Task-based interaction method, electronic device, and storage medium

Abstract

The present invention discloses a task-based interaction method, electronic device, and storage medium. The interaction method includes: determining a candidate reading range for the user account based on historical reading data or tag information under the user account; generating task phrases based on the content information of the candidate reading range; publishing a search task for the task phrases under the user account; obtaining task submission information of the user under the user account during the reading process; and determining reward information for the user account based on the matching degree between the task submission information and the task phrases. The method can be applied to text content presentation applications such as e-book reading applications or news applications, and can improve the user activity and user traffic of the application, and commercially promote the increase in revenue of the application operator.

Description

Technical Field

[0001] The present disclosure relates to the field of the Internet, and in particular to a task-based interaction method, electronic device, and storage medium. Background Art

[0002] With the development of Internet technology, various types of applications exist to present content information to users, such as news applications that present information content to users, e-book reading applications that present book content to users, and video playback applications that present video content to users.

[0003] Currently, most free apps monetize their revenue by placing ads, increasing the app's user traffic, and thereby boosting ad exposure or click-through rates. Increasing user traffic can encompass increasing the number of users, extending user retention, and increasing user login frequency.

[0004] Because videos have a greater impact on people's senses, users tend to spend more time in video apps than in reading apps. As a result, user activity in reading apps is relatively low. In the reading field, relying solely on content information to attract users has limited effect on increasing user traffic. Summary of the Invention

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, embodiments of the present disclosure provide a task-based interaction method, an electronic device, and a storage medium.

[0006] In a first aspect, embodiments of the present disclosure provide a task-based interaction method. The interaction method includes: determining a candidate reading range for a user account based on historical reading data or tag information; generating a task phrase based on content information within the candidate reading range; publishing a search task for the task phrase under the user account; obtaining task submission information from users of the user account during the reading process; and determining reward information for the user account based on the degree of match between the task submission information and the task phrase.

[0007] In a second aspect, embodiments of the present disclosure provide an electronic device. The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; the memory is configured to store at least one executable instruction, which causes the processor to perform the following steps: determining a candidate reading range for the user account based on historical reading data or tag information under the user account; generating a task phrase based on content information of the candidate reading range; publishing a search task for the task phrase under the user account; obtaining task submission information from users under the user account during the reading process; and determining reward information for the user account based on the degree of match between the task submission information and the task phrase.

[0008] According to an embodiment of the present disclosure, in the task-based interaction method provided in the first aspect and the electronic device provided in the second aspect, task words and sentences are generated based on the content information of the candidate reading range, including: segmenting the content information in the candidate reading range to obtain word vectors; screening target words with practical meanings based on the semantics of the word vectors; counting the frequency of occurrence of the target words to obtain statistical results; and generating task words and sentences based on the statistical results.

[0009] According to an embodiment of the present disclosure, in the task-based interaction method provided in the first aspect and the electronic device provided in the second aspect, task sentences are generated based on the above statistical results, including: determining, based on the statistical results of the occurrence frequency of the above target words, a specific target word in the above target words whose occurrence frequency is in a preset frequency range as a pending word; and generating, based on the semantic information of the above pending words or sentences related to the above pending words, a task sentence that explicitly or implicitly contains the above pending words.

[0010] According to an embodiment of the present disclosure, in the task-based interaction method provided in the first aspect and the electronic device provided in the second aspect, based on the semantic information of the above-mentioned pending word or the sentence related to the above-mentioned pending word, a task phrase explicitly or implicitly containing the above-mentioned pending word is generated, including: a direct generation method, a first association generation method, a second association generation method and a third association generation method. The above-mentioned direct generation method is: using the above-mentioned pending word as the task phrase for search. The above-mentioned first association generation method is: using the semantic associated words of the above-mentioned pending word as the task phrase for search, and correspondingly limiting the search for the associated words of the above-mentioned semantic associated words in the search task for the above-mentioned task phrase, so that the user locates the above-mentioned pending word. Among them, the semantic associated words of the above-mentioned pending word include at least one of the following: synonyms, antonyms, hypernyms, similar meaning relationship words or general-specific relationship words of the above-mentioned pending word, and the above-mentioned associated words are: synonyms, antonyms, hyponyms, similar meaning relationship words or general-specific relationship words. The second association generation method is to use, based on the semantic information of the sentences related to the undetermined word, a descriptive sentence with specific semantics that explicitly contains the undetermined word as the task phrase for search. The third association generation method is to use, based on the semantic information of the sentences related to the undetermined word, a descriptive sentence with specific semantics that implicitly contains the undetermined word as the task phrase for search.

[0011] According to an embodiment of the present disclosure, the task-based interaction method provided in the first aspect also includes the following steps, or in the electronic device provided in the second aspect, the executable instructions also enable the processor to perform the following steps: determining the strategy information for generating task phrases based on the historical task execution information of the user account, the strategy information including the direct generation method, the first association generation method, the second association generation method and the third association generation method; wherein, when the historical task execution information indicates that the user uses a page search method to search for task phrases, the strategy information is determined to be at least one of the first association generation method and the third association generation method; when the historical task execution information indicates that the actual search time spent by the user is less than the preset time, the strategy information is determined to be at least one of the first association generation method, the second association generation method or the third association generation method; when the historical task execution information indicates that the actual search time spent by the user is greater than the maximum time threshold, the strategy information is determined to be the direct generation method.

[0012] According to an embodiment of the present disclosure, in the task-based interaction method provided in the first aspect and the electronic device provided in the second aspect, determining a candidate reading range for the user account based on historical reading data or tag information under the user account includes: determining whether the user account has historical reading data; when the user account has historical reading data, determining the candidate reading range for the user account based on the historical reading data under the user account. When the user account does not have historical reading data, determining the candidate reading range for the user account based on the user tag information corresponding to the user account, or determining the candidate reading range for the user account based on the content tag information selected by the user under the user account.

[0013] According to an embodiment of the present disclosure, in the task-based interaction method provided in the first aspect and the electronic device provided in the second aspect, a candidate reading range for the above-mentioned user account is determined based on the historical reading data under the above-mentioned user account, including: determining the status information of the read object based on the historical reading data under the above-mentioned user account; for a specific read object whose status information is in an unfinished reading state, determining at least part of the content information of the above-mentioned specific read object that is in an unread state as the candidate reading range for the above-mentioned user account.

[0014] According to an embodiment of the present disclosure, in the task-based interaction method provided in the first aspect and the electronic device provided in the second aspect, the candidate reading range for the above-mentioned user account is determined based on the historical reading data under the above-mentioned user account, including: determining the attribute information of the read object based on the historical reading data under the above-mentioned user account; based on the above-mentioned attribute information, determining the content information associated with the above-mentioned attribute information in the unread content library to be pushed as the candidate reading range for the above-mentioned user account.

[0015] According to an embodiment of the present disclosure, in the task-based interaction method provided in the first aspect and the electronic device provided in the second aspect, the candidate reading range for the above-mentioned user account is determined based on the user tag information corresponding to the above-mentioned user account, including: determining the object to be pushed that matches the above-mentioned user identity tag and / or user preference tag based on the user identity tag and / or user preference tag; and determining the above-mentioned object to be pushed as the candidate reading range for the above-mentioned user account.

[0016] According to an embodiment of the present disclosure, in the task-based interaction method provided in the first aspect and the electronic device provided in the second aspect, the candidate reading range for the above-mentioned user account is determined based on the content tag information selected by the user under the above-mentioned user account, including: based on the content tag information selected by the user under the above-mentioned user account, determining the object to be pushed that matches the above-mentioned content tag information; and determining the above-mentioned object to be pushed as the candidate reading range for the above-mentioned user account.

[0017] According to an embodiment of the present disclosure, the task-based interaction method provided in the first aspect and the electronic device provided in the second aspect, before obtaining the task submission information of the user under the above-mentioned user account during the reading process, also include the following steps: receiving a prompt request from the above-mentioned user account; determining whether the above-mentioned user account meets the prompt conditions in response to the above-mentioned prompt request; when the above-mentioned user account meets the prompt conditions, generating and displaying prompt information including the task target area according to the above-mentioned candidate reading range, the range of the above-mentioned task target area is less than or equal to the above-mentioned candidate reading range.

[0018] In a third aspect, embodiments of the present disclosure provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the task-based interaction method described above.

[0019] One or more technical solutions in the above embodiments have at least some or all of the following advantages:

[0020] By obtaining a candidate reading range exclusive to the user account based on the historical reading data or tag information of the user account (for example, a specific one or more e-books or a specific chapter of an e-book), generating task phrases based on the content information of the above candidate reading range, and publishing a search task for the task phrase under the user account, the generation of personalized tasks is realized. By obtaining rewards by completing tasks, the fun of user interaction with the application is enhanced, which helps to increase the user's interest in visiting the application. At the same time, since the generated search tasks are personalized for different user accounts, it can enhance each user's willingness to continue to perform tasks and the length of time they stay in the application, promote the increase in the number of users, the extension of user retention time, the improvement of user login frequency, etc., thereby improving the user activity and user traffic of the application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0022] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 The system architecture of the task-based interaction method applicable to the embodiment of the present disclosure is schematically shown;

[0024] Figure 2 The following schematically shows a flow chart of a task-based interaction method according to an embodiment of the present disclosure;

[0025] Figure 3 Schematically shows a detailed implementation flow chart of step S201 according to an embodiment of the present disclosure;

[0026] Figure 4 Schematically shows a detailed implementation flow chart of step S202 according to an embodiment of the present disclosure;

[0027] Figure 5 Schematically shows a detailed implementation flow chart of step S440 according to an embodiment of the present disclosure;

[0028] Figure 6 A flowchart schematically illustrates a task-based interaction method according to another embodiment of the present disclosure; and

[0029] Figure 7 The structural block diagram of the electronic device provided by the embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0030] An embodiment of the present disclosure provides a task-based interaction method, which includes: determining a candidate reading range for the user account based on historical reading data or tag information under the user account; generating task words and sentences based on content information of the candidate reading range; publishing a search task for the task words and sentences under the user account; obtaining task submission information of the user under the user account during the reading process; and determining reward information for the user account based on the matching degree between the task submission information and the task words and sentences.

[0031] The above-mentioned interaction method enhances the fun of user interaction with the application by rewarding users for completing tasks, which helps to increase users' interest in accessing the application. At the same time, because the generated search tasks are personalized for different user accounts, it can increase each user's willingness to continue to complete tasks in the application and the length of time they stay in the application, promote the increase in the number of users, the extension of user retention time, and the increase in user login frequency, thereby increasing the user activity and user traffic of the application, and commercially promoting the increase in revenue for the application operator. The above-mentioned method can be applied to text content presentation applications such as e-book reading applications or news applications.

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0033] Figure 1 The system architecture of the task-based interaction method applicable to the embodiment of the present disclosure is schematically shown.

[0034] Reference Figure 1 As shown, a system architecture 100 applicable to the task-based interaction method according to an embodiment of the present disclosure includes: terminal devices 101, 102, and 103, a network 104, and a server 105. The network 104 is a medium for providing a communication link between the terminal devices 101, 102, and 103 and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0035] Users can use terminal devices 101, 102, 103 to interact with server 105 via network 104 to receive or send messages, etc. Terminal devices 101, 102, 103 can be installed with text content presentation applications such as e-book reading applications and news client applications.

[0036] In addition, the terminal devices 101, 102, and 103 may further be installed with shopping applications, short video applications, web browser applications, search applications, instant messaging tools, food delivery applications, social platform software, etc. (for example only).

[0037] Terminal devices 101, 102, and 103 may be electronic devices with display screens that support text presentation, or they may be electronic devices with display screens that support web browsing. For example, the electronic devices may include, but are not limited to, smartphones, tablet computers, laptop computers, desktop computers, smart watches, and the like.

[0038] The server 105 may be a server that provides various services, such as a background management server (for example only) that provides service support for application interfaces or web pages browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process received requests and feed back processing results (e.g., web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0039] It should be noted that the task-based interaction method provided by the embodiments of the present disclosure can generally be executed by a terminal device with certain computing capabilities.

[0040] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0041] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0042] A first exemplary embodiment of the present disclosure provides a task-based interaction method.

[0043] Figure 2 The flowchart of the task-based interaction method according to an embodiment of the present disclosure is schematically shown.

[0044] Reference Figure 2 As shown, the task-based interaction method provided by the embodiment of the present disclosure includes the following steps: S201, S202, S203, S204 and S205. Steps S201 to S205 can be executed by a terminal device installed with an application, which can be an e-book reading application, a news client application, or other text content presentation application.

[0045] In step S201 , a candidate reading range for the user account is determined based on historical reading data or tag information under the user account.

[0046] In the case of an e-book reading application, the candidate reading range for the user account can be one or more specific e-books or a specific chapter of an e-book. In the case of a news client application, the candidate reading range for the user account can be a piece of news information, multiple sequentially pushed news information, or a specific paragraph within a piece of news information.

[0047] Users register with an account and password on a text content presentation application, becoming registered users of the content presentation application and accessing the content by logging in with their account. In some cases, a guest mode can also be set, allowing users to browse limited content without logging in. In one embodiment, after a user logs in to the text content presentation application using their user account and reads content, historical reading data corresponding to the user account will be generated.

[0048] It should be noted that there is no historical reading data under the user account corresponding to a newly registered user, or when the user has only registered but has not yet read books, news, etc., there is no historical reading data; or when the user accesses the application as a visitor (for example, the user has not logged into the account when browsing the news), the data generated is not counted as historical reading data under the user account.

[0049] The tag information under a user account is used to represent the characteristic information of the user corresponding to the user account, and can cover both user tags and content tags. For example, it may include user identity tags, user preference tags, content tags, etc.

[0050] Exemplarily, user identity tags may include but are not limited to: elementary school students, high school students, college students, graduate students, doctoral students, computer practitioners, teachers, scientific researchers, workers, etc. For news client applications, user preference tags may include but are not limited to: novels, videos, anti-epidemic, local, military, family, etc. For e-book reading applications, user preference tags may include but are not limited to: romance, martial arts, sweet pet, science fiction, etc. The above-mentioned content tags may be content tags of books or news. For example, content tags may be obtained by labeling from the following exemplary dimensions: book / news themes, book / news content keywords, book authors, book awards, writing techniques, news release time (latest, within a week, one month ago), etc.

[0051] In step S202, task words and sentences are generated based on the content information of the candidate reading range.

[0052] The task words and sentences can be the content (words or sentences) existing in the above-mentioned candidate reading range or the content related to the content existing in the above-mentioned candidate reading range (for example, superordinate words, synonyms, antonyms, similar meaning related words or general-specific related words, etc.).

[0053] The main words in the above task sentences can be names of people, places, objects, or other words with actual meanings, such as nouns, verbs, adjectives, numerals and quantifiers, but do not include function words, pronouns, etc.

[0054] For example, in one embodiment, the task phrase is the word "snowing" that appears in Chapters 3 to 5 of Book A. Alternatively, in another embodiment, the task phrase is a sentence in Book A that describes "snowing," such as the sentences "It snowed, the mountains were covered with a white coat, and the roofs were covered with a thick veil" and "I remembered a poem: Suddenly, as if a spring breeze came overnight, thousands of pear trees blossomed." Alternatively, in yet another embodiment, the words "depressed" and "disappointed" appear in Book B, and the task phrase is "happy."

[0055] In step S203, a search task for the task phrase is published under the user account.

[0056] For example, a search task for the task phrase "snow" could be: Find the word "snow." If the task phrase is: Sentences describing "snow" in Book A, the corresponding search task would be: Find two sentences describing "snow" in Book A or other books. A search task for the task phrase "happy" could be: Find two antonyms of "happy" in books (with or without a specific candidate reading range).

[0057] In step S204, the task submission information of the user under the above user account during the reading process is obtained.

[0058] There will be a task display area in the display interface of the text content presentation application. The tasks presented in the task display area include the above-mentioned search tasks, and may also include check-in tasks, reading tasks or other tasks.

[0059] After the user logs in to the application through the user account, the various tasks published can be seen in the task display area of the display interface. If the user wants to do the above-mentioned search task, for example, the task of searching for the word "snowing", the user can read the book information prompted in the task (for example, the task prompt is: you can search from Chapters 3 to 5 of Book A) or search the book by himself, and search for the word "snowing" in the book. When the user believes that the specific words and sentences he has found are the task words and sentences, he can underline the specific words and sentences he has found and click the task submission function key. At this time, it is considered that the application has received the task submission information of the user during the reading process.

[0060] In step S205, the reward information of the user account is determined based on the matching degree between the task submission information and the task phrase.

[0061] If the task submission information completely matches the task phrase, the user account's reward is determined to be the full reward. If the task submission information partially matches the task phrase, the user account's reward is determined to be a portion of the preset reward. For example, if the search task is to find two sentences describing "snowfall" and the preset reward is 100 tokens, if only one sentence in the task submission information describes snowfall and the other does not, the user account's reward is determined to be 50 tokens.

[0062] Based on the above steps S201 to S205, a candidate reading range exclusive to the user account is obtained based on the historical reading data or tag information of the user account, a task phrase is generated based on the content information of the above candidate reading range, and a search task for the task phrase is published under the user account, thereby realizing the generation of personalized tasks. By obtaining rewards by completing tasks, the fun of user interaction with the application is enhanced, which helps to increase the user's interest in visiting the application. At the same time, since the generated search tasks are personalized for different user accounts, it can enhance each user's willingness to continue to perform tasks and the length of time they stay in the application, promote the increase in the number of users, the extension of user retention time, the improvement of user login frequency, etc., thereby improving the user activity and user traffic of the application.

[0063] Figure 3 The detailed implementation flow chart of step S201 according to the embodiment of the present disclosure is schematically shown.

[0064] According to the embodiment of the present disclosure, referring to Figure 3 As shown, in the above step S201, task words and sentences are generated according to the content information of the above candidate reading range, including the following steps: S310, S321, S322 and S323.

[0065] When the result of S310 is “yes”, execute step S321 ; when the result of S310 is “no”, execute step S322 or S323 .

[0066] In step S310, it is determined whether the user account has any historical reading data.

[0067] In step S321 , when there is historical reading data for the user account, a candidate reading range for the user account is determined based on the historical reading data of the user account.

[0068] According to an embodiment of the present disclosure, in step S321, the candidate reading range for the above-mentioned user account is determined based on the historical reading data under the above-mentioned user account, including: determining the status information of the read object based on the historical reading data under the above-mentioned user account; for a specific read object whose status information is in an unfinished reading state, determining at least part of the content information of the above-mentioned specific read object that is in an unread state as the candidate reading range for the above-mentioned user account.

[0069] For example, based on the historical reading data under the user account, it can be determined that the read objects are book A and book S, and their respective status information is: the user is reading book A and has not finished reading it, and has finished reading book S; all or part of the remaining chapters in book A are used as candidate reading ranges.

[0070] According to another embodiment of the present disclosure, in step S321, the candidate reading range for the above-mentioned user account is determined based on the historical reading data under the above-mentioned user account, including: determining the attribute information of the read object based on the historical reading data under the above-mentioned user account; based on the above-mentioned attribute information, determining the content information associated with the above-mentioned attribute information in the unread content library to be pushed as the candidate reading range for the above-mentioned user account.

[0071] The aforementioned read object is, for example, an e-book that has been read. Attribute information includes: type (or genre), author, publisher, publication date, etc., for example, the type of book content (which can be a broad category, such as novels, or a subcategory, such as martial arts novels). Content information associated with the aforementioned attribute information is, for example, content information with the same type, author, publisher, publication date, etc.

[0072] In step S322 , when there is no historical reading data for the user account, a candidate reading range for the user account is determined based on the user tag information corresponding to the user account.

[0073] According to an embodiment of the present disclosure, in step S322, the candidate reading range for the above-mentioned user account is determined based on the user tag information corresponding to the above-mentioned user account, including: determining the object to be pushed that matches the above-mentioned user identity tag and / or user preference tag based on the user identity tag and / or user preference tag; and determining the above-mentioned object to be pushed as the candidate reading range for the above-mentioned user account.

[0074] In the scenario of e-book reading or news reading, the above-mentioned objects to be pushed may be one or more news contents / e-book contents, and multiple e-book contents may be: multiple books, or different chapters / paragraphs of a book, etc.

[0075] In the embodiment including step S322, a candidate reading range for generating task sentences is obtained based on user tag information.

[0076] In step S323, when there is no historical reading data for the above user account, a candidate reading range for the above user account is determined according to the content tag information selected by the user under the above user account.

[0077] According to an embodiment of the present disclosure, in step S323, determining a candidate reading range for the above user account according to the content tag information selected by the user under the above user account includes: determining a push target object that matches the above content tag information according to the content tag information selected by the user under the above user account; and determining the above push target object as the candidate reading range for the above user account.

[0078] In the embodiment including step S323, in the function interface of the application, a content tag selection list or selection box is provided for the user. When the user selects a specific content tag, for example, the martial arts tag, then one or more martial arts books (an example of the push target object, or it can also be one or more news items) will be determined according to the selected martial arts tag by the user as the candidate reading range. Since there is no historical reading data for the above user account, the push target object is naturally regarded as a book / news object that the user has not read yet.

[0079] Figure 4 Schematically shows a detailed implementation flowchart of step S202 according to an embodiment of the present disclosure.

[0080] According to an embodiment of the present disclosure, referring to Figure 4 As shown, in the above step S202, generating task sentences according to the content information of the above candidate reading range includes the following steps: S410, S420, S430, and S440.

[0081] In step S410, the content information in the above candidate reading range is segmented to obtain word vectors.

[0082] In step S420, according to the semantics of the word vectors, target words with actual meanings are screened.

[0083] The above target words with actual meanings include nouns (such as personal names, object names, place names, etc.), verbs, adjectives, numerals, and quantifiers, etc., and do not include function words (such as "de", "di", "de", "he", "not only", "but also", etc.), pronouns (such as "he", "she", "it", "they"), etc.

[0084] In step S430, the occurrence frequencies of the above target words are counted to obtain a statistical result.

[0085] It can be obtained how many target words there are in the candidate reading range and how many times each target word appears.

[0086] In step S440, task words and sentences are generated based on the above statistical results.

[0087] In one embodiment, when there are multiple task words, the multiple task words are scattered in at least two different books / chapter / paragraph / line to ensure that the user's search time exceeds the preset time, which is, for example, the minimum reading time to generate revenue (for example, 30 seconds).

[0088] Figure 5 The detailed implementation flow chart of step S440 according to the embodiment of the present disclosure is schematically shown.

[0089] According to the embodiment of the present disclosure, referring to Figure 5 As shown, in the above step S440, task words and sentences are generated according to the above statistical results, including the following steps: S510 and S520.

[0090] In step S510, based on the statistical result of the occurrence frequency of the target words, a specific target word with an occurrence frequency within a preset frequency range among the target words is determined as a pending word.

[0091] In one embodiment, the preset frequency interval is a medium frequency interval, for example, 3 to 5 times. This setting effectively ensures that the user can find the pending word and the estimated average time taken is at least the preset time. The estimated average time taken can be, for example, a test of how long it takes volunteers to perform and complete a task, and the estimated average time taken by volunteers to find the pending word can be calculated based on the test results.

[0092] In step S520, a task sentence explicitly or implicitly containing the pending word is generated based on the semantic information of the pending word or a sentence related to the pending word.

[0093] According to an embodiment of the present disclosure, based on the semantic information of the above-mentioned undetermined words or sentences related to the above-mentioned undetermined words, task sentences that explicitly or implicitly contain the above-mentioned undetermined words are generated, including the following four parallel execution schemes: (a) direct generation method, (b) first association generation method, (c) second association generation method and (d) third association generation method.

[0094] The above-mentioned direct generation method (a) is to use the above-mentioned undetermined word as the task phrase for search. In this embodiment, the task phrase explicitly includes the above-mentioned undetermined word.

[0095] The above-mentioned (b) first association generation method is: using the semantic associated words of the above-mentioned undetermined words as the task words for search, and limiting the search for the associated words of the above-mentioned semantic associated words in the search task of the above-mentioned task words, so that the user can locate the above-mentioned undetermined words.

[0096] Among them, the semantic associated words of the above-mentioned undetermined words include at least one of the following: synonyms, antonyms, hypernyms, similar meaning related words or general-specific related words of the above-mentioned undetermined words, and the above-mentioned associated vocabulary is: synonyms, antonyms, hyponyms, similar meaning related words or general-specific related words.

[0097] In this implementation mode, the task phrase implicitly includes the above-mentioned undetermined words.

[0098] The second association generation method (c) mentioned above is: based on the semantic information of the sentence related to the above-mentioned undetermined word, a descriptive sentence with specific semantics that explicitly contains the above-mentioned undetermined word is used as the task sentence for search.

[0099] The third association generation method (d) is as follows: based on the semantic information of the sentence related to the undetermined word, a descriptive sentence with specific semantics that implicitly contains the undetermined word is used as the task sentence for search.

[0100] In this implementation scenario, the corresponding search task is, for example: sentences that do not contain "snow" but describe "snowing", such as the sentence "Suddenly, a spring breeze came overnight, and thousands of pear trees blossomed" in the book A in the above example, which is the task phrase to be searched.

[0101] In some scenarios, for published tasks, in reading applications that support full-text text search, users can complete tasks by searching for text in the search box of the reading software. In this case, in order to ensure that the user meets the minimum requirement for the length of stay, a logic is set up to determine which specific solution to execute in the above step S520 based on the historical task execution information of the user account.

[0102] According to an embodiment of the present disclosure, the above-mentioned step S520 specifically includes: determining the strategy information for generating task phrases based on the historical task execution information of the above-mentioned user account, and the above-mentioned strategy information includes the above-mentioned direct generation method, the first association generation method, the second association generation method and the third association generation method.

[0103] Among them, when the above-mentioned historical task execution information indicates that the user uses a page search method to search for task terms, the above-mentioned strategy information is determined to be at least one of the first association generation method or the third association generation method; when the above-mentioned historical task execution information indicates that the actual search time spent by the user is less than the preset time, the above-mentioned strategy information is determined to be at least one of the first association generation method, the second association generation method or the third association generation method; when the above-mentioned historical task execution information indicates that the actual search time spent by the user is greater than the maximum time threshold, the above-mentioned strategy information is determined to be a direct generation method.

[0104] Figure 6 The flowchart of the task-based interaction method according to another embodiment of the present disclosure is schematically shown.

[0105] According to another embodiment of the present disclosure, referring to Figure 6 As shown, the task-based interaction method provided by the embodiment of the present disclosure includes, in addition to the above steps S201 to S205, the following steps: S601, S602, and S603. Steps S601 to S603 are performed before step S204. In one embodiment, the above steps S601 to S603 can be performed after step S203. In this case, after publishing the search task, the user can submit a prompt request.

[0106] In step S601, a prompt request for the user account is received.

[0107] In step S602, in response to the prompt request, it is determined whether the user account meets the prompt condition.

[0108] For example, the method that meets the prompt conditions can be free or paid. For example, the scenario of the free method is: users can get free prompts by signing in, for example, signing in for 3 consecutive days to get a chance to get a free prompt. For example, the scenario of the paid method is: users get the opportunity to get free prompts by paying 10 tokens in advance. The above tokens can correspond to the tokens converted from the actual recharge amount, or the tokens obtained by the user by maintaining continuous login, maintaining online time, maintaining reading volume, etc. The value of the prepaid prompt here (for example, 10 tokens) is much smaller than the reward value for completing the task (for example, 100 to 500 tokens, etc.).

[0109] In step S603, when the user account meets the prompt condition, prompt information including the task target area is generated and displayed according to the candidate reading range, and the range of the task target area is smaller than or equal to the candidate reading range.

[0110] Furthermore, the above prompt information can be subdivided, for example, the prompt levels can be divided according to different payment amounts. The higher the payment amount, the smaller the task target area in the corresponding prompt information, which is more conducive to improving the user's task completion rate.

[0111] The various technical solutions provided by the embodiments of the present disclosure can be implemented in hardware in whole or in part, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the electronic device according to the embodiment of the present disclosure. The embodiments of the present disclosure can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. The program implementing the embodiments of the present disclosure can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0112] A second exemplary embodiment of the present disclosure provides an electronic device.

[0113] Figure 7 The structural block diagram of the electronic device provided by the embodiment of the present disclosure is schematically shown.

[0114] Reference Figure 7 As shown, the electronic device 700 provided by the embodiment of the present disclosure includes a processor 701, a communication interface 702, a memory 703 and a communication bus 704, wherein the processor 701, the communication interface 702 and the memory 703 communicate with each other through the communication bus 704; the memory 703 is used to store at least one executable instruction; the processor 701 is used to implement the task-based interaction method as described above when executing the executable instruction stored in the memory.

[0115] Specifically, the executable instructions cause the processor to perform the following steps: determining a candidate reading range for the user account based on historical reading data or tag information under the user account; generating a task phrase based on the content information of the candidate reading range; publishing a search task for the task phrase under the user account; obtaining task submission information from the user under the user account during the reading process; and determining reward information for the user account based on the degree of match between the task submission information and the task phrase. The detailed implementation process of each of the above steps or other steps that may be further included can be referred to the description of the first embodiment and will not be repeated here.

[0116] According to an embodiment of the present disclosure, the electronic device may be a terminal device installed with a text content presentation application. In an exemplary scenario, for example, the text content presentation application is one or more of the following software: e-reading software, news client software, etc.

[0117] The above-mentioned memory 703 can be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk or ROM. The memory 703 has storage space for executable instructions for executing any method step in the above-mentioned method. For example, the storage space for executable instructions may include individual executable instructions for implementing each step in the above-mentioned method. These executable instructions can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. Such computer program products are typically portable or fixed storage units. The storage unit may have storage segments or storage spaces arranged similarly to the memory 703 in the above-mentioned electronic device. The executable instructions can be compressed, for example, in an appropriate form. Generally, the storage unit includes executable instructions for executing the method operation steps according to the embodiments of the present disclosure, that is, program codes that can be read by a processor such as 701, and when these program codes are executed by an electronic device, the electronic device performs the various steps in the method described above.

[0118] A third exemplary embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the task-based interaction method described above.

[0119] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments, or may exist independently without being incorporated into the device / apparatus. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present disclosure.

[0120] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0121] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or step from another entity or step, and do not necessarily require or imply any actual relationship or order between these entities or steps. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0122] The foregoing description is intended only to provide specific embodiments of the present disclosure, intended to enable those skilled in the art to understand and implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure is not to be limited to the embodiments shown herein, but is to be construed in the broadest manner consistent with the principles and novel features claimed herein.

Claims

1. A task-based interaction method, characterized in that: include: Determining a candidate reading range for the user account based on historical reading data or tag information under the user account; Generating task phrases based on the content information of the candidate reading range includes: determining the statistical results of the occurrence frequency of target words with actual meaning within the candidate reading range; determining, based on the statistical results, specific target words in the target words whose occurrence frequency is within a preset frequency range as pending words; determining, based on the historical task execution information of the user account, strategy information for generating task phrases and generating task phrases based on the strategy information; the strategy information is used to generate task sentences that explicitly or implicitly contain the pending words through differentiated generation methods, so that the user meets the minimum stay time requirement; Posting a search task for the task phrase under the user account; Obtaining task submission information of the user under the user account during the reading process; The reward information of the user account is determined according to the matching degree between the task submission information and the task phrase.

2. The interactive method according to claim 1, characterized in that The statistical result of determining the occurrence frequency of target words with actual meaning within the candidate reading range includes: Segmenting the content information in the candidate reading range to obtain word vectors; Based on the semantics of word vectors, select target words with practical meanings; The occurrence frequency of the target vocabulary is counted to obtain a statistical result.

3. The interactive method according to claim 1, characterized in that: The strategy information includes at least one of the following methods: direct generation method: using the pending word as a task word for search; or, The first association generation method: using the semantic associated words of the pending word as the task phrase for search, and limiting the search for the associated words of the semantic associated words in the search task of the task phrase, so that the user can locate the pending word; wherein, the semantic associated words of the pending word include at least one of the following: synonyms, antonyms, hypernyms, similar meaning relationship words or general-specific relationship words of the pending word, and the associated words are: synonyms, antonyms, hyponyms, similar meaning relationship words or general-specific relationship words; or, The second association generation method: based on the semantic information of the sentence related to the undetermined word, a descriptive sentence with specific semantics that explicitly contains the undetermined word is used as the task sentence for search; or The third association generation method: based on the semantic information of the sentence related to the undetermined word, the descriptive sentence with specific semantics that implicitly contains the undetermined word is used as the task sentence for search.

4. The interactive method according to claim 3, characterized in that: Also includes: When the historical task execution information indicates that the user uses a page search method to find the task phrase, determining that the strategy information is at least one of the first association generation method and the third association generation method; When the historical task execution information indicates that the actual search time spent by the user is less than the preset time, determining that the strategy information is at least one of the first association generation method, the second association generation method, or the third association generation method; When the historical task execution information indicates that the search time actually spent by the user is greater than the maximum time threshold, the strategy information is determined to be the direct generation mode.

5. The interactive method according to claim 1, characterized in that: The step of determining a candidate reading range for the user account based on historical reading data or tag information under the user account includes: Determine whether there is historical reading data for the user account; When the user account has historical reading data, determining a candidate reading range for the user account based on the historical reading data under the user account; When the user account does not have historical reading data, the candidate reading range for the user account is determined based on the user tag information corresponding to the user account, or based on the content tag information selected by the user under the user account.

6. The interactive method according to claim 5, characterized in that: The determining of a candidate reading range for the user account based on historical reading data under the user account includes: Determining the status information of the read object based on the historical reading data under the user account; For a specific read object whose status information is in a reading unfinished state, at least a portion of the content information in the specific read object that is in an unread state is determined as a candidate reading range for the user account.

7. The interactive method according to claim 5, characterized in that: The determining of a candidate reading range for the user account based on historical reading data under the user account includes: Determining attribute information of the read object based on the historical reading data under the user account; According to the attribute information, content information associated with the attribute information in the unread content library to be pushed is determined as a candidate reading range for the user account.

8. The interactive method according to claim 5, characterized in that: The determining of a candidate reading range for the user account according to the user tag information corresponding to the user account includes: Determining, based on the user identity tag and / or the user preference tag, an object to be pushed that matches the user identity tag and / or the user preference tag; The object to be pushed is determined as a candidate reading range for the user account.

9. The interactive method according to claim 5, characterized in that: The determining of a candidate reading range for the user account based on the content tag information selected by the user under the user account includes: Determining, based on the content tag information selected by the user under the user account, an object to be pushed that matches the content tag information; The object to be pushed is determined as a candidate reading range for the user account.

10. The interactive method according to claim 1, characterized in that: Before obtaining the task submission information of the user under the user account during the reading process, the method further includes: receiving a prompt request for the user account; In response to the prompt request, determining whether the user account meets a prompt condition; When the user account meets the prompt condition, prompt information including a task target area is generated and displayed according to the candidate reading range, and the range of the task target area is smaller than or equal to the candidate reading range.

11. An electronic device, characterized in that: The processor, the communication interface, the memory and the communication bus are connected to each other via the communication bus. The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform the following steps: Determining a candidate reading range for the user account based on historical reading data or tag information under the user account; Generating task phrases based on the content information of the candidate reading range includes: determining the statistical results of the occurrence frequency of target words with actual meaning within the candidate reading range; determining, based on the statistical results, specific target words in the target words whose occurrence frequency is within a preset frequency range as pending words; determining, based on the historical task execution information of the user account, strategy information for generating task phrases and generating task phrases based on the strategy information; the strategy information is used to generate task sentences that explicitly or implicitly contain the pending words through differentiated generation methods, so that the user meets the minimum stay time requirement; Posting a search task for the task phrase under the user account; Obtaining task submission information of the user under the user account during the reading process; The reward information of the user account is determined according to the matching degree between the task submission information and the task phrase.

12. The electronic device according to claim 11, wherein: The statistical result of determining the occurrence frequency of target words with actual meaning within the candidate reading range includes: Segmenting the content information in the candidate reading range to obtain word vectors; Based on the semantics of word vectors, select target words with practical meanings; The occurrence frequency of the target vocabulary is counted to obtain a statistical result.

13. The electronic device according to claim 11, wherein: The strategy information includes at least one of the following methods: direct generation method: using the pending word as a task word for search; or, The first association generation method: using the semantic associated words of the pending word as the task phrase for search, and limiting the search for the associated words of the semantic associated words in the search task of the task phrase, so that the user can locate the pending word; wherein, the semantic associated words of the pending word include at least one of the following: synonyms, antonyms, hypernyms, similar meaning relationship words or general-specific relationship words of the pending word, and the associated words are: synonyms, antonyms, hyponyms, similar meaning relationship words or general-specific relationship words; or, The second association generation method: based on the semantic information of the sentence related to the undetermined word, a descriptive sentence with specific semantics that explicitly contains the undetermined word is used as the task sentence for search; or The third association generation method: based on the semantic information of the sentence related to the undetermined word, the descriptive sentence with specific semantics that implicitly contains the undetermined word is used as the task sentence for search.

14. The electronic device according to claim 13, wherein: The executable instructions further cause the processor to perform the following steps: When the historical task execution information indicates that the user uses a page search method to find the task phrase, determining that the strategy information is at least one of the first association generation method and the third association generation method; When the historical task execution information indicates that the actual search time spent by the user is less than the preset time, determining that the strategy information is at least one of the first association generation method, the second association generation method, or the third association generation method; When the historical task execution information indicates that the search time actually spent by the user is greater than the maximum time threshold, the strategy information is determined to be the direct generation mode.

15. The electronic device according to claim 11, wherein The step of determining a candidate reading range for the user account based on historical reading data or tag information under the user account includes: Determine whether there is historical reading data for the user account; When the user account has historical reading data, determining a candidate reading range for the user account based on the historical reading data under the user account; When the user account does not have historical reading data, the candidate reading range for the user account is determined based on the user tag information corresponding to the user account, or based on the content tag information selected by the user under the user account.

16. The electronic device according to claim 15, characterized in that The determining of a candidate reading range for the user account based on historical reading data under the user account includes: Determining the status information of the read object based on the historical reading data under the user account; For a specific read object whose status information is in a reading unfinished state, at least a portion of the content information in the specific read object that is in an unread state is determined as a candidate reading range for the user account.

17. The electronic device according to claim 15, characterized in that The determining of a candidate reading range for the user account based on historical reading data under the user account includes: Determining attribute information of the read object based on the historical reading data under the user account; According to the attribute information, content information associated with the attribute information in the unread content library to be pushed is determined as a candidate reading range for the user account.

18. The electronic device according to claim 15, characterized in that The determining of a candidate reading range for the user account according to the user tag information corresponding to the user account includes: Determining, based on the user identity tag and / or the user preference tag, an object to be pushed that matches the user identity tag and / or the user preference tag; The object to be pushed is determined as a candidate reading range for the user account.

19. The electronic device according to claim 15, wherein: The determining of a candidate reading range for the user account based on the content tag information selected by the user under the user account includes: Determining, based on the content tag information selected by the user under the user account, an object to be pushed that matches the content tag information; The object to be pushed is determined as a candidate reading range for the user account.

20. The electronic device according to claim 11, wherein Before obtaining the task submission information of the user under the user account during the reading process, the executable instructions further cause the processor to execute the following steps: receiving a prompt request for the user account; In response to the prompt request, determining whether the user account meets a prompt condition; When the user account meets the prompt condition, prompt information including a task target area is generated and displayed according to the candidate reading range, and the range of the task target area is smaller than or equal to the candidate reading range.

21. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the interactive method according to any one of claims 1 to 10 is implemented.

Images