A display method, device, computer device and storage medium

Abstract

The present disclosure provides a display method, device, computer equipment and storage medium, wherein the method comprises: in response to a trigger operation for any book recommendation topic, obtaining a plurality of topic posts under the book recommendation topic and a target filtering option matched with the topic post; wherein the target filtering option is determined based on book recommendation information of the topic post and book information recommended in the topic post; displaying the target filtering option; in response to any target filtering option being selected, displaying a topic post associated with the any target filtering option.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more specifically, to a display method, apparatus, computer device, and storage medium. Background Technology

[0002] The internet offers a wide variety of online books, and to quickly find the books you want to read, you can browse the various topic posts in the book recommendation section.

[0003] However, because the number of posts in book recommendation topics is not fixed, and different posts come from different users, with varying types and numbers of books included in each post, users still need to read the posts carefully to find their target books. This not only increases the complexity of finding target books but also reduces the efficiency of doing so. Summary of the Invention

[0004] This disclosure provides at least one demonstration method, apparatus, computer device, and storage medium.

[0005] In a first aspect, embodiments of this disclosure provide a demonstration method, including:

[0006] In response to a trigger operation for any book recommendation topic, the system retrieves multiple topic posts under the book recommendation topic, as well as target filter items matching the topic posts; wherein, the target filter items are determined based on the book recommendation information of the topic posts and the book information recommended in the topic posts.

[0007] Display the target filter options;

[0008] In response to any of the stated target filters being selected, the topic post associated with said target filter is displayed.

[0009] In one possible implementation, the target filter matching the topic post is determined according to the following method:

[0010] Based on the common information of the books recommended in the topic post, the target attribute features are determined;

[0011] Using the target attribute features, extract recommendation keywords from the book recommendation information of the topic post;

[0012] The target filtering options are generated based on the recommended keywords and the target attribute features.

[0013] In one possible implementation, the target attribute features are used to extract recommendation keywords from the book recommendation information of the topic post, including:

[0014] According to the preset segmentation dimensions, the book recommendation information of each topic post is segmented to obtain at least one initial keyword for each topic post under the preset segmentation dimensions; wherein, the preset segmentation dimensions include at least one of the following: a first segmentation dimension determined according to the relationships between characters in the book, a second segmentation dimension determined according to the personalities of characters in the book, and a third segmentation dimension determined according to the word class attribute;

[0015] From the initial keywords, recommended keywords that match the target attribute features are selected.

[0016] In one possible implementation, the target filtering items are generated based on the recommended keywords and the target attribute features, including:

[0017] At least one first filter item is generated based on the recommended keywords, and at least one second filter item is generated based on the target attribute features;

[0018] The target filter is determined based on the at least one first filter and the at least one second filter.

[0019] In one possible implementation, determining the target filter based on the at least one first filter and the at least one second filter includes:

[0020] Determine the number of filter items corresponding to the target type based on the type of each of the second filter items;

[0021] If the number of filter items is greater than the first preset number, the sorting order of each second filter item of the target type is determined according to the first number of books corresponding to each second filter item of the target type, in descending order of quantity.

[0022] The second filter item whose sorting order is greater than the preset order, the at least one first filter item, and other second filter items other than the second filter item of the target type are used as the target filter item.

[0023] In one possible implementation, the second filter item includes a first sub-filter item for characterizing the book type of the book;

[0024] The step of generating at least one second filtering item based on the target attribute features includes:

[0025] Semantic recognition is performed on the book recommendation topics to determine the book search intent; the book search intent is used to indicate the attribute features of the books to be searched corresponding to the book recommendation topics;

[0026] If the type of book search intent matches a first preset type, the reading type indication information corresponding to the user who triggered the book recommendation topic is obtained; the first preset type includes non-targeted type and / or type with a rating higher than a set threshold; the reading type indication information is used to indicate the types of books of interest.

[0027] Based on the book types indicated by the target attribute features, determine the second number of first books under each of the book types of interest;

[0028] Based on the type of book of interest whose corresponding second quantity is greater than the second preset quantity, at least one of the first sub-filter items is generated.

[0029] In one possible implementation, the second filter item includes a second sub-filter item for characterizing the book's subject matter;

[0030] The step of generating at least one second filtering item based on the target attribute features includes:

[0031] If the type of book search intent matches the second preset type, determine the third number of topic posts in the book recommendation topics; the second preset type includes at least one preset book genre type;

[0032] If the third quantity is greater than the third preset quantity, a fourth quantity of second books that match the type of book search intent is determined based on the book genres of each book indicated by the target attribute features.

[0033] If the ratio between the fourth quantity and the total number of books corresponding to the book information is greater than a preset ratio, at least one second sub-filter item is generated according to the type of book search intent.

[0034] In one possible implementation, the second filter item includes a third sub-filter item for characterizing the completion status of the book and / or a fourth sub-filter item for characterizing the book rating.

[0035] The step of generating at least one second filtering item based on the target attribute features includes:

[0036] Based on the book update status indicated by the target attribute features, if it is determined that there are books whose update status is completed, the third sub-filter item is generated; and / or

[0037] If, based on the book ratings of the books indicated by the target attribute features, it is determined that there are books with a rating greater than a set threshold, the fourth sub-filter item is generated.

[0038] Secondly, embodiments of this disclosure also provide a display device, comprising:

[0039] The acquisition module is used to respond to a trigger operation for any book recommendation topic, acquire multiple topic posts under the book recommendation topic, and target filter items matching the topic posts; wherein, the target filter items are determined based on the book recommendation information of the topic posts and the book information recommended in the topic posts;

[0040] The first display module is used to display the target filter items;

[0041] The second display module is used to display topic posts associated with any of the target filter items when any of the target filter items is selected.

[0042] Thirdly, an optional implementation of this disclosure also provides a computer device, a processor, and a memory, wherein the memory stores machine-readable instructions executable by the processor, and the processor is configured to execute the machine-readable instructions stored in the memory. When the machine-readable instructions are executed by the processor, the steps of the first aspect above, or any possible implementation of the first aspect, are performed.

[0043] Fourthly, an optional implementation of this disclosure also provides a computer-readable storage medium storing a computer program that, when run, performs the steps of the first aspect or any possible implementation of the first aspect.

[0044] For a description of the effects of the aforementioned display device, computer equipment, and computer-readable storage medium, please refer to the description of the display method above; it will not be repeated here.

[0045] The display method, apparatus, computer device, and storage medium provided in this disclosure, since the filtering options are determined based on book recommendation information for each topic post and the book information recommended in each topic post, can associate each obtained target filtering option with the attribute information of a certain number of topic posts. By obtaining and displaying target filtering options matching topic posts, users can be provided with filtering choices. Furthermore, by displaying topic posts associated with any selected target filtering option on the current page, flexible filtering of topic posts under book recommendation topics can be achieved based on each target filtering option, resulting in topic posts that meet the book search needs. Then, by using the filtered topic posts to search for books, the efficiency of book searching can be effectively improved.

[0046] In other words, the display method provided in this embodiment allows users to filter topic posts under book recommendation topics by displaying target filter options, thus obtaining topic posts that better meet their book search needs. Using the filtered topic posts to search for books can effectively improve search efficiency.

[0047] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0049] Figure 1 A flowchart illustrating a demonstration method provided by an embodiment of this disclosure is shown;

[0050] Figure 2 A schematic diagram of a details page provided in an embodiment of this disclosure is shown;

[0051] Figure 3 A schematic diagram illustrating an embodiment of this disclosure showing other target filters is shown;

[0052] Figure 4 A schematic diagram of another details page provided in an embodiment of this disclosure is shown;

[0053] Figure 5 A schematic diagram of a display device provided in an embodiment of this disclosure is shown;

[0054] Figure 6 A schematic diagram of a computer device structure provided in an embodiment of this disclosure is shown. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown herein can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0056] Furthermore, the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein.

[0057] In this article, "multiple or several" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0058] Research has found that most users have a habit of reading online in their daily lives. However, due to the vast variety and quantity of online books available, finding target books from this large pool has become a significant challenge. Typically, users can initiate book recommendation topics and then view other users' responses within those topics. Alternatively, they can search for books by browsing existing posts within published book recommendation topics. However, regardless of the method used, the efficiency of finding a target book is affected by the number of posts within the recommended topic and the variety and quantity of books listed, resulting in inconsistent search efficiency.

[0059] Based on the above research, this disclosure provides a display scheme. Since the filter options are determined based on book recommendation information from various topic posts and the book information recommended within those topic posts, each obtained target filter option can be associated with the attribute information of a certain number of topic posts. By obtaining and displaying target filter options matching topic posts, users can be provided with filtering choices. Furthermore, in response to any target filter option being selected, the topic posts associated with that target filter option are displayed on the current page. This allows for flexible filtering of various topic posts under book recommendation topics based on each target filter option, resulting in topic posts that meet the book search needs. Furthermore, using the filtered topic posts to search for books can effectively improve book search efficiency.

[0060] The shortcomings of the above solutions are the result of the inventor's practical experience and careful research. Therefore, the discovery process of the above problems and the solutions proposed in this disclosure below should be considered as the inventor's contribution to this disclosure.

[0061] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0062] 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.

[0063] To facilitate understanding of this embodiment, a demonstration method disclosed in this disclosure will first be described in detail. The execution subject of the demonstration method provided in this disclosure is generally a terminal device or other processing device with certain computing power. The terminal device may be a user equipment (UE), mobile device, user terminal, terminal, personal digital assistant (PDA), handheld device, computer device, etc. In some possible implementations, the demonstration method can be implemented by the processor calling computer-readable instructions stored in the memory.

[0064] The following description uses a computer device as an example to illustrate the demonstration method provided in the embodiments of this disclosure.

[0065] like Figure 1 The flowchart shown is a demonstration method provided by an embodiment of this disclosure, which may include the following steps:

[0066] S101: In response to a trigger operation for any book recommendation topic, obtain multiple topic posts under the book recommendation topic, as well as target filter items matching the topic posts; wherein, the target filter items are determined based on the book recommendation information of the topic posts and the book information recommended in the topic posts.

[0067] Here, a book recommendation topic is a user-initiated discussion for finding books. Each book recommendation topic can include one or more user responses to that topic; each response is called a topic post. A topic post can include book recommendation information and book information. The book information specifically includes details about the books recommended in the topic post, such as book title, cover, attributes, and author. The book recommendation information represents the reasons why users recommend each book in the topic post.

[0068] The target filters are determined based on the book recommendation information and the book information within each topic post. A topic post can have one or more target filters, and the number and types of target filters vary from topic post to topic post. Target filters can be used to filter out topic posts that have a specific target filter from multiple topic posts under the book recommendation topic. For each target filter that matches a topic post, there is at least one topic post that has that target filter.

[0069] The trigger actions for book recommendation topics can be actions that allow users to view the topic, such as clicking on it, or actions that allow users to actively search for it. For example, a book discussion community can display multiple book recommendation topics initiated by various users. When a user decides they want to view a specific book recommendation topic, they can trigger the viewing by clicking on it.

[0070] For example, for each book recommendation topic in a book discussion community, the target filters matching each topic post within that topic can be determined first, based on the book recommendation information and the book information within each topic post. Since there can be multiple topic posts, and one topic post can have one or more target filters determined, the final number of target filters matching each topic post can be multiple. Then, in response to a user's click on any book recommendation topic in the book discussion community, the various topic posts under that book recommendation topic, along with the pre-determined target filters matching those topic posts, are retrieved.

[0071] S102: Display the target filter options.

[0072] In practice, after obtaining the target filter that matches the topic post under the book recommendation topic, you can jump to the details page corresponding to the triggered book recommendation topic and display the target filter on the details page.

[0073] like Figure 2 The diagram shown is a schematic of a details page provided in an embodiment of this disclosure, wherein the details page displays the name of a book recommendation topic (i.e., Figure 2 The text appears to be a list of keywords or tags, possibly related to a search query or similar feature. It includes phrases like "If you could travel through time, you would be...", multiple target filters arranged according to a preset sorting rule, and the number of eligible topic posts under each target filter. Figure 2 (All 123, Latest, Completed 123, High Score 12,000, Ancient Romance) Here, it should be noted that when there are many target filter options and the page cannot fully display all of them, it can respond to the user's swipe operation in the area displaying target filter options, and display other target filter options according to the swipe distance. Figure 2 The text displays two topic posts, topic post 1 and topic post 2. The first topic post contains book information for five books (books 1 through 5). Figure 2 The "Recommended Books" button in the post can be used to generate a book recommendation page in response to user clicks, where users can then actively recommend books. Furthermore, to ensure that the "Completed" filter for book 6 in topic post 2 is prominently displayed, if the book recommendation information in topic post 2 is too extensive, the recommendation information can be collapsed and an expand button displayed. This allows for full display of the book recommendation information in response to triggering the expand button.

[0074] S103: In response to any target filter being selected, display the topic posts associated with that target filter.

[0075] Here, the topic post associated with the target filter is the topic post that has that target filter. A topic post can have multiple target filters, which may include target filters determined from book recommendation information and / or target filters possessed by each book in the topic post. Target filters determined from book recommendation information can also be referred to as target filters possessed by the book recommendation information.

[0076] For example, in response to a user's click on any target filter item displayed on the details page, the details page can display various topic posts associated with that target filter item, and when displaying the associated topic posts, all target filters items present in the topic posts can be highlighted.

[0077] For example, in Figure 2In the first post, the target filter "Completed" is selected. Since the book recommendations in post 1 do not have this target filter, while books 1 and 3-5 within post 1 do, the details page can display only a portion of the book recommendations from post 1, showing the full content of each book and highlighting the "Completed" target filter for books 1 and 3-5. Because post 2 is positioned below post 1, even if the book recommendations in post 2 do not have this target filter, it will still be displayed to maintain page continuity. Book 6 within post 2 has the "Completed" target filter, and therefore this filter will be highlighted.

[0078] Of course, it can also respond to user requests. Figure 2 The swipe gesture within the area containing the target filter will display other target filters based on the swipe distance. For example... Figure 3 The diagram shown is a schematic representation of an embodiment of this disclosure illustrating other target filtering options. Figure 3 The target filters displayed include "Completed", "High Score", "Ancient Romance" and "Don't mind XX".

[0079] For example, targeting Figure 3 In this regard, the target filter "ancient romance" was selected. Figure 3 The displayed topics are still topic post 1 and topic post 2. Among them, books 1, 3, and 4 in topic post 1 all have the target filter "ancient romance", so they can be highlighted. Book 6 in topic post 2 also has the target filter "ancient romance", so this target filter will also be highlighted.

[0080] like Figure 4 The diagram shown is a schematic of another details page provided in an embodiment of this disclosure, wherein... Figure 4 The target filter options displayed in the middle are Figure 3 The target filter is consistent. Figure 4 The target filter option "Don't mind XX" was selected. Figure 4 The topic posts displayed include topic post 1 and topic post 2. Since the book recommendation information in topic post 1 has the target filter "Don't mind XX", the target filter in the book recommendation information can be highlighted. The book recommendation information in topic post 2 also has the target filter "Don't mind XX", so it will also be highlighted.

[0081] In this way, since the filter options are determined based on book recommendation information from each topic post and the book information recommended within each topic post, each target filter option can be associated with the attribute information of a certain number of topic posts. By obtaining and displaying target filter options that match topic posts, users can be provided with filtering choices. Furthermore, by displaying the topic posts associated with any selected target filter option on the current page, users can flexibly filter topic posts under book recommendation topics based on each target filter option, obtaining topic posts that meet their book search needs. Then, by using the filtered topic posts to search for books, the efficiency of book searching can be effectively improved.

[0082] In one embodiment, the target filter items matching the topic post can be determined according to steps one through three:

[0083] Step 1: Determine the target attribute features based on the common information of the books recommended in the topic post.

[0084] Here, the book information corresponds to the books recommended in the topic post. For example, Figure 2 The recommended books in topic post 1 are categorized as books 1 through 5. Shared information refers to information shared by at least two books within each category. This shared information includes, for example, shared book content, genre, update status, plot, author, rating, character relationships, and character traits. Additionally, shared information may include shared attributes such as book title and publisher. Target attribute features are used to characterize the shared information. Specifically, target attribute features may include the book's update status, rating, type, genre, character relationships, and character traits. Update status can be either completed or ongoing; genre can include publication type, comic book type, and audiobook type; and genre can be, for example, historical romance, fantasy, martial arts, science fiction, suspense, or ancient history.

[0085] In practice, for any topic post, we can first perform content analysis on each book corresponding to the book information recommended in the topic post, and determine the common information of each book based on the analysis results and the book information of each book. Then, we can use the common information as target attribute features.

[0086] Step 2: Utilize target attribute features to extract recommended keywords from the book recommendation information in the topic post.

[0087] Here, recommended keywords are keywords related to the target attribute features obtained after segmenting the book recommendation information. For example, Figure 2 The phrase "Don't mind XX" in the book recommendation information of topic post 1 is a recommended keyword that has been extracted.

[0088] For example, for each topic post, a pre-trained word segmentation network can be used to segment the book recommendation information of the topic post according to the target attribute features, so as to obtain various recommended keywords related to the target attribute features.

[0089] In one embodiment, the step of "extracting recommendation keywords from book recommendation information using target attribute features" can be implemented according to the following steps:

[0090] According to the preset segmentation dimensions, the book recommendation information of each topic post is segmented separately to obtain at least one initial keyword for each topic post under the preset segmentation dimensions;

[0091] The preset segmentation dimensions include at least one of the following: a first segmentation dimension determined by the relationships between characters in the book, a second segmentation dimension determined by the personalities of characters in the book, and a third segmentation dimension determined by word class attributes.

[0092] For example, the first segmentation dimension is determined according to the possible relationships between characters in the book. Specifically, a first segmentation rule related to the first segmentation dimension can be pre-defined, and this first segmentation rule indicates a first word class collocation rule. For example, for the first segmentation dimension, the first word class collocation rule may include, but is not limited to, the following rules: female lead + verb + male lead, male lead + verb + female lead, female lead is male lead's XX, male lead is female lead's XX, no CP, no female lead, no male lead, etc., where the female lead and male lead in the first word class collocation rule can also be specific character names.

[0093] The second segmentation dimension can be determined based on the possible personalities / roles of the characters in the book. Specifically, second segmentation rules related to the second segmentation dimension can be pre-defined, and these rules indicate second word class collocation rules. For example, for the second segmentation dimension, the second word class collocation rules can include, but are not limited to, the following rules: female protagonist + adjective, male protagonist + adjective, female protagonist is + noun / adjective, male protagonist is + noun / adjective, female + adjective, male + adjective, male protagonist and female protagonist are + noun / adjective, etc. Among these, the female protagonist and male protagonist in the second word class collocation rules can also be specific character names.

[0094] The third segmentation dimension can be a segmentation dimension determined based on the part-of-speech attribute, where the part-of-speech attribute can be noun, adjective, verb, etc.

[0095] It should be noted that, for each topic post, when segmenting the book recommendation information of the topic post according to various preset segmentation dimensions, the character length of each initial keyword can be arbitrary.

[0096] In practice, for each topic post, the book recommendation information of the topic post can be segmented using the first segmentation dimension, the second segmentation dimension, and the third segmentation dimension to obtain the initial keywords under the three segmentation dimensions.

[0097] By segmenting book recommendation information using multiple dimensions, the comprehensiveness and accuracy of the obtained recommendation keywords can be improved.

[0098] Then, recommended keywords that match the target attribute features can be filtered from the initial keywords.

[0099] For example, after obtaining the initial keywords, the correlation between each initial keyword and each target attribute feature can be determined. If the correlation between the initial keyword and any target attribute feature is greater than a set value, the initial keyword can be used as a recommended keyword; if the correlation between the initial keyword and any target attribute feature is not greater than the set value, the initial keyword can be filtered.

[0100] Step 3: Generate target filtering options based on recommended keywords and target attribute features.

[0101] For example, the selected recommended keywords can be directly used as the names of the target filter items, and target filter items with those names can be generated. Similarly, target attribute features such as book update status, book rating, book type, and book genre can be used as the names of the target filter items, and target filter items with these names can be generated.

[0102] In one embodiment, step three above can be implemented according to the following S1 and S2:

[0103] S1: Generate at least one first filter item based on recommended keywords, and generate at least one second filter item based on target attribute features.

[0104] Here, the first selection option is represented by recommended keywords, and the second selection option is represented by target attribute features.

[0105] For example, recommended keywords can be filtered first to obtain at least one keyword that can be used to generate a first filter item. For instance, the number of topic posts corresponding to each recommended keyword can be calculated, and recommended keywords with a number greater than a certain value can be selected as the filtered recommended keywords. Then, a first filter item with a corresponding name can be generated based on the selected recommended keywords. Simultaneously, based on the number of books possessing each target attribute feature, at least one target attribute feature can be selected from the target attribute features to be used. Then, each target attribute feature to be used can be used as the name of a second filter item, and a second filter item with a corresponding name can be generated.

[0106] In another embodiment, the second filter item may include a first sub-filter item for characterizing the book type. For example, the first sub-filter item may be... Figure 3 The target filtering options are used to characterize the genre of ancient romance novels. The step of generating a second filtering option based on the target attribute features can be implemented according to the following steps:

[0107] P1: Perform semantic recognition on book recommendation topics to determine book search intent; book search intent is used to indicate the attribute features of the books to be searched corresponding to the book recommendation topic.

[0108] Here, attribute features can include information such as high scores, book type, and book genre. Book search intent characterizes what kind of books a user wants to be recommended. Book search intent can have different types, specifically including undirected types, types with ratings above a set threshold, types of published books, types of comic books, types of audiobooks, and multi-intent types. Undirected types are those without a clear intent; for example, in the case of a book recommendation topic "I'm out of books to read, please recommend some," the book search intent for that topic is undirected. For types with ratings above a set threshold, the book search intent corresponding to the book recommendation topic "Please recommend high-scoring novels" falls into this category. For multi-intent types, the book search intent corresponding to the book recommendation topic "If I could time travel, what would a high-powered male protagonist with superb medical skills be like?" is a multi-intent type. In one embodiment, all types except undirected types, types with ratings above a set threshold, types of published books, types of comic books, and types of audiobooks can be considered multi-intent types.

[0109] For example, a semantic recognition neural network can be used to perform semantic recognition on book recommendation topics, determine the semantic information of the book recommendation topics, and determine the book search intent represented by the book recommendation topics based on the semantic information.

[0110] The intent to search for books can also be determined based on the category and title of the book recommendation topic. For example, if the book recommendation topic is categorized as a publishing category or the title includes information such as publishing category, book title, and author, the search intent can be determined as searching for published books. Similarly, if the book recommendation topic is categorized as a comic book category or the title includes information such as comic book category, comic book title, and author, the search intent can be determined as searching for comic books. And if the book recommendation topic is categorized as an audiobook category or the title includes information such as audiobook category, audiobook title, and author, the search intent can be determined as searching for audiobooks.

[0111] P2: If the type of book search intent matches the first preset type, obtain the reading type indication information corresponding to the user who triggered the book recommendation topic; the first preset type includes undirected type and / or type with a rating higher than a set threshold; the reading type indication information is used to indicate the types of books of interest.

[0112] Here, the reading type indication information is information authorized by the user, and the "interested book types" refers to the types of books the user prefers to read. Understandably, the reading type indication information is used to indicate the various book types the user prefers to read. For example, the reading type indication information might indicate five book types the user prefers to read.

[0113] In practice, after determining the intent to search for books, the type of that intent can be identified first. Then, if the type matches a first preset type, the reading type indication information corresponding to the user who triggered the book recommendation topic can be obtained.

[0114] P3: Based on the book types indicated by the target attribute features, determine the second number of the first book under each type of book of interest.

[0115] Here, the target attribute feature can indicate the book type of each book, with the first book being the book of interest.

[0116] In practice, for each type of book of interest, a second number of first books that match the type of book of interest can be determined based on the book types indicated by the target attribute features.

[0117] P4: Generate at least one first sub-filter item based on the type of books of interest whose second quantity is greater than the second preset quantity.

[0118] Here, the second preset quantity can be set based on experience, and this embodiment does not impose a specific limitation. For example, the second preset quantity can be 6, 8, 10, etc. The second quantity corresponding to a type of book of interest is the second quantity of the first book under that type of book of interest.

[0119] In practice, the corresponding second number of book types of interest that are greater than the second preset number can be filtered out, and a first sub-filter item can be generated to represent each of the filtered book types of interest. That is, the name of the first sub-filter item is each of the filtered book types of interest.

[0120] In this way, by generating the first sub-filter, users can easily filter topic posts based on book type.

[0121] In another embodiment, when the type of book search intent matches the second preset type, the second filter item may further include a second sub-filter item representing the book's genre. The second preset type includes at least one preset book genre type. Specifically, the second preset type may include a search for published books corresponding to a publishing genre, a search for comic books corresponding to a comic book genre, and a search for audiobooks corresponding to an audiobook genre. The step of generating the second filter item based on target attribute features can be implemented according to the following steps:

[0122] T1: Determine the third number of topic posts in the book recommendation topics.

[0123] For example, if the type of book search intent matches the second preset type, the number of topic posts in the book recommendation topics can be determined first, i.e., the third number.

[0124] T2: If the third quantity is greater than the third preset quantity, determine the fourth quantity of second books that match the type of book search intent based on the book genre indicated by the target attribute characteristics.

[0125] Here, the target attribute features can also indicate the book genres of each book. The third preset number can be set based on experience, and this embodiment does not impose a specific limitation. For example, the third preset number can be 6, 7, 8, etc. The second book is a book whose genre matches the type of book search intent.

[0126] For example, after determining the third quantity, it can be determined whether the third quantity is greater than the third preset quantity. If so, a fourth quantity of books whose book genres match the search intent can be found from all the books recommended in the topic post, based on the book genres indicated by the target attribute features. For instance, if the search intent is to find published books, then a fourth quantity of books whose genre is published is determined.

[0127] T3: If the ratio between the fourth quantity and the total number of books corresponding to the book information is greater than the preset ratio, generate at least one second sub-filter item based on the type of book search intent.

[0128] Here, the total number of books is the sum of the number of books included in each topic post under the book recommendation topic. The preset ratio can be set based on experience, and this embodiment does not impose a specific limitation. For example, the preset ratio can be 0.7, 0.8, etc. When the type of book search intent is to search for published books, the second preset type that the type of book search intent conforms to is the type of published books; when the type of book search intent is to search for comic books, the second preset type that the type of book search intent conforms to is the type of comic books; when the type of book search intent is to search for audiobooks, the second preset type that the type of book search intent conforms to is the type of audiobooks.

[0129] For example, if the ratio of the fourth number of books in the category of "published books" to the total number of books is greater than 0.7, a second sub-filter named "Published Books" can be generated. Of course, multiple second sub-filters can also be generated based on the type of book search intent. For instance, if the ratio of the fourth number of books in the category of "published books" to the total number of books is greater than 0.7, a second sub-filter named "Published Books," a second sub-filter named "Search Published Books," and a second sub-filter named "Non-Published Books" can be generated.

[0130] In this way, by generating a second sub-filter, users can easily filter topic posts based on book themes.

[0131] In another embodiment, the second filter item may include a third sub-filter item characterizing the completion status of the book and / or a fourth sub-filter item characterizing the book's rating. For example, the fourth sub-filter item may be... Figure 2 The high-score filtering option can be defined as a book rating greater than a set threshold, such as a rating greater than 9 points. The step of generating a second filtering option based on the target attribute features can be implemented as follows:

[0132] Based on the book update status indicated by the target attribute features, if it is determined that there are books whose update status is completed, a third sub-filter is generated.

[0133] Here, the target attribute features can indicate the update status of each book.

[0134] For example, for each book in a topic post, the update status of the book, indicated by the target attribute features, can be used to determine whether the book is in a completed state. If so, the book can be determined to have a target filter indicating a completed state, and a third sub-filter can be generated to represent the completed state. This third sub-filter can be generated if at least one book is determined to be in a completed state, or if a fourth preset number of books are in a completed state. Conversely, if none of the books are determined to be in a completed state, no third sub-filter can be generated, meaning that the book recommendation topic does not have a target filter to represent a completed state.

[0135] And / or, if, based on the book ratings of each book indicated by the target attribute features, it is determined that there are books with a rating greater than a set threshold, a fourth sub-filter is generated.

[0136] Here, the target attribute features can indicate the book ratings for each book.

[0137] For example, for each book in a topic post, the book's rating can be determined based on the book's update status indicated by the target attribute features. If so, it can be determined that the book has a target filter indicating a high rating, and a fourth sub-filter is generated to represent the high rating. This fourth sub-filter can be generated if at least one book has a rating greater than the set threshold, or if a fifth preset number of books have ratings greater than the preset threshold. Conversely, if none of the books have ratings greater than the preset threshold, no fourth sub-filter is generated, meaning it can be determined that the book recommendation topic does not have a target filter to represent a high rating.

[0138] In this way, by generating the third and fourth sub-filters, a general target filter can be obtained, which can satisfy users' filtering of various topics under the book recommendation topic in the conventional way.

[0139] S2: Determine the target filter based on at least one first filter and at least one second filter.

[0140] For example, after obtaining at least one first filter item and at least one second filter item, each first filter item and each second filter item can be directly used as the target filter item; or any number of first filter items and second filter items can be selected as the target filter item.

[0141] In one embodiment, S2 can also be implemented according to the following steps:

[0142] S2-1: Determine the number of filter items corresponding to the target type based on the type of each second filter item.

[0143] Here, the type of the second filter option can specifically include the types corresponding to the first, second, third, and fourth sub-filter options mentioned above. The target type can be the type corresponding to the type of books you are interested in, and the second filter option for the target type is the first sub-filter option.

[0144] For example, based on the type of each second filter item, a first sub-filter item can be selected from the second filter items and the number of first sub-filter items can be determined, which is the number of filter items.

[0145] S2-2: When the number of filter items is greater than the first preset number, the sorting order of each second filter item of the target type is determined according to the first number of books corresponding to each second filter item of the target type, in descending order of quantity.

[0146] Here, the aforementioned first preset quantity can be set based on experience, and this embodiment of the disclosure does not impose a specific limitation. For example, the first preset quantity can be 5, 6, etc. The first quantity of books corresponding to the second filter item of the target type is the first quantity of books under the type of books of interest corresponding to the second filter item.

[0147] For example, when the number of filter items is greater than the first preset number, each second filter item of the target type can be sorted according to the first number corresponding to each second filter item of the target type in descending order of quantity, so as to obtain the sorting order of each second filter item.

[0148] S2-3: Select the second filter item whose sorting order is greater than the preset order, at least one first filter item, and other second filter items other than the target type as the target filter item.

[0149] Here, the second filter options other than the target type may include one or more of the second, third, and fourth sub-filter options. The preset order can be set according to the maximum number of book types of interest to be displayed, and this embodiment does not impose a specific limitation. For example, if the maximum number of book types of interest to be displayed is 2, the preset order can be 2.

[0150] For example, the second filter item of the target type with a sorting order greater than 2, at least one first filter item, and other second filter items besides the second filter item of the target type can be used as the final determined target filter item.

[0151] In this way, by filtering the second filter options for certain target types, the displayed second filter options can represent the types of books that users are most interested in, thus improving the user's filtering experience.

[0152] In one embodiment, the step of generating the first filter item based on the recommended keywords can be implemented according to the following sub-steps:

[0153] Sub-step 1: For each topic post, filter out the candidate keywords corresponding to that topic post based on the similarity between the recommended keywords in that topic post and the topic information of the book recommendation topic.

[0154] Here, the topic information of a book recommendation topic is the text information of that topic. For example, if the book recommendation topic is "If you could time travel, what would you be…", then the topic information is "If you could time travel, what would you be…". If the book recommendation topic is "I'm experiencing a reading slump, and I recommend highly-rated romance novels", then the topic information is "I'm experiencing a reading slump, and I recommend highly-rated romance novels". Candidate keywords are the recommended keywords corresponding to the topic post that have a similarity greater than a preset similarity threshold with the topic information. The preset similarity threshold can be set based on experience, and this embodiment does not impose specific limitations. For example, the preset similarity threshold can be 0.7, 0.8, 0.9, etc.

[0155] In practice, for each topic post, the similarity between each recommended keyword in that topic post and the topic information of the book recommendation topic can be calculated separately. The similarity between the recommended keywords and the topic information can be calculated using methods such as length-adaptive edit distance calculation, cosine similarity algorithm, and Manhattan distance calculation algorithm. Then, based on the calculated similarity, candidate keywords with similarity scores greater than a preset similarity threshold can be selected from the recommended keywords in that topic post.

[0156] Sub-step two: Based on the candidate keywords corresponding to each topic post, determine the target number of topic posts corresponding to each candidate keyword.

[0157] For example, based on the above sub-step one, candidate keywords corresponding to each topic post can be determined. Then, based on the candidate keywords corresponding to each topic post, the first number of topic posts corresponding to each candidate keyword can be determined. Here, the target number can be understood as the number of topic posts in the book recommendation information that include the candidate keyword for a given candidate keyword.

[0158] Sub-step 3: Based on the target quantity corresponding to each candidate keyword, determine at least one target keyword, and based on the determined at least one target keyword, generate at least one first filter item.

[0159] Here, the target keywords are keywords selected from the candidate keywords, and the number of targets corresponding to them is greater than a preset threshold. The preset threshold can be set based on experience, and this embodiment does not impose a specific limitation. For example, the preset threshold can be 6, 8, 10, etc.

[0160] For example, target keywords whose target quantity is greater than a preset threshold can be selected from the candidate keywords based on the target quantity corresponding to each candidate keyword. Alternatively, the candidate keywords can be sorted in descending order based on their target quantity, and the candidate keywords whose sorting order is earlier than the specified order can be used as target keywords.

[0161] After obtaining the target keywords, each keyword can be directly used as the name of the target filter item, thus generating target filter items that represent each target keyword. Here, after generating each target filter item, the display position of each target filter item on the details page can be determined based on the number of targets or the sorting order of each target keyword. For example, the target filter item with the largest number of targets (or the highest sorting order) is displayed on the far left of the area containing target filter items on the details page, while the target filter item with the smallest number of targets (or the lowest sorting order) is displayed on the far right.

[0162] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0163] Based on the same inventive concept, this disclosure also provides a display device corresponding to the display method. Since the principle of the device in this disclosure for solving the problem is similar to the above-mentioned display method in this disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0164] like Figure 5 The diagram shown is a schematic representation of a display device provided in an embodiment of this disclosure, comprising:

[0165] The acquisition module 501 is used to respond to a trigger operation for any book recommendation topic, acquire multiple topic posts under the book recommendation topic, and target filter items matching the topic posts; wherein, the target filter items are determined based on the book recommendation information of the topic posts and the book information recommended in the topic posts;

[0166] The first display module 502 is used to display the target filter items;

[0167] The second display module 503 is used to display topic posts associated with any of the target filter items when any of the target filter items is selected.

[0168] In one possible implementation, the apparatus further includes a determining module 504 for determining target filter items that match the topic post in the following manner:

[0169] Based on the common information of the books recommended in the topic post, the target attribute features are determined;

[0170] Using the target attribute features, extract recommendation keywords from the book recommendation information of the topic post;

[0171] The target filtering options are generated based on the recommended keywords and the target attribute features.

[0172] In one possible implementation, the determining module 504, when extracting recommended keywords from the book recommendation information of the topic post using the target attribute features, is used to:

[0173] According to the preset segmentation dimensions, the book recommendation information of each topic post is segmented to obtain at least one initial keyword for each topic post under the preset segmentation dimensions; wherein, the preset segmentation dimensions include at least one of the following: a first segmentation dimension determined according to the relationships between characters in the book, a second segmentation dimension determined according to the personalities of characters in the book, and a third segmentation dimension determined according to the word class attribute;

[0174] From the initial keywords, recommended keywords that match the target attribute features are selected.

[0175] In one possible implementation, the determining module 504, when generating the target filtering items based on the recommended keywords and the target attribute features, is configured to:

[0176] At least one first filter item is generated based on the recommended keywords, and at least one second filter item is generated based on the target attribute features;

[0177] The target filter is determined based on the at least one first filter and the at least one second filter.

[0178] In one possible implementation, the determining module 504, when determining the target filter based on the at least one first filter and the at least one second filter, is configured to:

[0179] Determine the number of filter items corresponding to the target type based on the type of each of the second filter items;

[0180] If the number of filter items is greater than the first preset number, the sorting order of each second filter item of the target type is determined according to the first number of books corresponding to each second filter item of the target type, in descending order of quantity.

[0181] The second filter item whose sorting order is greater than the preset order, the at least one first filter item, and other second filter items other than the second filter item of the target type are used as the target filter item.

[0182] In one possible implementation, the second filter item includes a first sub-filter item for characterizing the book type of the book;

[0183] The determining module 504, when generating at least one second filtering item based on the target attribute features, is used to:

[0184] Semantic recognition is performed on the book recommendation topics to determine the book search intent; the book search intent is used to indicate the attribute features of the books to be searched corresponding to the book recommendation topics;

[0185] If the type of book search intent matches a first preset type, the reading type indication information corresponding to the user who triggered the book recommendation topic is obtained; the first preset type includes non-targeted type and / or type with a rating higher than a set threshold; the reading type indication information is used to indicate the types of books of interest.

[0186] Based on the book types indicated by the target attribute features, determine the second number of first books under each of the book types of interest;

[0187] Based on the type of book of interest whose corresponding second quantity is greater than the second preset quantity, at least one of the first sub-filter items is generated.

[0188] In one possible implementation, the second filter item includes a second sub-filter item for characterizing the book's subject matter;

[0189] The determining module 504, when generating at least one second filtering item based on the target attribute features, is used to:

[0190] If the type of book search intent matches the second preset type, determine the third number of topic posts in the book recommendation topics; the second preset type includes at least one preset book genre type;

[0191] If the third quantity is greater than the third preset quantity, a fourth quantity of second books that match the type of book search intent is determined based on the book genres of each book indicated by the target attribute features.

[0192] If the ratio between the fourth quantity and the total number of books corresponding to the book information is greater than a preset ratio, at least one second sub-filter item is generated according to the type of book search intent.

[0193] In one possible implementation, the second filter item includes a third sub-filter item for characterizing the completion status of the book and / or a fourth sub-filter item for characterizing the book rating.

[0194] The determining module 504, when generating at least one second filtering item based on the target attribute features, is used to:

[0195] Based on the book update status indicated by the target attribute features, if it is determined that there are books whose update status is completed, the third sub-filter item is generated; and / or

[0196] If, based on the book ratings of the books indicated by the target attribute features, it is determined that there are books with a rating greater than a set threshold, the fourth sub-filter item is generated.

[0197] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0198] Based on the same technical concept, embodiments of this application also provide a computer device. (Refer to...) Figure 6 The diagram shown is a structural schematic of a computer device provided in an embodiment of this application, including:

[0199] The system includes a processor 61, a memory 62, and a bus 63. The memory 62 stores machine-readable instructions executable by the processor 61. The processor 61 executes these machine-readable instructions, and when executed, performs the following steps: S101: In response to a trigger operation for any book recommendation topic, the processor obtains multiple topic posts under the book recommendation topic, and target filter items matching the topic posts; wherein the target filter items are determined based on the book recommendation information of the topic posts and the book information recommended in the topic posts; S102: The processor displays the target filter items; and S103: In response to any target filter item being selected, the processor displays the topic posts associated with that target filter item.

[0200] The aforementioned memory 62 includes a main memory 621 and an external memory 622. The main memory 621, also known as internal memory, is used to temporarily store the computational data in the processor 61, as well as the data exchanged with external memory such as a hard disk. The processor 61 exchanges data with the external memory 622 through the main memory 621. When the computer device is running, the processor 61 and the memory 62 communicate through the bus 63, so that the processor 61 executes the execution instructions mentioned in the above method embodiments.

[0201] This disclosure also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the illustrated method described in the above method embodiments. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0202] The computer program product of the demonstration method provided in this disclosure includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the steps of the demonstration method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0203] The computer program product can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0204] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0205] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0206] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0207] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0208] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, with clear signs / information informing users of the personal information processing rules, authorization is obtained from the user through pop-up information or by asking the user to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

[0209] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A display method, characterized in that, include: In response to a trigger operation for any book recommendation topic, the system retrieves multiple topic posts under the book recommendation topic, as well as target filtering items matching the topic posts. The target filtering items are determined based on recommendation keywords extracted from the book recommendation information of the topic posts and target attribute features of the book information recommended in the topic posts. The target attribute features are determined based on common information of each book corresponding to the book information, and the recommendation keywords are extracted using the target attribute features. The target filter option is displayed on the details page corresponding to the triggered book recommendation topic; In response to any of the target filters being selected, the topic post associated with any of the target filters is displayed on the details page.

2. The method according to claim 1, characterized in that, The extraction of recommendation keywords from the book recommendation information in the topic post includes: According to the preset segmentation dimensions, the book recommendation information of each topic post is segmented to obtain at least one initial keyword for each topic post under the preset segmentation dimensions; wherein, the preset segmentation dimensions include at least one of the following: a first segmentation dimension determined according to the relationships between characters in the book, a second segmentation dimension determined according to the personalities of characters in the book, and a third segmentation dimension determined according to the word class attribute; From the initial keywords, recommended keywords that match the target attribute features are selected.

3. The method according to claim 1, characterized in that, The target selection criteria are determined according to the following method: At least one first filter item is generated based on the recommended keywords, and at least one second filter item is generated based on the target attribute features; The target filter is determined based on the at least one first filter and the at least one second filter.

4. The method according to claim 3, characterized in that, Determining the target filter based on the at least one first filter and the at least one second filter includes: Determine the number of filter items corresponding to the target type based on the type of each of the second filter items; If the number of filter items is greater than the first preset number, the sorting order of each second filter item of the target type is determined according to the first number of books corresponding to each second filter item of the target type, in descending order of quantity. The second filter item whose sorting order is greater than the preset order, the at least one first filter item, and other second filter items other than the second filter item of the target type are used as the target filter item.

5. The method according to claim 3, characterized in that, The second filter includes a first sub-filter for characterizing the book type; The step of generating at least one second filtering item based on the target attribute features includes: Semantic recognition is performed on the book recommendation topics to determine the book search intent; the book search intent is used to indicate the attribute features of the books to be searched corresponding to the book recommendation topics; If the type of book search intent matches a first preset type, the reading type indication information corresponding to the user who triggered the book recommendation topic is obtained; the first preset type includes non-targeted type and / or type with a rating higher than a set threshold; the reading type indication information is used to indicate the types of books of interest. Based on the book types indicated by the target attribute features, determine the second number of first books under each of the book types of interest; Based on the type of book of interest whose corresponding second quantity is greater than the second preset quantity, at least one of the first sub-filter items is generated.

6. The method according to claim 3, characterized in that, The second filter option includes a second sub-filter option for characterizing the book's subject matter; The step of generating at least one second filtering item based on the target attribute features includes: If the type of book search intent matches the second preset type, determine the third number of topic posts in the book recommendation topics; The second preset type includes at least one preset book genre type; If the third quantity is greater than the third preset quantity, a fourth quantity of second books that match the type of book search intent is determined based on the book genres of each book indicated by the target attribute features. If the ratio between the fourth quantity and the total number of books corresponding to the book information is greater than a preset ratio, at least one second sub-filter item is generated according to the type of book search intent.

7. The method according to claim 3, characterized in that, The second filter includes a third sub-filter for characterizing the completion status of a book and / or a fourth sub-filter for characterizing the book's rating. The step of generating at least one second filtering item based on the target attribute features includes: Based on the book update status indicated by the target attribute features, if it is determined that there are books whose update status is completed, the third sub-filter item is generated; and / or If, based on the book ratings of the books indicated by the target attribute features, it is determined that there are books with a rating greater than a set threshold, the fourth sub-filter item is generated.

8. A display device, characterized in that, include: The acquisition module is used to respond to a trigger operation for any book recommendation topic, acquire multiple topic posts under the book recommendation topic, and target filter items matching the topic posts; wherein, the target filter items are determined based on recommendation keywords extracted from the book recommendation information of the topic posts, and target attribute features of the book information recommended in the topic posts, the target attribute features are determined based on the common information of each book corresponding to the book information, and the recommendation keywords are extracted using the target attribute features; The first display module is used to display the target filter item on the details page corresponding to the triggered book recommendation topic; The second display module is used to display a topic post associated with any of the target filter items in the details page in response to the selection of any of the target filter items.

9. A computer device, characterized in that, include: A processor and a memory, the memory storing machine-readable instructions executable by the processor, the processor executing the machine-readable instructions stored in the memory, wherein when the machine-readable instructions are executed by the processor, the processor performs the steps of the demonstration method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a computer device, performs the steps of the display method as described in any one of claims 1 to 7.

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