same target event type.

[0082] Specifically, after obtaining the target event types corresponding to each subscription information, the same event types among the target event types can be integrated to obtain the same event types among the target event types.

[0083] For example, the target event type corresponding to each subscription information can be an oil price increase event type, a natural disaster event type, an environmental pollution event type, etc. Multiple subscription information may all correspond to the oil price increase event type, then the oil price increase type can be determined to be the same event type.

[0084] S208 controls the user terminal to collapse the display of subscription information corresponding to the same event type.

[0085] The user terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, etc. Furthermore, folded display refers to displaying one or more subscription messages as a group of content, grouping the subscription information according to certain criteria, and providing expand / collapse functionality, so that users can expand the groups of interest to view the subscription information within them.

[0086] It should be noted that when displaying subscription information, subscription information with the same target event type is displayed as a group of content, while subscription information with multiple events of the same type is displayed as multiple groups of content.

[0087] Furthermore, the control method for the computer device to control the user terminal to perform folding display can be by sending a request or instruction to the user terminal to control the folding display, or by sending control information to the user terminal through message push, enabling the user terminal to perform folding display operation according to the instruction. Alternatively, other control methods may be used, which are not specifically limited in this embodiment.

[0088] Specifically, after obtaining the same event type among the target event types, the computer device can control the user terminal to collapse and display the subscription information corresponding to the same event type.

[0089] For example, subscription information related to the type of oil price increase can be collapsed and displayed. When a user expands the content related to the type of oil price increase, they can view multiple subscription messages related to the type of oil price increase.

[0090] In the aforementioned information display method, subscription information published by multiple objects subscribed to by the user is obtained. Each subscription information is then matched with a preset hot topic event model to determine the target event type corresponding to each subscription information. Next, common event types within each target event type are identified, and the user terminal is controlled to collapse and display subscription information corresponding to the same event type. The hot topic event model includes different hot topic event types and the event content corresponding to each hot topic event type. This method, by classifying subscription information by event type using the preset hot topic event model, allows each subscription information to be assigned to its own event type, thus avoiding the problem of multiple identical hot topic events repeatedly appearing in the user's subscription information, causing a cluttered and chaotic subscription information. Simultaneously, by collapsing and displaying subscription information corresponding to the same target event type, the user's subscription information is displayed according to the same event type. Related subscription information for the same hot topic event is displayed as a group of content, allowing the user's attention to focus on the specific hot topic event, rather than repeatedly viewing multiple hot topic event subscriptions in a cluttered environment. This improves the user's reading experience and further avoids the problem of cluttered subscription information display.

[0091] The above embodiments mention that each subscription information can be matched with a preset hot event model to obtain the target event type corresponding to each subscription information. Based on this, the following embodiments will explain the specific process of how to match each subscription information to obtain the target event type.

[0092] In another exemplary embodiment, another information display method is provided, based on the above embodiments, such as... Figure 3 As shown, S202 may include the following steps:

[0093] S302, calculate the matching degree between each subscription information and each hot event type in the hot event model, and obtain multiple matching degrees corresponding to each subscription information.

[0094] The aforementioned hot event model includes different hot event types and the event content corresponding to each hot event type. Accordingly, when calculating the matching degree between each subscription information and each hot event type in the hot event model, the matching degree between each subscription information and the event content corresponding to each hot event type in the hot event model is calculated.

[0095] It should be noted that each individual subscription message will have its matching degree calculated against each hot event type in the hot event model, resulting in multiple matching degrees for a single subscription message. Furthermore, the matching degree between each subscription message and each hot event type within the hot event model will also be calculated, resulting in multiple matching degrees for each subscription message.

[0096] Furthermore, the matching degree of each subscription information is calculated with the event content corresponding to each hot event type in the hot event model. Optionally, the matching degree can be calculated using the following steps A1-A3:

[0097] Step A1: Represent the subscription information and the hot event content corresponding to the hot event type as numerical vectors.

[0098] Step A2: Calculate the similarity between the numerical vectors of the subscribed message and the content of the hot event corresponding to each hot event type. This can be done by calculating the cosine similarity, Jaccard similarity, edit distance, etc. between the numerical vectors.

[0099] Step A3: The calculated similarity value is regarded as the matching degree between the subscribed information and a certain hot event type.

[0100] In addition to the above-mentioned calculation method of determining the matching degree between each subscription message and the event content corresponding to each hot event type in the hot event model, the matching degree can also be characterized by the number of times one or more feature keywords in the event content corresponding to each hot event type in the hot event model appear in each subscription message. For example, if the event content corresponding to a certain hot event type in the hot event model includes 5 feature keywords, and all 5 feature keywords appear in a certain subscription message, then the matching degree between the subscription message and the event content corresponding to that hot event type can be considered 100%, meaning the subscription message and the hot event type are a perfect match. On the other hand, if only 3 of the 5 feature keywords appear in a certain subscription message, then the matching degree between the subscription message and the event content corresponding to that hot event type can be considered 60%, meaning the matching degree between the subscription message and the hot event type is 60%.

[0101] Specifically, the matching degree between each subscription information and the corresponding hot event content of each hot event type in the hot event model is calculated to characterize the matching degree between each subscription information and each hot event type in the hot event model, thereby obtaining multiple matching degrees corresponding to each subscription information.

[0102] For example, suppose that the subscription information for a single natural disaster has a 100% match with the natural disaster event type in the hot event model, a 10% match with the oil price increase event type in the hot event model, and a 30% match with the environmental pollution event type in the hot event model. In this way, the subscription information for a single natural disaster will have multiple match degrees.

[0103] S304, determine the target event type corresponding to each subscription information based on the multiple matching degrees corresponding to each subscription information.

[0104] The target event type can be any event type among the various hot event types in the hot event model.

[0105] Furthermore, for each of the above subscription information's multiple matching degrees, as an optional embodiment, the maximum matching degree among the multiple matching degrees corresponding to each subscription information can be determined, and then the hot event type corresponding to the maximum matching degree of each subscription information can be determined as the target event type of the subscription information.

[0106] Meanwhile, among the multiple matching degrees corresponding to each subscription information mentioned above, there is generally a maximum matching degree. However, when the multiple matching degrees of a certain subscription information with each hot event type are low, for example, multiple matching degrees are all below 5%, it can be determined that the subscription information does not match any of the hot event types in the hot event model. If the hot event type corresponding to the maximum matching degree of each subscription information is determined as the target event type of the subscription information, then when the maximum matching degree is low, the target event type of the subscription information will be determined as an unmatched hot event type, resulting in inaccurate matching of the target event type of the subscription information.

[0107] Furthermore, to avoid using the hot event type corresponding to the maximum matching degree as the target event type of the subscription information even when the maximum matching degree is low, as an optional embodiment, it can be determined whether multiple matching degrees corresponding to the subscription information are all less than a preset threshold; if multiple matching degrees are all less than the preset threshold, then the target event type corresponding to the subscription information is determined to be a non-hot event. Here, non-hot events do not belong to the hot event types in the hot event model and can be used to classify subscription information into hot event types and non-hot event types.

[0108] Specifically, after obtaining multiple matching scores for each subscription information, the maximum matching score among these scores is determined, and the hot event type corresponding to the maximum matching score for each subscription information is then identified as the target event type for that subscription information. Additionally, it is necessary to determine whether all matching scores for each subscription information are less than a preset threshold. If all matching scores are less than the preset threshold, the target event type for the subscription information is determined to be a non-hot event.

[0109] For example, if the subscription information for a single natural disaster has a 100% match rate with the natural disaster event type in the hotspot event model, a 10% match rate with the oil price increase event type in the hotspot event model, and a 30% match rate with the environmental pollution event type in the hotspot event model, then the maximum match rate for the subscription information for that natural disaster is 100%, and the target event type for the subscription information for that natural disaster is the natural disaster event type.

[0110] For example, if the matching degree of a single financial product subscription information with the natural disaster event type in the hot event model is 5%, the matching degree with the oil price increase event type in the hot event model is 3%, and the matching degree with the environmental pollution event type in the hot event model is 1%, and assuming the preset matching degree threshold is 10%, it can be determined that the multiple matching degrees corresponding to the single financial product subscription information are all less than the preset matching degree threshold. Then, the target event type of the financial product subscription information is a non-hot event type.

[0111] In this embodiment, the matching degree between each subscription information and each hot event type in the hot event model is calculated. Then, the target event type of each subscription information is determined based on multiple matching degrees, making the target event type determined by the matching degree more accurate. Furthermore, by selecting the maximum matching degree from multiple matching degrees of each subscription information to determine the target event type, the subscription information can be categorized, allowing each subscription information to be associated with a specific hot event type. Simultaneously, allowing the same subscription information to be compared with multiple hot event types to find the most matching event type improves the accuracy and reliability of the target event type for each subscription information. Further, by analyzing the relationship between multiple matching degrees of each subscription information and a preset threshold, subscription information with matching degrees all less than the threshold is identified as non-hot event. This avoids incorrectly classifying subscription information with low matching degrees to hot event types as hot event types, and also prevents non-hot event information with weak correlation to hot events from appearing in the subscription information obtained by users, enabling users to obtain more accurate subscription information and thus improving the user's reading experience.

[0112] The above embodiments mention that each subscription information can be matched with a preset hot event model to determine the target event type of each subscription information. The hot event model includes different hot event types and the event content corresponding to each hot event type. The following embodiments will describe the specific construction method of the hot event model.

[0113] In another embodiment, another information display method is provided, based on the above embodiments, such as... Figure 4 As shown, optionally, the construction of the above-mentioned hotspot event model may include the following steps:

[0114] S402, Obtain the prompt words input by the user, and construct an initial hotspot event model based on the prompt words; the initial hotspot event model includes at least one model element.

[0115] In this context, the user-inputted prompts refer to the prompts entered by the user on the interactive interface of the information publishing platform. The platform's interface may provide users with a selection of preset prompts, which can be one or more words, short text, or other forms; this embodiment does not impose specific limitations on this. Furthermore, the user-inputted prompts should be content related to the user's subscribed information that the user is interested in or needs to access.

[0116] It should be noted that the prompt words can be time, place, subject, predicate, object, etc. Specifically, it can be a single word or multiple words, or it can be a sentence composed of multiple words.

[0117] In addition, the initial hot topic event model can be a table frame that includes prompts entered by the user. The initial hot topic event model can be stored in a database, which makes it easy to search, modify and other operations on the initial hot topic event model.

[0118] Furthermore, the prompts input by the user are filled into the initial hot event model. The corresponding prompts in the initial event model then constitute the model elements in the initial hot event model. The initial hot event model includes at least one model element, which can be one or more of the prompts input by the user.

[0119] In this step, the initial hotspot event model is constructed based on the prompt words. This can be done by creating a table in a database where the columns are the prompt words entered by the user, and the rows are temporarily empty. Alternatively, the rows can be the prompt words entered by the user, and the columns are temporarily empty. This embodiment does not specify which method is correct.

[0120] Specifically, after a user enters a prompt word on the information publishing platform, the computer device can obtain the prompt word entered by the user and then build an initial hot topic event model based on the prompt word.

[0121] S404 retrieves multiple trending events published by the information platform.

[0122] The information platform can publish multiple trending events, which can be either historical or current. For example, they can be trending events from historical or current trending lists. Users can choose whether a trending event is historical or current. If no choice is made, the current trending event will be used by default.

[0123] It should be noted that the multiple trending events were obtained in order of their popularity. The events with the highest popularity were obtained first, followed by those with relatively high popularity, and then other trending events were obtained.

[0124] Specifically, depending on the user's selection, the computer device can access multiple trending events published by the information platform.

[0125] For example, computer devices can obtain relevant events from the platform's trending search list and topic list as hot topics.

[0126] S406, Construct a hotspot event model based on the initial hotspot event model and multiple hotspot events.

[0127] In this step, the initial hot event model includes at least one model element. The content of the element corresponding to the model element in the initial hot event model is all empty values. Here, the relevant content in multiple hot events can be mapped to the model element in the initial hot event model to construct the hot event model.

[0128] Specifically, after the computer device obtains multiple hot events published by the information platform, it matches the obtained hot events with the model elements in the initial hot event model constructed above, so as to construct a hot event model related to the multiple hot events.

[0129] For example, relevant events in the trending search lists and topic lists of information platforms can be mapped to time, location, subject, predicate, and object in the model elements to construct a hot event model.

[0130] In this embodiment, an initial trending event model is constructed based on user-input prompts, and multiple trending events published by the information platform are acquired. Then, a new trending event model is constructed based on the initial model and these multiple events. The initial trending event model includes at least one model element. Because the initial model is constructed using user-input prompts, it ensures that the model elements included in the initial model are content that the user is interested in or follows. Furthermore, by acquiring multiple trending events published by the information platform and constructing the new model based on the initial model and these multiple events, the constructed trending event model includes model elements associated with the user-input prompts. This allows the trending event model to be accurately constructed based on each user's preferences and interests, providing more personalized recommendations that better match user interests, thus improving user experience and satisfaction.

[0131] The above embodiments mentioned that a hot event model can be constructed based on an initial hot event model and multiple hot events. The following embodiments will explain the specific process of how to construct a hot event model.

[0132] In another embodiment, another information display method is provided, based on the above embodiments, such as... Figure 5 As shown, the above S406 may include the following steps:

[0133] S502: Analyze each hot topic event to obtain at least one keyword corresponding to each hot topic event.

[0134] The analysis of the aforementioned hot topics can involve extracting text from each topic, such as extracting the titles or content of each topic. The extracted text can be long paragraphs or short paragraphs.

[0135] In addition, after extracting the text from the hot topics, the extracted text information can be stored in the database to generate multiple text records related to the hot topics.

[0136] Furthermore, after extracting the text of trending events as described above, the text can be further segmented into phrases to obtain at least one keyword corresponding to each trending event. Additionally, the segmentation of trending event text can be performed using a natural language database, such as a Chinese dictionary or word database, or through clustering algorithms, or deep learning algorithms. Of course, other segmentation methods can also be used, and this embodiment does not specifically limit them.

[0137] It should be noted that when breaking down the text of each trending event, you can distinguish subjects, predicates, objects, time, location, etc., according to Chinese grammar. In other words, you can break it down according to model elements, so that the obtained trending event keywords can correspond to the model elements. At the same time, when there are parallel words in the text of each trending event, the parallel words can be broken down as a whole into a single keyword during the breakdown.

[0138] In addition, the number of keywords corresponding to each hot event can be the same or different. For example, hot event one corresponds to 3 keywords, hot event two corresponds to 5 keywords, and hot event three corresponds to 3 keywords.

[0139] Specifically, after obtaining multiple trending events published on the aforementioned information platform, each trending event is analyzed. For example, text extraction can be performed on each trending event to obtain the text of the trending event, and then the text of the trending event can be broken down into phrases to obtain at least one keyword corresponding to each trending event.

[0140] For example, when analyzing trending events on an information platform, you can extract the titles of the platform's trending search list or the content of the platform's topic list. Then, you can break down the extracted text into keywords.

[0141] S504. Based on the element information corresponding to the keywords of each hot event, fill each hot event into the initial hot event model according to the corresponding model elements to determine the hot event model.

[0142] The element information refers to the model element information in the initial hot topic event model. Furthermore, there is a correspondence between the keywords of each hot topic event and the model elements; for example, if the model element is time, then the keyword could be "Year XX Month".

[0143] In this step, the keywords corresponding to each of the above-mentioned hot events correspond to the model elements in the initial hot event model. Since the element content in the initial hot event model is empty, the keywords in each hot event are filled into the corresponding element content according to the model elements. In this way, hot event content containing the keywords of each hot event can be obtained.

[0144] It should be noted that the number of keywords for each trending event can be less than or equal to the number of model elements. Furthermore, since keyword splitting is based on model elements, the number of keywords will not exceed the number of model elements. When the number of keywords for a trending event is less than the number of model elements, the content of model elements without corresponding keywords will remain empty.

[0145] Specifically, after obtaining the keywords of each hot event, the hot events are filled into the initial hot event model according to the model element information corresponding to the keywords of each hot event, thus obtaining the hot event model.

[0146] For example, suppose the model elements in the initial hot event model include time, location, subject, predicate, and object. The keywords for hot event one are year, mountainous area, occurrence, and earthquake. The keywords for hot event two are year, oil price, and increase. The keywords for hot event one and hot event two are filled into the initial hot event model according to the model elements to obtain a hot event model that includes hot event one and hot event two.

[0147] In this embodiment, by parsing each hot event, at least one keyword corresponding to each hot event is obtained. Then, according to the element information corresponding to the keywords of each hot event, the hot events are filled into the initial hot event model according to the model elements, thus obtaining the hot event model. Here, by parsing each hot event to obtain at least one keyword corresponding to each hot event, the keywords are used to represent the key information of the hot event. Then, the keywords of each hot event are filled into the initial hot event model according to the model elements. The hot event model obtained in this way only includes the keywords of multiple hot events and model elements. The coarse information of the corresponding hot event can be obtained through the keywords, without the need to integrate all hot events together, which greatly saves the storage space of the hot event model. At the same time, after obtaining the hot event model including the hot event keywords, subscription information can be matched with the keywords in the hot event model, without the need to match the subscription information with the entire hot event, which can significantly improve the matching speed.

[0148] The above embodiments mentioned filling each hot event into the initial hot event model according to the corresponding model elements to determine the hot event model. The following embodiments will explain the specific process of how to determine the hot event model.

[0149] In another embodiment, another information display method is provided, based on the above embodiments, such as... Figure 6 As shown, the above S504 may include the following steps:

[0150] S602, fill each hot event into the initial hot event model according to the corresponding model elements, and determine the intermediate hot event model.

[0151] When analyzing and filling keywords for the aforementioned hot events, the analysis and filling can be carried out in the order of the hot events' popularity. The keywords of each hot event are then filled into the initial hot event model in turn. The hot event model obtained here, which fills keywords in the order of event popularity, can be used as an intermediate hot event model.

[0152] Specifically, each hot event is populated into the initial hot event model according to the corresponding model elements to obtain the intermediate hot event model.

[0153] For example, assuming multiple trending events are obtained based on popularity, with trending event one having the highest popularity and trending event two having the second highest popularity, then when filling in keywords, trending event one is parsed first and its keywords are filled into the initial trending event model first, and then trending event two is parsed and its keywords are filled into the initial trending event model.

[0154] S604 performs clustering processing on the event content of each hot event in the intermediate hot event model to determine multiple hot event types.

[0155] In this model, the event content of each hot event is the keyword of the aforementioned hot events, which can also be considered as the event content included in the model elements. Furthermore, the clustering method for processing the event content of each hot event in the intermediate hot event model can be K-means clustering, hierarchical clustering, or a custom clustering criterion. Of course, other clustering methods can also be used, but this embodiment does not specifically limit them.

[0156] In this step, when clustering the event content of each hot event in the intermediate hot event model, clustering can be performed according to the number of model elements and the content of those elements. As an optional embodiment, each hot event in the intermediate hot event model can be clustered according to the number of model elements and the event content included in those elements, thus determining multiple hot event types. The event content included in the model elements corresponding to different hot event types will vary.

[0157] Furthermore, when clustering hot events according to the number of model elements and the event content included in the model elements, if the number of keywords and the keyword content in the model elements of the intermediate hot event model are consistent, the corresponding hot events in these intermediate hot event models are clustered into one category; or, if the keyword content in the model elements of the intermediate hot event model is mostly consistent, the corresponding hot events in these intermediate hot event models are clustered into another category; or, if the keyword content in the model elements of multiple intermediate hot event models is different, the hot events corresponding to each keyword are clustered into a separate category. Each of the above categories corresponds to a hot event type, thus determining multiple hot event types.

[0158] It should be noted that the hot topic event type can be described as a combination phrase of keywords that appear repeatedly in one or more hot topic event keywords corresponding to each category.

[0159] In addition, in the intermediate hot event model, the content of each hot event needs to be assigned to one of the categories. Event content that has already been assigned to another category cannot be assigned to a second category.

[0160] Specifically, after obtaining the intermediate hot event model, it is necessary to cluster the event content of each hot event in the intermediate hot event model. As an optional embodiment, for each hot event in the intermediate hot event model, the hot events can be clustered according to the number of model elements and the degree of similarity of the event content included in the model elements to determine multiple hot event types.

[0161] For example, in the intermediate hotspot event model, the keywords for hotspot event one are: XX year, mountainous area, occurrence, earthquake; the keywords for hotspot event two are: XX year, oil price, increase; the keywords for hotspot event three are: XX year, mountainous area, occurrence, earthquake; the keywords for hotspot event four are: XX year, oil price, decrease; and the keywords for hotspot event five are: XX year, financial crisis. Therefore, during clustering, hotspot events one and three can be clustered together, and the corresponding hotspot event type for this cluster is "an earthquake occurred in a mountainous area in XX year"; hotspot events two and four can be clustered together, and the corresponding hotspot event type for this cluster is "oil price in XX year"; and hotspot event five can be clustered separately, and the corresponding hotspot event type for this cluster is "financial crisis in XX year".

[0162] S606, obtains a hot event model based on each hot event type and its corresponding content.

[0163] In this step, the intermediate hot event model includes model elements and keywords corresponding to each hot event. After obtaining each hot event type, descriptive content of each hot event type can be added to the intermediate hot event model. Thus, the obtained hot event model includes not only model elements and keywords of each hot event, but also the event type to which each hot event belongs.

[0164] Furthermore, the keywords of each hot event in the hot event model can be sorted according to the event type to which each hot event belongs, and the keywords of the same type of hot events can be arranged together.

[0165] Specifically, after obtaining multiple hot event types as described above, each hot event type is matched with its corresponding event content, i.e., the keywords of each hot event, to obtain a hot event model.

[0166] For example, in the intermediate hot event model, the hot event type of hot event one is "an earthquake occurred in the mountainous area in XX year", the hot event type of hot event two is "oil price in XX year", the hot event type of hot event three is "an earthquake occurred in the mountainous area in XX year", and the hot event behavior category of hot event four is "oil price in XX year". Then, the row / column containing the keywords of hot event one and hot event three can be arranged together, and the row / column containing the keywords of hot event two and hot event four can be arranged together.

[0167] In this embodiment, an intermediate hot event model is determined by filling each hot event into the initial hot event model according to the corresponding model elements. Then, the event content of each hot event in the intermediate hot event model is clustered to obtain multiple hot event types. A hot event model is then obtained based on each hot event type and its corresponding event content. Here, the event content corresponding to each hot event can be integrated according to the hot event type, grouping similar or identical hot event content into one category, thus avoiding a chaotic arrangement of hot events in the hot event model. Simultaneously, in the hot event model obtained based on each hot event type and its corresponding event content, each event content corresponds to a hot event type. This allows for quick identification of the corresponding hot event type based on the event content, and also facilitates rapid matching of the corresponding hot event type when matching subscription information subsequently. Furthermore, for each hot event in the intermediate hot event model, clustering can be performed according to the number of model elements and the event content included in those elements. Since events with similar numbers of model elements and event content can be grouped into the same category, the clustering results can reveal the similarity and differences between different hot events, as well as the degree of correlation between events.

[0168] The following example, using four subscription messages subscribed to by a user, provides a detailed embodiment to illustrate the technical solution of this application. Based on the above embodiment, the method may include the following steps:

[0169] S1. Obtain the prompt words entered by the user on the user's terminal device, and construct an initial hot topic event model based on the prompt words; the initial hot topic event model includes at least one model element, such as time, location, subject, object, predicate, etc.

[0170] S2 retrieves multiple trending events published by the information platform, such as "Event 1: A Y-magnitude earthquake occurred in the mountains...", "Event 2: Nuclear wastewater was discharged...", "Event 3: Gasoline prices rose again...", "Event 4: Gasoline prices decreased...";

[0171] S3 analyzes each hot topic and obtains at least one keyword corresponding to each hot topic. The keyword corresponds to the above model elements, such as "mountainous area, occurrence, earthquake", "emission, nuclear wastewater", "gasoline price, increase", "gasoline price, decrease".

[0172] S4. According to the element information corresponding to the keywords of each hot event, fill each hot event into the initial hot event model according to the model elements to determine the intermediate hot event model. For example, under the subject model element, fill in "mountain area", "Null", "gasoline price" and "gasoline price" in sequence.

[0173] S5. For each hot event in the intermediate hot event model, cluster the hot events according to the number of model elements and the event content included in the model elements to determine multiple hot event types. For example, cluster the above events 3 and 4 into one category, with the hot event type being "gasoline price", and cluster the above event 1 into a separate category, with the hot event type being "earthquake in the mountainous area".

[0174] S6. Obtain a hot event model based on each hot event type and its corresponding event content. The hot event model includes model elements, keywords corresponding to each hot event, and hot event type of each hot event.

[0175] S7 retrieves subscription information published by multiple objects that the user has subscribed to, such as "Subscription Information 1: An earthquake has occurred in the mountains...", "Subscription Information 2: Nuclear wastewater has been discharged into the ocean...", "Subscription Information 3: Multiple earthquakes have occurred...", "Subscription Information 4: A serious accident has occurred on a highway...";

[0176] S8. Calculate the matching degree between each subscription information and each hot event type in the hot event model to obtain multiple matching degrees for each subscription information. Here, the matching degree can be determined by the proportion of the number of hot event keywords appearing in the subscription information to the total number of hot event keywords. For example, the matching degree of subscription information 1 with hot event 1 keyword is 100%, the matching degree with hot event 2 keyword is 0%, the matching degree with hot event 3 keyword is 0%, and the matching degree with hot event 4 keyword is 0%. For another example, the matching degree of subscription information 2 with hot event 1 keyword is 0%, the matching degree with hot event 2 keyword is 100%, the matching degree with hot event 3 keyword is 0%, and the matching degree with hot event 4 keyword is 0%.

[0177] S9, determine the maximum matching degree among multiple matching degrees corresponding to each subscription information. For example, the maximum matching degree among multiple matching degrees of subscription information 1 is 100%, the maximum matching degree among multiple matching degrees of subscription information 2 is 100%, the maximum matching degree of subscription information 3 is 66%, and the maximum matching degree of subscription information 4 is 0%.

[0178] S10, the hot event type corresponding to the maximum matching degree of each subscription information is determined as the target event type of the subscription information. The hot event type corresponding to the maximum matching degree of subscription information 1 is "earthquake occurred in the mountainous area", the hot event type corresponding to the maximum matching degree of subscription information 2 is "nuclear wastewater discharge", and the hot event type corresponding to the maximum matching degree of subscription information 3 is "earthquake occurred in the mountainous area".

[0179] S11, determine whether the multiple matching degrees corresponding to the subscription information are all less than the preset threshold. Assuming the threshold here is 10%, then the multiple matching degrees corresponding to the above subscription information 4 are all less than the preset threshold, and the multiple matching degrees of other subscription information are all greater than the preset threshold.

[0180] S12, if multiple matching degrees are all less than the preset threshold, then the target event type corresponding to the subscription information is determined to be a non-hotspot event, and the above subscription information 4 is determined to be a non-hotspot event;

[0181] S13, determine the same event type among the target event types, and the above subscription information 1 and subscription information 3 have the same event type;

[0182] S14, control the user terminal to collapse the subscription information corresponding to the same event type, collapse subscription information 1 and subscription information 3 together for display, display subscription information 2 separately, and display subscription information 4 as a non-hot event.

[0183] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0184] Based on the same inventive concept, this application also provides an information display device for implementing the information display method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more information display device embodiments provided below can be found in the limitations of the information display method described above, and will not be repeated here.

[0185] In one exemplary embodiment, such as Figure 7 As shown, an information display device is provided, comprising: an acquisition module, a matching module, a determination module, and a display module, wherein:

[0186] The acquisition module is used to retrieve subscription information published by multiple objects that the user has subscribed to;

[0187] The matching module is used to match each subscription information with a preset hot event model to determine the target event type corresponding to each subscription information; the hot event model includes different hot event types and the event content corresponding to each hot event type;

[0188] The determination module is used to identify the same event type among various target event types;

[0189] The display module is used to control the user terminal to collapse the display of subscription information corresponding to the same event type.

[0190] In another embodiment, another information display device is provided. Based on the above embodiments, the matching module includes a matching degree calculation unit and a target event type determination unit, wherein:

[0191] The matching degree calculation unit is used to calculate the matching degree between each subscription information and each hot event type in the hot event model, and obtain multiple matching degrees corresponding to each subscription information;

[0192] The target event type determination unit is used to determine the target event type corresponding to each subscription information based on multiple matching degrees corresponding to each subscription information.

[0193] Optionally, the target event type determination unit mentioned above may include:

[0194] The maximum matching degree determination subunit is used to determine the maximum matching degree among multiple matching degrees corresponding to each subscription information;

[0195] The target event type determination subunit is used to determine the hot event type corresponding to the maximum matching degree of each subscription information as the target event type of the subscription information.

[0196] Optionally, the target event type determination unit may further include:

[0197] The judgment sub-unit is used to determine whether multiple matching degrees corresponding to the subscription information are all less than a preset threshold.

[0198] The non-hotspot event determination subunit is used to determine the target event type corresponding to the subscription information as a non-hotspot event if multiple matching degrees are all less than a preset threshold.

[0199] In another embodiment, another information display device is provided. Based on the above embodiments, the construction method of the above-mentioned hot event model includes: a prompt word acquisition module, a hot event acquisition module, and a construction module, wherein:

[0200] The prompt word acquisition module is used to acquire prompt words input by the user and construct an initial hot event model based on the prompt words; the initial hot event model includes at least one model element;

[0201] The "Get Hot Events" module is used to retrieve multiple hot events published by the information platform.

[0202] The building module is used to construct a hotspot event model based on the initial hotspot event model and multiple hotspot events.

[0203] In another embodiment, another information display device is provided. Based on the above embodiments, the above-described building module includes a parsing unit and a filling unit, wherein:

[0204] The parsing unit is used to analyze each hot topic event and obtain at least one keyword corresponding to each hot topic event;

[0205] The fill unit is used to fill the initial hot event model with the element information corresponding to the keywords of each hot event, and to determine the hot event model.

[0206] Optionally, the above-mentioned filling unit may include:

[0207] The fill sub-unit is used to fill each hot event into the initial hot event model according to the corresponding model elements, and to determine the intermediate hot event model;

[0208] The clustering subunit is used to cluster the event content of each hot event in the intermediate hot event model to determine multiple hot event types.

[0209] The acquisition sub-unit is used to obtain the hot event model based on each hot event type and its corresponding event content.

[0210] Optionally, the above clustering subunit is specifically used to cluster each hot event in the intermediate hot event model according to the number of model elements and the event content included in the model elements, and to determine multiple hot event types; wherein the event content included in the model elements corresponding to different hot event types is different.

[0211] Each module in the aforementioned information display device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0212] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0213] The system retrieves subscription information published by multiple objects subscribed to by the user; matches each subscription information with a preset hot event model to determine the target event type corresponding to each subscription information; the hot event model includes different hot event types and the event content corresponding to each hot event type; identifies the same event type among the target event types; and controls the user terminal to collapse and display the subscription information corresponding to the same event type.

[0214] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0215] Calculate the matching degree between each subscription information and each hot event type in the hot event model to obtain multiple matching degrees corresponding to each subscription information; determine the target event type corresponding to each subscription information based on the multiple matching degrees corresponding to each subscription information.

[0216] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0217] Determine the maximum matching degree among multiple matching degrees for each subscription information; determine the hot event type corresponding to the maximum matching degree of each subscription information as the target event type of the subscription information.

[0218] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0219] Determine if multiple matching degrees corresponding to the subscription information are all less than a preset threshold; if multiple matching degrees are all less than the preset threshold, then determine that the target event type corresponding to the subscription information is a non-hotspot event.

[0220] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0221] Obtain prompts input by the user and construct an initial hot topic event model based on the prompts; the initial hot topic event model includes at least one model element; obtain multiple hot topics published by the information platform; construct a hot topic event model based on the initial hot topic event model and the multiple hot topics events.

[0222] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0223] Each hot topic event is analyzed to obtain at least one keyword corresponding to each hot topic event; according to the element information corresponding to the keywords of each hot topic event, each hot topic event is filled into the initial hot topic event model according to the model element correspondence, and the hot topic event model is determined.

[0224] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0225] Each hot event is populated into the initial hot event model according to the corresponding model elements to determine the intermediate hot event model; the event content of each hot event in the intermediate hot event model is clustered to determine multiple hot event types; and the hot event model is obtained based on each hot event type and its corresponding event content.

[0226] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0227] For each hot event in the intermediate hot event model, the hot events are clustered according to the number of model elements and the event content included in the model elements to determine multiple hot event types; among them, the event content included in the model elements corresponding to different hot event types is different.

[0228] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0229] The system retrieves subscription information published by multiple objects subscribed to by the user; matches each subscription information with a preset hot event model to determine the target event type corresponding to each subscription information; the hot event model includes different hot event types and the event content corresponding to each hot event type; identifies the same event type among the target event types; and controls the user terminal to collapse and display the subscription information corresponding to the same event type.

[0230] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0231] Calculate the matching degree between each subscription information and each hot event type in the hot event model to obtain multiple matching degrees corresponding to each subscription information; determine the target event type corresponding to each subscription information based on the multiple matching degrees corresponding to each subscription information.

[0232] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0233] Determine the maximum matching degree among multiple matching degrees for each subscription information; determine the hot event type corresponding to the maximum matching degree of each subscription information as the target event type of the subscription information.

[0234] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0235] Determine if multiple matching degrees corresponding to the subscription information are all less than a preset threshold; if multiple matching degrees are all less than the preset threshold, then determine that the target event type corresponding to the subscription information is a non-hotspot event.

[0236] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0237] Obtain prompts input by the user and construct an initial hot topic event model based on the prompts; the initial hot topic event model includes at least one model element; obtain multiple hot topics published by the information platform; construct a hot topic event model based on the initial hot topic event model and the multiple hot topics events.

[0238] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0239] Each hot topic event is analyzed to obtain at least one keyword corresponding to each hot topic event; according to the element information corresponding to the keywords of each hot topic event, each hot topic event is filled into the initial hot topic event model according to the model element correspondence, and the hot topic event model is determined.

[0240] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0241] Each hot event is populated into the initial hot event model according to the corresponding model elements to determine the intermediate hot event model; the event content of each hot event in the intermediate hot event model is clustered to determine multiple hot event types; and the hot event model is obtained based on each hot event type and its corresponding event content.

[0242] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0243] For each hot event in the intermediate hot event model, the hot events are clustered according to the number of model elements and the event content included in the model elements to determine multiple hot event types; among them, the event content included in the model elements corresponding to different hot event types is different.

[0244] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0245] The system retrieves subscription information published by multiple objects subscribed to by the user; matches each subscription information with a preset hot event model to determine the target event type corresponding to each subscription information; the hot event model includes different hot event types and the event content corresponding to each hot event type; identifies the same event type among the target event types; and controls the user terminal to collapse and display the subscription information corresponding to the same event type.

[0246] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0247] Calculate the matching degree between each subscription information and each hot event type in the hot event model to obtain multiple matching degrees corresponding to each subscription information; determine the target event type corresponding to each subscription information based on the multiple matching degrees corresponding to each subscription information.

[0248] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0249] Determine the maximum matching degree among multiple matching degrees for each subscription information; determine the hot event type corresponding to the maximum matching degree of each subscription information as the target event type of the subscription information.

[0250] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0251] Determine if multiple matching degrees corresponding to the subscription information are all less than a preset threshold; if multiple matching degrees are all less than the preset threshold, then determine that the target event type corresponding to the subscription information is a non-hotspot event.

[0252] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0253] Obtain prompts input by the user and construct an initial hot topic event model based on the prompts; the initial hot topic event model includes at least one model element; obtain multiple hot topics published by the information platform; construct a hot topic event model based on the initial hot topic event model and the multiple hot topics events.

[0254] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0255] Each hot topic event is analyzed to obtain at least one keyword corresponding to each hot topic event; according to the element information corresponding to the keywords of each hot topic event, each hot topic event is filled into the initial hot topic event model according to the model element correspondence, and the hot topic event model is determined.

[0256] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0257] Each hot event is populated into the initial hot event model according to the corresponding model elements to determine the intermediate hot event model; the event content of each hot event in the intermediate hot event model is clustered to determine multiple hot event types; and the hot event model is obtained based on each hot event type and its corresponding event content.

[0258] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0259] For each hot event in the intermediate hot event model, the hot events are clustered according to the number of model elements and the event content included in the model elements to determine multiple hot event types; among them, the event content included in the model elements corresponding to different hot event types is different.

[0260] It should be noted that the data involved in this application (including but not limited to data used for analysis, data stored, data displayed, etc.) are all data that have been fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0261] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0262] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0263] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An information display method, characterized in that, The method includes: Retrieves subscription information published by multiple objects that the user has subscribed to; Each subscription information is matched with a preset hot event model to determine the target event type corresponding to each subscription information; the hot event model includes different hot event types and the event content corresponding to each hot event type; Identify the common event types among the described target event types; Control the user terminal to collapse and display the subscription information corresponding to the same event type; The construction methods of the hotspot event model include: Obtain the prompt words input by the user, and construct an initial hot topic event model based on the prompt words; the initial hot topic event model includes at least one model element; Based on the element information corresponding to the keywords of each hot event, each hot event is filled into the initial hot event model according to the corresponding model elements to determine the intermediate hot event model; Clustering is performed on the event content of each hot event in the intermediate hot event model to determine multiple hot event types; The hot topic event model is obtained based on the types of hot topics and their corresponding event content.

2. The method according to claim 1, characterized in that, The step of matching each subscription information with a preset hot event model to determine the target event type corresponding to each subscription information includes: Calculate the matching degree between each subscription information and each hot event type in the hot event model to obtain multiple matching degrees corresponding to each subscription information; Based on the multiple matching degrees corresponding to each subscription information, the target event type corresponding to each subscription information is determined.

3. The method according to claim 2, characterized in that, The step of determining the target event type corresponding to each subscription information based on multiple matching degrees includes: Determine the maximum matching degree among multiple matching degrees corresponding to each subscription information; The hot event type corresponding to the maximum matching degree of each subscription information is determined as the target event type of the subscription information.

4. The method according to claim 2, characterized in that, The step of determining the target event type corresponding to each subscription information based on multiple matching degrees includes: Determine whether multiple matching degrees corresponding to the subscription information are all less than a preset threshold; If all of the multiple matching degrees are less than the preset threshold, then the target event type corresponding to the subscription information is determined to be a non-hotspot event.

5. The method according to any one of claims 1-4, characterized in that, The hot topic event model is constructed in the following ways: Get multiple trending events published by the information platform; The hotspot event model is constructed based on the initial hotspot event model and the multiple hotspot events.

6. The method according to claim 5, characterized in that, The step of constructing the hotspot event model based on the initial hotspot event model and the plurality of hotspot events includes: Each of the aforementioned hot topics is analyzed to obtain at least one keyword corresponding to each hot topic. Based on the element information corresponding to the keywords of each hot event, each hot event is filled into the initial hot event model according to the model elements, and the hot event model is determined.

7. The method according to claim 1, characterized in that, The process of clustering the event content of each hot event in the intermediate hot event model to determine multiple hot event types includes: For each hot event in the intermediate hot event model, the hot events are clustered according to the number of model elements and the event content included in the model elements to determine the multiple hot event types; The event content included in the model elements corresponding to different types of hot events varies.

8. An information display device, characterized in that, The device includes: The acquisition module is used to retrieve subscription information published by multiple objects that the user has subscribed to; The matching module is used to match each of the subscription information with a preset hot event model to determine the target event type corresponding to each of the subscription information; the hot event model includes different hot event types and the event content corresponding to each hot event type; The determination module is used to determine the same event type among the various target event types; The display module is used to control the user terminal to collapse and display the subscription information corresponding to the same event type; The construction methods of the hotspot event model include: Obtain the prompt words input by the user, and construct an initial hot topic event model based on the prompt words; the initial hot topic event model includes at least one model element; Based on the element information corresponding to the keywords of each hot event, each hot event is filled into the initial hot event model according to the corresponding model elements to determine the intermediate hot event model; Clustering is performed on the event content of each hot event in the intermediate hot event model to determine multiple hot event types; The hot topic event model is obtained based on the types of hot topics and their corresponding event content.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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