Online reading recommendation method, system, device and computer-readable storage medium

Abstract

The present application provides an online reading recommendation method, system, device and computer-readable storage medium. The method includes: obtaining the browsing history data of the reader after the current login, the browsing history data including the browsing time, operation times and page views of each reading material; calculating the reader's preference value for the reading material based on the browsing time, operation times and page views; selecting the common tags of the reading materials whose preference values ​​are greater than the preset preference values ​​as recommendation tags; storing the reading materials containing the recommendation tags in the reading material tags into the recommendation catalog and recommending them to the reader. Since the calculation factors of the operation times and page views are added in the process of calculating the preference value, the calculation process of the preference value refers to the browsing actions of the reader after the current login, thereby improving the accuracy of the calculation of the reader's interest and the accuracy of the reading material recommendation.

Description

Technical Field

[0001] The present application relates to the technical field of online reading, and in particular to an online reading recommendation method, system, device and computer-readable storage medium. Background Art

[0002] With the development of society and the improvement of per capita cultural level, reading has become more and more popular and has attracted people's attention and welcome. In order to facilitate people's reading, reading software came into being.

[0003] In order to improve people's reading experience, general reading software will add the function of reading recommendation to assist readers in quickly finding their favorite reading materials. For example, for those preparing for IELTS, they need to be able to recommend reading materials that will help them prepare for the IELTS test, and for those taking accounting exams, they need to be able to recommend reading materials that will help them prepare for the accounting exam.

[0004] However, general reading software makes recommendations based on the tags selected by the reader when logging into the software and the preset tags of each reading material. This recommendation method is too broad and not precise enough, which makes the reading materials recommended to readers by the reading software very different from the reading materials they really need. Summary of the invention

[0005] In order to provide readers with accurate reading material recommendations, the present application provides an online reading recommendation method, system, device and computer-readable storage medium.

[0006] The first object of this application is to provide an online reading recommendation method, the online reading recommendation method comprising:

[0007] Obtaining the browsing history data of the reader after the current login, wherein the browsing history data includes the browsing time, operation times and page views of each reading material;

[0008] Calculating the reader's preference value for the reading material according to the browsing time, the number of operations and the browsing volume;

[0009] Selecting the common tags of the readings whose desirable values ​​are greater than the preset desirable values ​​as recommended tags;

[0010] The reading materials whose reading material tags contain the recommendation tag are stored in a recommendation catalog and recommended to the reader.

[0011] By adopting the above technical solution, when the reader logs in to the software and browses the reading material, the reader's favorite reading material label can be calculated based on the browsing history data after the current login, thereby realizing accurate recommendation of the reading material. Since the calculation factors of the number of operations and the amount of browsing are added in the process of calculating the favorite value, the calculation process of the favorite value refers to the browsing action of the reader after the current login. If the reader has a large number of operations on the reading material and a long browsing time, it means that the reader has a great interest in the reading material, thereby improving the accuracy of the calculation of the reader's interest and the accuracy of the reading material recommendation.

[0012] As an improvement to the solution of the present invention, the number of operations is the number of operations performed by the reader on the introduction page of the book.

[0013] By adopting the above technical solution, general reading software will have an introduction page corresponding to each reading material, which includes various information about the reading material, such as page views, evaluations, introductions, etc. If readers browse the book introduction page many times, it proves that the readers are very interested in the relevant content of the book introduction page, thereby providing an accurate data basis for calculating the reader's preference value.

[0014] As an improvement to the solution of the present invention, the number of operations is the number of operations the reader performs on the book introduction section on the book introduction page, and the book introduction section satisfies: after the book introduction section is clicked a preset number of times, the book introduction content of the book introduction section is displayed.

[0015] By adopting the above technical solution, the book introduction section of the book introduction page includes a brief introduction to the content of the book. Since all the contents of the book introduction section can be browsed only after the book introduction section is clicked a sufficient number of times, readers cannot browse the entire book introduction content with a single click or without any operation, but can only read more content gradually as the number of clicks increases. The more times the reader clicks on the book introduction section, the more interested the reader is in the introduction content of the book, thereby providing an accurate data basis for the calculation of the desired value.

[0016] As an improvement to the solution of the present invention, the desirability value is weighted according to the reading material label selected by the reader, and the common label of the reading material whose desirability value after weighted calculation is greater than the preset desirability value is selected as the recommended label.

[0017] By adopting the above technical solution, after readers register or log in to the software, they will select the tags of the reading materials they are interested in. The tags selected by readers can be used as additional calculation factors for readers' reading needs to perform weighted calculation on the desired values, so that the recommended reading materials can better meet the readers' interests or needs.

[0018] The second purpose of this application is to provide an online reading recommendation system, including:

[0019] A data acquisition module is used to acquire the browsing history data of the reader after the current login, wherein the browsing history data includes the browsing time, operation times and browsing amount of each reading material;

[0020] A data calculation module, used for calculating the reader's preference value for the reading material according to the browsing time, the number of operations and the browsing amount;

[0021] The storage recommendation module is used to store the reading materials with the recommendation tags in the reading material tags into the recommendation catalog and recommend them to the readers.

[0022] As an improvement to the solution of the present invention, the number of operations is the number of operations performed by the reader on the book introduction page acquired by the data acquisition module.

[0023] As an improvement of the solution of the present invention, the number of operations is the number of operations of the reader on the book introduction section on the book introduction page acquired by the data acquisition module, and the book introduction section satisfies: after clicking the book introduction section a preset number of times, the book introduction content of the book introduction section is displayed.

[0024] As an improvement of the solution of the present invention, the data calculation module performs weighted calculation on the desired value according to the reading material label selected by the reader;

[0025] The storage recommendation module selects the common tags of the readings whose desirability values ​​after weighted calculation are greater than the preset desirability values ​​as the recommended tags.

[0026] The third object of this application is to provide an online reading recommendation device, comprising:

[0027] at least one processor and at least one memory;

[0028] The memory stores instructions;

[0029] The processor retrieves the instruction from the memory and executes the online reading recommendation method.

[0030] The fourth objective of the present application is to provide a computer-readable storage medium, characterized in that it includes a program, and when the program is run, the above-mentioned online reading recommendation method is executed.

[0031] In summary, the present application includes at least one of the following beneficial technical effects:

[0032] 1. Since the calculation of the desire value takes into account the number of operations and views, the desire value can accurately reflect the degree of interest of readers and provide a data basis for the precise push of reading materials.

[0033] 2. Since the reading introduction content of the reading introduction section is displayed after the preset number of clicks on the reading introduction section, the number of operations can better reflect the reader's interest in the reading material, further improving the accuracy of reading material push. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart of the online reading recommendation method in this application.

[0035] Figure 2 This is a schematic diagram of the reading introduction page in this application.

[0036] Figure 3 It is a system diagram of the online reading recommendation system in this application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0039] In order to facilitate the understanding of this solution, the following is a brief description of the conventional setting method of the reading software. Reading software generally adds tags to readings, such as fantasy, history, biography, etc. After the reader registers an account or logs in, the reading tag can be selected or the selected reading tag can be changed. At the same time, in the process of readers browsing different readings through the reading software, the interface presented to the reader by the reading software generally includes four types, namely, a tag list, a reading list, a reading introduction page, and a reading interface. The reader can enter the reading list corresponding to the tag by clicking on different tags in the tag list, and then click on the reading in the reading list to enter the reading introduction page of the corresponding reading. In the reading introduction page, the reader can browse the introduction of the reading, and the introduction content can include relevant information such as the number of views, evaluation, content introduction, author, update time, word count, etc. to introduce the reading, and the reader can read the specific content of the reading in the reading interface. This embodiment is to accurately push readings to the reader during the operation process after the current login after the reader logs in to the reading software. The following is a further detailed description of the embodiment of the present application in conjunction with the accompanying drawings of the specification.

[0040] like Figure 1 As shown, the embodiment of the present application provides an online reading recommendation method, and the main process of the method is described as follows:

[0041] Step S101: Obtain the browsing history data of the reader after the current login, wherein the browsing history data includes the browsing time, operation times and browsing amount of each reading material.

[0042] After logging into the reading software, readers generally browse different software interfaces according to their own intentions. The browsing time, number of operations, and pageviews described in this embodiment are all the operation data of the reader on a specific reading list or reading. For example, when the reader is browsing the reading software interface, after selecting a tag, he will enter the reading list with a specific tag. For example, if the selected reading tag is biography, then the reading list with the tag biography will be entered. Then, the browsing time, number of operations, and number of browsed readings during the browsing of the reading list are the reader's partial browsing history data after the current login; for example, when the reader is browsing the reading introduction page, the time spent on the reading introduction page is the browsing time, the number of clicks or drags of the reader on the reading introduction page is the number of operations, and the amount of reading introduction section content read by the reader on the reading introduction page is the pageviews; for example, after the reader enters the reading interface of a certain reading, the time the reader reads, the number of clicks or drags, and the reading amount are the corresponding browsing time, number of operations, and pageviews. This embodiment does not impose a sole limitation on the browsing history data described, but any browsing history data indicating the reader's browsing of a specific tag or a specific reading material is the browsing history data described in this embodiment.

[0043] Reference Figure 1 and Figure 2 In another example, this embodiment discloses a layout method for a reading material introduction page. It should be noted that the reading software disclosed in this embodiment can be a mobile phone APP, or an application software installed on a PC, or an application software installed on an IPAD, or software installed in an electronic reader. This embodiment does not limit the installation location and application environment of the reading software, but all of them should include the reading material introduction page section content described in this embodiment. At the same time, this embodiment only introduces the section display method that must be included in the reading material introduction page, and does not limit the size, position and shape of the section. The following only takes the reading material introduction page on the mobile phone APP as an example for introduction.

[0044] The book introduction page is provided with a book introduction section and a book review section. The book introduction section is provided with a content introduction area and a content click virtual button. The content of the content introduction area is set to display different book introduction contents according to the number of clicks on the content click virtual button. After the content click virtual button is clicked N times, N is a natural number, and the content introduction area displays the entire content of the book introduction. The entire content of the book introduction can be a book introduction, or a book introduction and author information and other text display information related to the book. The book review section is provided with a comment introduction area and a comment click virtual button. The comment introduction area is used to display one or more comment information. The comment information displayed in the comment introduction area is set to display different comment information according to the number of clicks on the introduction virtual button.

[0045] In this embodiment, the number of operations includes the number of clicks on the content click virtual button and the number of clicks on the introduction click virtual button.

[0046] Step S102: Calculate the reader's preference value for the reading material based on the browsing time, operation times and browsing volume. The reading material in this step is a specific reading material, and the specific calculation method of the preference value is as follows:

[0047] S=a1*t1+a2*t2+a3*t3...+an*tn+b1*p1+b2*p2+b3*p3+...+bm*pm+c1*x1+c2*x2+c3*x3...+cz*xz;

[0048] Among them, S is the desired value, n, m, z are all natural numbers, t1, t2, t3...tn are the numerical values ​​of different browsing times related to the specific reading material, a1, a2, a3...an are the weighted cardinal numbers of t1, t2, t3...tn respectively, p1, p2, p3...pm are the numerical values ​​of different operation times related to the specific reading material, b1, b2, b3...bm are the weighted cardinal numbers of p1, p2, p3...pm respectively, x1, x2, x3...xz are the numerical values ​​of different browsing amounts related to the specific reading material, c1, c2, c3...cz are the weighted cardinal numbers of x1, x2, x3...xz respectively.

[0049] The preference value indicates the preference degree of the reference reader for a certain book during the browsing process of the current login process.

[0050] Step S103: select the common tags of the reading materials whose desired values ​​are greater than the preset desired values ​​as recommended tags. In the reading software, each reading material will be given multiple tags, such as hot-blooded, fantasy, brain-burning and other different types of tags. After calculating the desired value of each reading material browsed by the reader after the current login, select the reading materials whose desired values ​​are greater than the preset desired values, calculate their common tags, and select the common tags as recommended tags.

[0051] It should be noted that the shared label described in this step can be a label shared by all readings with a desired value greater than a preset desired value, or a label shared by some readings with a desired value greater than a preset desired value, and is not limited to this.

[0052] Step S104: Store the readings with the recommendation tag in the reading tags into the recommendation catalog and recommend them to the reader. The recommendation to the reader described in this step includes recommendation during the current login browsing process or recommendation during the subsequent login browsing process. The way of recommendation to the reader has different expressions depending on the type of reading software. For example, the way to recommend to the reader in the mobile phone APP is to add a recommendation section to the tag list, and each time the reader enters the tag list, a number of readings will be randomly displayed in the recommendation section; for example, the way to recommend to the reader in the PC terminal is to add a floating window to each browsing interface, and randomly display a number of readings through the floating window.

[0053] From the above, it can be seen that when the reader browses the reading material after logging into the software, the reader's favorite reading material tags can be calculated based on the browsing history data after the current login, thereby realizing accurate recommendation of the reading material. Since the calculation factors of the number of operations and the amount of views are added in the process of calculating the favorite value, the calculation process of the favorite value refers to the browsing actions of the reader after the current login. If the reader has a large number of operations on the reading material, a long browsing time, and a large amount of views, it means that the reader has a great interest in the reading material, thereby improving the accuracy of the calculation of the reader's interest and the accuracy of the reading material recommendation.

[0054] In another example, since readers will select or change some of the selected reading material tags when initially logging into the reading software or in the process of logging into the reading software later, the reading material tags selected by the readers represent the initial needs of the readers when logging into the software. Therefore, in the process of selecting recommended tags, the preference values ​​are weighted according to the reading material tags selected by the readers, and the common tags of the reading materials whose preference values ​​after weighted calculation are greater than the preset preference values ​​are selected as recommended tags. The weighted values ​​in the process of weighted calculation of the preference values ​​are preset fixed values, and the preset weighted values ​​of different selected tags can be the same or different.

[0055] Reference Figure 3 , the present application also discloses an online reading recommendation system, including a data acquisition module, a data calculation module and a storage recommendation module, wherein:

[0056] A data acquisition module is used to acquire the browsing history data of the reader after the current login, wherein the browsing history data includes the browsing time, operation times and browsing amount of each reading material;

[0057] A data calculation module, used for calculating the reader's preference value for the reading material according to the browsing time, the number of operations and the browsing amount;

[0058] The storage recommendation module is used to select the common tags of the reading materials whose desirability values ​​are greater than the preset desirability values ​​as recommendation tags, store the reading materials whose tags contain the recommendation tags in the recommendation catalog and recommend them to the readers.

[0059] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system and module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0060] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation, for example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0061] It should also be understood that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0062] The embodiment of the present application also discloses an online reading recommendation device, which is mainly composed of one or more memories and one or more processors:

[0063] The memory is used to store instructions;

[0064] The processor is used to call and run instructions from the memory to execute the online reading recommendation method as described above.

[0065] It can be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories.

[0066] The non-volatile memory may be a ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory.

[0067] The volatile memory may be a RAM, which is used as an external cache. There are many different types of RAM, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct memory bus RAM.

[0068] The processor mentioned in any of the above places can be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the above-mentioned feedback information transmission method. The processing unit and the storage unit can be decoupled and respectively set on different physical devices, and connected by wire or wireless means to realize the respective functions of the processing unit and the storage unit to support the system chip to realize the various functions in the above-mentioned embodiments. Alternatively, the processing unit and the memory can also be coupled on the same device.

[0069] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a computer-readable storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned computer-readable storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0070] The embodiments of this specific implementation method are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Therefore, all equivalent changes made based on the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An online reading recommendation method, characterized in that: Obtaining the browsing history data of the reader after the current login, wherein the browsing history data includes the browsing time, operation times and page views of each reading material; Calculating the reader's preference value for the reading material according to the browsing time, the number of operations and the browsing volume; Selecting the common tags of the readings whose desired values ​​are greater than the preset desired values ​​as recommended tags; storing the readings with the recommendation tag in the reading tags into the recommendation catalog and recommending them to the readers; The number of operations refers to the number of operations performed by the reader on the book introduction page; The operation count is the number of operations performed by the reader on the book introduction section on the book introduction page, and the book introduction section satisfies: after the book introduction section is clicked a preset number of times, the book introduction content of the book introduction section is displayed; The selecting the common tags of the readings whose desired values ​​are greater than the preset desired values ​​as recommended tags includes: The desirability values ​​are weighted according to the reading material labels selected by the reader, and the common labels of the reading materials whose desirability values ​​after weighted calculation are greater than the preset desirability values ​​are selected as recommended labels; The step of storing the reading material having the recommendation tag in the reading material tag into the recommendation catalog and recommending it to the reader comprises: Recommendations are made during the current logged-in browsing process or during the subsequent logged-in browsing process. The way of recommendation to readers varies depending on the type of reading software. In mobile APP, the way of recommendation to readers is to add a recommendation section to the tag list, and each time the reader enters the tag list, several reading materials will be randomly displayed in the recommendation section; in PC, the way of recommendation to readers is to add a floating window to each browsing interface, and randomly display several reading materials through the floating window.

2. An online reading recommendation system, characterized in that: include: A data acquisition module is used to acquire the browsing history data of the reader after the current login, wherein the browsing history data includes the browsing time, operation times and browsing amount of each reading material; A data calculation module, used for calculating the reader's preference value for the reading material according to the browsing time, the number of operations and the browsing amount; A storage recommendation module, configured to select the common tags of the reading materials whose desired values ​​are greater than a preset desired value as recommendation tags, store the reading materials containing the recommendation tags in a recommendation catalog, and recommend them to the reader; The number of operations refers to the number of operations performed by the reader on the book introduction page; The operation count is the number of operations performed by the reader on the book introduction section on the book introduction page, and the book introduction section satisfies: after the book introduction section is clicked a preset number of times, the book introduction content of the book introduction section is displayed; The selecting the common tags of the readings whose desired values ​​are greater than the preset desired values ​​as recommended tags includes: The desirability values ​​are weighted according to the reading material labels selected by the reader, and the common labels of the reading materials whose desirability values ​​after weighted calculation are greater than the preset desirability values ​​are selected as recommended labels; The step of storing the reading material having the recommendation tag in the reading material tag into the recommendation catalog and recommending it to the reader comprises: Recommendations are made during the current logged-in browsing process or during the subsequent logged-in browsing process. The way of recommendation to readers varies depending on the type of reading software. In mobile APP, the way of recommendation to readers is to add a recommendation section to the tag list, and each time the reader enters the tag list, several reading materials will be randomly displayed in the recommendation section; in PC, the way of recommendation to readers is to add a floating window to each browsing interface, and randomly display several reading materials through the floating window.

3. An online reading recommendation device, characterized in that: include: at least one processor and at least one memory; The memory stores instructions; The processor retrieves the instruction from the memory and executes the online reading recommendation method according to claim 1.

4. A computer-readable storage medium, characterized in that: The invention comprises a program, and when the program is run, the online reading recommendation method as claimed in claim 1 is executed.

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