Book recommendation method, server, system and storage medium

Abstract

The present disclosure relates to a book recommendation method, apparatus, device, system and medium. Among them, the book recommendation method includes: receiving a book recommendation request sent by a client, the book recommendation request includes the target book type to be recommended; in response to the book recommendation request, querying a first book list corresponding to the target book type in the first recent recommendation result, the first recent recommendation result includes a recommended book list generated within a first preset time period, and the first book list is a recommended book list generated for the client; if the first book list is found in the first recent recommendation result, a first book list is generated according to the first book list; and the first book list is sent to the client. According to the embodiment of the present disclosure, it can meet the needs of personalized book recommendations for different users, and meet the needs of users to quickly view the book recommendation list, thereby improving the user experience.

Description

Technical Field

[0001] The present disclosure relates to the field of Internet technology, and in particular to a book recommendation method, apparatus, device, system and medium. Background Art

[0002] With the continuous development and popularization of mobile terminal devices and electronic reading devices, e-books are becoming more and more popular among readers due to their convenience. In e-book platforms, there is generally a book recommendation function, which allows users to quickly find books of interest by browsing the books recommended by the e-book platform.

[0003] However, currently, every time a user uses the book recommendation function of an e-book platform, he or she needs to wait for a long time before seeing the books recommended by the e-book platform, which reduces the user experience. Summary of the Invention

[0004] In order to solve the above technical problems, the present disclosure provides a book recommendation method, apparatus, device, system and medium.

[0005] In a first aspect, the present disclosure provides a book recommendation method, comprising:

[0006] Receive a book recommendation request sent by a client, where the book recommendation request includes a target book type to be recommended;

[0007] In response to the book recommendation request, searching for a first book list corresponding to the target book type in a first recent recommendation result, the first recent recommendation result including a recommended book list generated within a first preset time period, the first book list being a recommended book list generated for the client;

[0008] If a first book list is found in the first recent recommendation result, a first book list is generated according to the first book list;

[0009] Send the first book list to the client.

[0010] In a second aspect, the present disclosure provides a server, including a processor and a memory, wherein the memory is configured to store executable instructions, and the executable instructions enable the processor to perform the following operations:

[0011] Receive a book recommendation request sent by a client, where the book recommendation request includes a target book type to be recommended;

[0012] In response to the book recommendation request, searching for a first book list corresponding to the target book type in a first recent recommendation result, the first recent recommendation result including a recommended book list generated within a first preset time period, the first book list being a recommended book list generated for the client;

[0013] If a first book list is found in the first recent recommendation result, a first book list is generated according to the first book list;

[0014] Send the first book list to the client.

[0015] In a third aspect, the present disclosure provides a book recommendation system, including an interaction node, a first query node, and a list generation node, wherein:

[0016] The interactive node is used to receive a book recommendation request sent by a client and send the first book list generated by the list generation node to the client, wherein the book recommendation request includes the target book type to be recommended;

[0017] The first query node is configured to query a first book list corresponding to a target book type in a first recent recommendation result in response to a book recommendation request, wherein the first recent recommendation result includes a recommended book list generated within a first preset time period, and the first book list is a recommended book list generated for the client;

[0018] The list generation node is used to generate a first book list based on the first book list if a first book list is found in the first recent recommendation result.

[0019] In a fourth aspect, the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor implements the book recommendation method of the first aspect.

[0020] The technical solution provided by the embodiments of the present disclosure has the following advantages over the prior art:

[0021] The book recommendation method, apparatus, device, system and medium of the disclosed embodiments can, after receiving a book recommendation request sent by a client, query a first book list corresponding to the target book type included in the book recommendation request in the first recent recommendation result; if the first book list is found in the first recent recommendation result, generate a first book list based on the first book list and send the first book list to the client; since the first recent recommendation result may include a recommended book list generated within a first preset time period and the first book list may be a recommended book list generated for the client, when the user uses the book recommendation function, the books in the recommended book list generated for the user within the first preset time period can be directly recommended to the user, which can meet the needs of personalized book recommendations for different users and save the time of generating book recommendation lists, so that when the user uses the book recommendation function of the e-book platform, they can see the books recommended for them according to their reading needs without waiting for a long time, which meets the needs of users to quickly view the book recommendation list and improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0023] Figure 1 A flowchart of a book recommendation method provided in an embodiment of the present disclosure;

[0024] Figure 2 A flowchart of a method for generating a book list provided in an embodiment of the present disclosure;

[0025] Figure 3 A flowchart of another book recommendation method provided by an embodiment of the present disclosure;

[0026] Figure 4 A flowchart of another book recommendation method provided in an embodiment of the present disclosure;

[0027] Figure 5 A schematic diagram of the structure of a book recommendation system provided by an embodiment of the present disclosure;

[0028] Figure 6 A schematic diagram of the structure of another book recommendation system provided by an embodiment of the present disclosure;

[0029] Figure 7 A schematic diagram of the structure of another book recommendation system provided by an embodiment of the present disclosure;

[0030] Figure 8 A structural diagram of another book recommendation system provided by an embodiment of the present disclosure;

[0031] Figure 9 A schematic diagram of the structure of a server provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0033] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0034] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.

[0035] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0036] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0037] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0038] The embodiments of the present disclosure provide a book recommendation method, server, system, and storage medium that can meet the needs of personalized book recommendations for different users and the needs of users to quickly view book recommendation lists.

[0039] First, combine Figures 1-4 The book recommendation method provided by the embodiment of the present disclosure is described.

[0040] In the embodiment of the present disclosure, the book recommendation method may be executed by a book recommendation system, wherein the book recommendation system may be composed of one server or multiple servers.

[0041] Figure 1 A flow chart of a book recommendation method provided by an embodiment of the present disclosure is shown.

[0042] like Figure 1 As shown, the book recommendation method may include the following steps.

[0043] S110: Receive a book recommendation request sent by a client, where the book recommendation request includes a target book type to be recommended.

[0044] In an embodiment of the present disclosure, when a user uses the book recommendation function of the e-book platform through a client, the user may initiate a book recommendation request to the book recommendation system through the client, and the book recommendation system may receive the book recommendation request sent by the client.

[0045] The client may be a website platform or an application of an e-book platform, and may be installed in an electronic device, which may include but is not limited to a mobile terminal device and an electronic reading device.

[0046] For example, the client may be an “XX e-book application (Application, APP)” installed in a mobile phone.

[0047] Furthermore, the book recommendation request may include a target book type to be recommended, wherein the target book type may be a book type to which the user wishes the e-book platform to recommend a book list.

[0048] Specifically, the client can determine the target book type that the user wants the e-book platform to recommend to him based on the specified list, specified channel page or bookstore page that the user wants to browse.

[0049] In some embodiments, when a user wants to browse a specified list or a specified channel page, if the specified list or the specified channel page involves multiple book types, the target book type may include all the book types it involves; if the specified list or the specified channel page involves one book type, the target book type may include one book type it involves.

[0050] In other embodiments, when a user wants to browse the bookstore page, the target book type may include all book types in the e-book platform.

[0051] In an embodiment of the present disclosure, optionally, a book recommendation request may include any one of the following: a recommendation request generated when a user requests a book recommendation for a target book type for the first time within a first preset time period; a recommendation request generated when a user requests a book recommendation for a target book type for a non-first time within the first preset time period.

[0052] The first preset time period may be a time period of a first preset duration starting from the book recommendation request initiated by the user through the client. The first preset duration may be preset as needed and is not limited here.

[0053] For example, the first preset duration may be 8 hours, and the first preset time period may be within 8 hours before the user initiates the book recommendation request through the client this time.

[0054] In some embodiments, if the book recommendation request is the first time a user requests a book recommendation for a target book type within a first preset time period, the book recommendation request may be a book recommendation request generated by the client based on a designated list control displayed in the client clicked by the user.

[0055] For example, the homepage of an e-book app may display a "Youth Campus Ranking" control. A user may click on the "Youth Campus Ranking" control to cause the client to generate a book recommendation request for a target book type belonging to the "Youth Campus Ranking" list. In this case, the target book type may be a youth campus book type.

[0056] For example, a "Must-Read List" control can be displayed on the e-book homepage of an XX e-book app. The user can click on the "Must-Read List" control to cause the client to generate a book recommendation request corresponding to the target book type to which the "Must-Read List" belongs. In this case, the target book type can include all book types on the e-book platform.

[0057] In other embodiments, if the book recommendation request is a recommendation request generated by the user's first request for a recommended book of the target book type within a first preset time period, the book recommendation request may also be a book recommendation request generated by the client based on the user triggering the client to display a designated channel page.

[0058] For example, a "Youth Campus" channel entry may be displayed in the bottom sidebar or the top sidebar of the XX e-book app. The user may click on the "Youth Campus" channel entry to cause the client to generate a book recommendation request corresponding to the target book type belonging to the "Youth Campus" channel. In this case, the target book type may be a youth campus book type.

[0059] In some other embodiments, if the book recommendation request is the first time a user requests a book recommendation for a target book type within a first preset time period, the book recommendation request may also be a book recommendation request generated by the client based on the bookstore page displayed by the client triggered by the user.

[0060] For example, a "Bookstore" page entrance may be displayed in the bottom sidebar of the XX e-book APP page. The user can click on the "Bookstore" entrance to enable the client to generate a book recommendation request corresponding to the target book type involved in the bookstore page. In this case, the target book type may include all book types in the e-book platform.

[0061] In some further embodiments, if the book recommendation request is not the first time that the user requests a book recommendation for the target book type within the first preset time period, then the book recommendation request can be a book recommendation request generated by the above-mentioned client based on the specified list control displayed in the client clicked by the user, or a specified channel page displayed by the client triggered by the user, or a book store page displayed by the client triggered by the user. The book recommendation request can also be a book recommendation request generated by the client when the client has displayed the book list corresponding to the target book type and receives an operation in which the user triggers the client to display more book list contents corresponding to the target book type. There is no limitation here.

[0062] For example, a "More" control can be displayed on the "Youth Campus Ranking" page of an XX e-book app. The user can click the "More" control to cause the client to generate a book recommendation request corresponding to the target book type of the "Youth Campus Ranking" list, and then display more list content of the "Youth Campus Ranking" list to the user. In this case, the target book type can be youth campus books.

[0063] S120. In response to the book recommendation request, query a first book list corresponding to the target book type in a first recent recommendation result, where the first recent recommendation result includes a recommended book list generated within a first preset time period, and the first book list is a recommended book list generated for the client.

[0064] In an embodiment of the present disclosure, when the book recommendation system receives a book recommendation request with a target book type to be recommended, it can respond to the book recommendation request and query a first book list corresponding to the target book type in a first recent recommendation result.

[0065] The first recent recommendation result may include at least one recommended book list generated for different clients within a first preset time period. Each recommended book list may be stored in association with identification information and book type information. Specifically, the identification information may be used to determine the client for which the recommended book list is targeted, and the book type information may be used to determine the book type to which the recommended book list belongs.

[0066] Optionally, the book recommendation request may further include target identification information, and the target identification information may include at least one of the following: user identification, client identification.

[0067] Specifically, the book recommendation system can obtain the target book type and target identification information from the book recommendation request, and query in the first recent recommendation result whether there is a recommended book list whose associated stored book type information is the target book type and whose associated stored identification information is the target identification information. The recommended book list is the first book list.

[0068] For example, if user A has requested the book recommendation system to recommend a list of books of the youth campus book type within 8 hours before initiating the book recommendation request corresponding to the youth campus book type through the client, the book recommendation system can store the recommended book list used to generate the book list of the youth campus book type for user A in the first recent recommendation result, and associate the recommended book list with the identification information corresponding to the client used by user A and the book type information corresponding to the youth campus book type. At this time, after receiving the book recommendation request, the book recommendation system can query the recommended book list of the youth campus book type generated for user A within 8 hours in the first recent recommendation result. Otherwise, if user A has not requested the book recommendation system to recommend a list of books of the youth campus book type within 8 hours before initiating the book recommendation request corresponding to the youth campus book type through the client, the recommended book list of the youth campus book type generated for user A within 8 hours cannot be queried in the first recent recommendation result.

[0069] It should be noted that if a book list that a user wants to browse involves multiple target book types, the book type information is all the target book types involved in the book list; if a book list that a user wants to browse involves one target book type, the book type information is one target book type involved in the book list.

[0070] S130: If a first book list is found in the first recent recommendation result, a first book list is generated according to the first book list.

[0071] In an embodiment of the present disclosure, after the book recommendation system queries the first book list corresponding to the target book type generated for the client in the first recent recommendation result, it can generate a first book list for display to the user based on the obtained first book list. The detailed steps will be explained later.

[0072] S140: Send the first book list to the client.

[0073] In an embodiment of the present disclosure, after the book recommendation system generates a first book list based on the first recommended book list, the generated first book list can be sent to the client, so that the client can display the first book list for the user to browse and select books of interest in the first book list for reading.

[0074] In an embodiment of the present disclosure, after receiving a book recommendation request sent by a client, a first book list corresponding to the target book type included in the book recommendation request can be queried in the first recent recommendation result. If the first book list is found in the first recent recommendation result, a first book list is generated based on the first book list, and the first book list is sent to the client. Since the first recent recommendation result may include a recommended book list generated within a first preset time period and the first book list may be a recommended book list generated for the client, when the user uses the book recommendation function, the books in the recommended book list generated for the user within the first preset time period can be directly recommended to the user, which can meet the needs of personalized book recommendations for different users and save the time of generating book recommendation lists. When using the book recommendation function of the e-book platform, users do not need to wait for a long time to see the books recommended for them according to their reading needs, which meets the needs of users to quickly view the book recommendation list and improves the user experience.

[0075] In one embodiment of the present disclosure, after the book recommendation system queries the first book list, it can quickly generate a first book list based on the first book list to reduce the user's waiting time. Figure 2 Provide detailed explanation.

[0076] Figure 2 A flow chart of a method for generating a book list provided in an embodiment of the present disclosure is shown.

[0077] like Figure 2 As shown, the book recommendation method may include the following steps.

[0078] S210 , starting from a target book in the first book list, selecting a preset number of consecutive first books to be recommended in the first book list, where the target book is the first book that has not been recommended within a first preset time period.

[0079] In an embodiment of the present disclosure, after the book recommendation system queries the first book list corresponding to the target book type in the first recent result, it can select a preset number of consecutive books to be recommended from the queried first book list starting from the target book, that is, the first book that has not been recommended within the first preset time period. These selected books to be recommended are the first books to be recommended.

[0080] In some embodiments, after the book recommendation system generates a book list based on the first book list each time within a first preset time period, the book recommendation system can mark the books used to generate the book list in the first book list, and mark these books as recommended books, so that after the book recommendation system queries the first book list corresponding to the target book type, it can generate a first book list based on books that have not been recommended in the first book list, thereby avoiding repeatedly recommending the same books to users.

[0081] In these embodiments, the target book may further be the first book that has not been marked as a recommended book.

[0082] For example, the first book list includes 300 books. If within 8 hours, the first 100 books in the first book list have been used to generate a book list, that is, these books have been recommended to users, then these 100 books are marked as recommended books. In this case, the target book can be the 101st book in the first book list.

[0083] In other embodiments, after the book recommendation system generates a book list based on the first book list each time within a first preset time period, the book recommendation system can delete the books in the first book list used to generate the book list. The remaining books in the first book list are books that have not been recommended, so that the book recommendation system can generate a first book list based on books that have not been recommended in the first book list after querying the first book list corresponding to the target book type, thereby avoiding recommending the same books to users repeatedly.

[0084] In these embodiments, the target book may further be the first remaining book in the first book list.

[0085] In the embodiment of the present disclosure, the preset number may be a preset maximum number of books that can be displayed on each screen of the book list, for example, 10 books, which is not limited here.

[0086] Optionally, a one-screen book list refers to the list content displayed by the electronic device each time the user requests a recommended book (first time or not first time).

[0087] Continuing with the example that the first book list includes 300 books and the target book is the 101st book in the first book list, if the preset number is 10, the book recommendation system can take the 101st to 110th books as the first books to be recommended.

[0088] S220: Generate a first book list based on the selected first book to be recommended.

[0089] In an embodiment of the present disclosure, after the book recommendation system selects a preset number of consecutive first books to be recommended from the first book list, it can generate a first book list based on the selected first books to be recommended.

[0090] Specifically, after the book recommendation system obtains the first book to be recommended, it can further sort the first book to be recommended according to, for example, the popularity of the book, and then use the sorted order to generate a first book list based on the first book to be recommended. It can also not sort, and directly generate a first book list based on the first book to be recommended according to the order of the books in the first book list, and send the generated first book list to the client so that the client can display the first book list.

[0091] Therefore, in the embodiment of the present disclosure, the book recommendation system can generate a first book list based on books that have not been recommended in the first book list, which can not only remove duplicate recommended books, but also improve the efficiency of generating book lists and further enhance user experience.

[0092] In some other embodiments of the present disclosure, before S220, the book recommendation method may further include: obtaining historical behavior data of a user corresponding to the client; and obtaining a second to-be-recommended book corresponding to the target book type based on the historical behavior data of the user.

[0093] In some embodiments, before generating the first book list according to the first book list, the book recommendation system further obtains historical user behavior data corresponding to the client, and obtains a second to-be-recommended book corresponding to the target book type according to the historical user behavior data.

[0094] Optionally, the user historical behavior data corresponding to the client may include the user's click behavior data, download behavior data, payment behavior data, etc.

[0095] Specifically, multiple user historical behavior data can be pre-stored in the book recommendation system, and each user historical behavior data can be stored in association with an identification information. The book recommendation system can query whether there is user historical behavior data with the associated stored identification information as the target identification information in the multiple user historical behavior data. If user historical behavior data with the associated stored identification information as the target identification information is queried, the second book to be recommended corresponding to the target book type is obtained based on the queried user historical behavior data, and then the first book list and the second book to be recommended are generated based on the first book to be recommended. Otherwise, the first book list is directly generated based on the first book to be recommended.

[0096] Furthermore, the book recommendation system can recall books based on the retrieved user historical behavior data to obtain multiple second books to be recommended.

[0097] Specifically, the book recommendation system can retrieve books that are most similar to books related to the user's historical behavior data and use these retrieved books as the second books to be recommended. For example, if the client's corresponding user historical behavior data includes the user's download behavior data, the book recommendation system can determine the books the user has downloaded or recently downloaded based on the user's download behavior data, and then search for several books that are most similar to the book, and use these books as the second books to be recommended.

[0098] In the embodiment of the present disclosure, S220 may further include: generating a first book list according to the selected first book to be recommended and the second book to be recommended.

[0099] In some embodiments, the book recommendation system may first roughly sort the plurality of second books to be recommended, and then combine the obtained first books to be recommended with the roughly sorted second books to be recommended to generate a first book list. The rough sorting may include sorting by book popularity, which is not limited here.

[0100] In other embodiments, the book recommendation system may further perform mixed sorting on the first and second books to be recommended to obtain a first book list. Mixed sorting may include sorting by book popularity, which is not limited here.

[0101] Therefore, in the embodiment of the present disclosure, the book recommendation system can generate a first book list for the user from multiple dimensions, so that the recommended first book list is more likely to include books that the user is interested in, further improving the user experience.

[0102] In another embodiment of the present disclosure, in order to further shorten the user's waiting time, after the book recommendation system queries the first book list corresponding to the target book type in the first recent recommendation result, if the book recommendation system does not query the first book list in the first recent recommendation result, it can generate a book list for the user based on the second recent recommendation result. Figure 3 Provide explanation.

[0103] Figure 3 A flow chart of another book recommendation method provided by an embodiment of the present disclosure is shown.

[0104] like Figure 3 As shown, the book recommendation method may include the following steps.

[0105] S310: Receive a book recommendation request sent by a client, where the book recommendation request includes a target book type to be recommended.

[0106] S320: In response to the book recommendation request, query a first book list corresponding to the target book type in a first recent recommendation result, where the first recent recommendation result includes a recommended book list generated within a first preset time period, and the first book list is a recommended book list generated for the client.

[0107] S330: If a first book list is found in the first recent recommendation result, a first book list is generated according to the first book list.

[0108] S340: Send the first book list to the client.

[0109] It should be noted that S310-S340 and Figure 1 S110 - S140 in the illustrated embodiment are similar and are not described in detail here.

[0110] S350. If the first book list is not found in the first recent recommendation result, a second book list corresponding to the target book type is searched in the second recent recommendation result, where the second recent recommendation result includes a recommended book list generated within a second preset time period. The second book list is a recommended book list generated for the client, and the second preset time period includes the first preset time.

[0111] In an embodiment of the present disclosure, after the book recommendation system queries the first book list corresponding to the target book type in the first recent recommendation result, if the first book list is not found in the first recent recommendation result, the book recommendation system can continue to respond to the book recommendation request and query the second book list corresponding to the target book type in the second recent recommendation result.

[0112] Optionally, similar to the first recent recommendation result, the second recent recommendation result may include at least one recommended book list generated for different clients within a second preset time period. Each recommended book list may be stored in association with an identification information and a book type information, which will not be repeated here.

[0113] The second preset time may be a time period of a second preset duration starting from the book recommendation request initiated by the user through the client. The second preset duration may be preset as needed and is not limited here.

[0114] Furthermore, the second preset duration may be greater than the first preset duration, so that the second preset time period includes the first preset time.

[0115] For example, the first preset time period can be 8 hours and the second preset time period can be 90 days. The first preset time period can be within 8 hours before the user initiates the book recommendation request through the client this time, and the second preset time period can be within 90 days before the user initiates the book recommendation request through the client this time.

[0116] Specifically, the book recommendation system can query in the second recent recommendation result whether there is a recommended book list whose associated stored book type information is the target book type in the book recommendation request and whose associated stored identification information is the target identification information in the book recommendation request. The recommended book list is the second book list.

[0117] For example, if user A has requested the book recommendation system to recommend a list of books in the youth campus book category within 90 days before initiating a book recommendation request corresponding to the youth campus book category through the client, the book recommendation system can store a recommended book list for user A used to generate a list of books in the youth campus book category in the second most recent recommendation result, and associate the recommended book list with the identification information corresponding to the client used by user A and the book category information corresponding to the youth campus book category. In this case, after receiving the book recommendation request, if the book recommendation system does not find a recommended book list of the youth campus book category generated for user A within 8 hours in the first most recent recommendation result, it can find a recommended book list of the youth campus book category generated for user A within 90 days in the second most recent recommendation result. Otherwise, if user A has not requested the book recommendation system to recommend a list of books in the youth campus book category within 90 days before initiating a book recommendation request corresponding to the youth campus book category through the client, it will not be able to find a recommended book list of the youth campus book category generated for user A within 90 days in the second most recent recommendation result.

[0118] S360: If a second book list is found in the second recent recommendation result, a second book list is generated according to the second book list.

[0119] In an embodiment of the present disclosure, after the book recommendation system queries the second book list corresponding to the target book type generated for the client in the second recent recommendation results, a second book list for display to the user can be generated based on the obtained second book list. The detailed steps are similar to step S130 and will not be repeated here.

[0120] S370: Send the second book list to the client.

[0121] In an embodiment of the present disclosure, after the book recommendation system generates a second book list based on the second recommended book list, the generated second book list can be sent to the client, so that the client can display the second book list for the user to browse and select books of interest in the second book list for reading.

[0122] In the embodiment of the present disclosure, even if the book recommendation system does not find recommended books generated for the client in the most recent time period, it can quickly generate a book list based on recommended books generated for the client in a slightly longer time period, further shortening the user's waiting time and improving the user experience.

[0123] In another embodiment of the present disclosure, in order to further shorten the user's waiting time, after the book recommendation system fails to find the second book list in the second recent recommendation result, if the second book list is not found in the second recent recommendation result, the book list can also be cold started.

[0124] Optionally, after querying the second book list corresponding to the target book type in the second recent recommendation result, the book recommendation method may further include: if the second book list is not found in the second recent recommendation result, obtaining a popular book list corresponding to the target book type; generating a third book list based on the popular book list; and sending the third book list to the client.

[0125] In an embodiment of the present disclosure, if the book recommendation system does not find the second book list in the second recent recommendation result, it will obtain a popular book list from a popular database corresponding to the target book type.

[0126] In some embodiments, if the book recommendation system determines that the book recommendation request does not include user historical reading data and the user historical reading data corresponding to the target identification information in the book recommendation request is not found, the book recommendation system can determine that the user using the client is a new user or the user has not used the client on a new electronic device. At this time, the book recommendation system can directly obtain a preset number of books in the corresponding popular database of the target book type to generate a popular book list.

[0127] Optionally, the popular database corresponding to the target book type may include multiple popular books generated within a third preset time, and the third preset time period may be a time period of a third preset length starting from the user's book recommendation request initiated through the client this time, wherein the third preset time length can be pre-set as needed and is not limited here.

[0128] For example, the third preset duration may be 2 days, and the third preset time period may be the 2 days before the user initiates the book recommendation request through the client. If the target book type is youth campus books, the popular database corresponding to the youth campus book type stores popular books of the youth campus book type in the past 2 days.

[0129] In other embodiments, if the book recommendation system determines that the book recommendation request includes user historical reading data or queries the user historical reading data corresponding to the target identification information in the book recommendation request, the book recommendation system can determine that the user using the client is an old user. At this time, the book recommendation system can obtain a preset number of books that are most similar to the historical reading books involved in the user's historical reading data in the corresponding popular database of the target book type, and generate a popular book list.

[0130] Furthermore, after obtaining the popular book list, the book recommendation system may generate a third book list based on the order of the books in the popular book list.

[0131] Furthermore, after generating the third book list, the book recommendation system can send the third book list to the client, so that the client can display the third book list for the user to browse and select books of interest in the third book list to read.

[0132] In the disclosed embodiment, regardless of whether the user is a new or old user of the e-book platform, a list of popular books corresponding to the target data type can be quickly recommended to the user, further shortening the user's waiting time and improving the user experience.

[0133] In some further embodiments of the present disclosure, after the book list is cold-started, in order to further shorten the waiting time for the user to browse the book list of the same book type next time, the book recommendation system can also generate a spare target book type target recommended book list for the client. Figure 4 Provide explanation.

[0134] Figure 4 A flow chart of another book recommendation method provided by an embodiment of the present disclosure is shown.

[0135] like Figure 4 As shown, the book recommendation method may include the following steps.

[0136] S410: Generate a target recommended book list corresponding to the target book type for the client based on the user's historical reading data.

[0137] In an embodiment of the present disclosure, after the book recommendation system searches for a second book list corresponding to the target book type in the second recent recommendation result, if the second book list is not found, the book recommendation system will also generate a target recommended book list corresponding to the target book type for the client based on the user's historical reading data.

[0138] In some embodiments, the book recommendation request may include user historical reading data. The book recommendation system may directly obtain the user historical reading data in the book recommendation request and generate a target recommended book list corresponding to the target book type for the client based on the user historical reading data.

[0139] In other embodiments, the book recommendation request may not include the user's historical reading data. The book recommendation system may query the user's historical reading data corresponding to the target identification information based on the target identification information in the book recommendation request. If the user's historical reading data is found, a target recommended book list corresponding to the target book type is generated for the client based on the user's historical reading data.

[0140] S420: Cache the target recommended book list in the first recent recommendation result and the second recent recommendation result.

[0141] In an embodiment of the present disclosure, after the book recommendation system generates a target recommended book list corresponding to the target book type for the client based on the user's historical reading data, the book recommendation system can cache the generated target recommended book list in the first recent recommendation result and the second recent recommendation result respectively, and cache the first recent recommendation result for a first preset time length and cache the second recent recommendation result for a second preset time length, so that the book recommendation system can query the corresponding book list according to the first recent recommendation result when receiving a new recommendation request of the same book type within the first preset time length, and can query the corresponding book list according to the second recent recommendation result when receiving a new recommendation request of the same book type within the second preset time length.

[0142] In the embodiment of the present disclosure, optionally, S410 may specifically include: obtaining multiple groups of candidate books corresponding to the target book type based on the user's historical reading data; sorting the multiple groups of candidate books respectively to obtain multiple groups of sorted candidate books; and combining the multiple groups of sorted candidate books according to the recall order of the multiple groups of sorted candidate books to obtain a target recommended book list.

[0143] Specifically, the book recommendation system can obtain multiple groups of candidate books corresponding to the target book type from multiple groups of preset books corresponding to the target book type based on the user's historical reading data, that is, obtain a group of candidate books from each group of preset books.

[0144] Optionally, the user's historical reading data may include the user's historical browsing data, the user's attention data, the user's comment data, etc. The historical viewing data may be the user's browsing record of a certain book, the user's attention data may be the user's collection record of a certain book, and the user's comment data may be the user's comment record on a certain book.

[0145] Furthermore, after the book recommendation system obtains multiple groups of candidate books corresponding to the target book type, it will sort each group of candidate books separately to obtain multiple groups of sorted candidate books.

[0146] Optionally, the sorting rule may be, for example, book popularity, etc., which is not limited here.

[0147] Furthermore, after the book recommendation system obtains multiple groups of ranked candidate books, it can combine the multiple groups of ranked candidate books in series according to their recall order to obtain a target recommended book list.

[0148] In some embodiments, the book recommendation system may combine multiple groups of ranked candidate books based solely on their recall order to obtain a target recommended book list.

[0149] For example, after the book recommendation system serially recalls multiple groups of candidate books, and after each recall of a group of candidate books, the group of candidate books is sorted to obtain the sorted candidate books corresponding to the group of candidate books. Then, the multiple groups of candidate books are spliced ​​together in sequence according to the recall order of the multiple groups of candidate books to obtain a target recommended book list.

[0150] In other embodiments, combining multiple groups of sorted candidate books according to their recall order to obtain a target recommended book list may specifically include: combining multiple groups of sorted candidate books according to their recall order and the order of books in each group of sorted candidate books to obtain a target recommended book list.

[0151] For example, after the book recommendation system recalls multiple groups of candidate books in parallel, after all candidate books are recalled, each group of candidate books can be sorted synchronously to obtain the sorted candidate books corresponding to each group of candidate books. Then, according to the recall order of the multiple groups of candidate books and the order of books in each group of sorted candidate books, the multiple groups of candidate books are spliced ​​together to obtain a target recommended book list.

[0152] For example, a book recommendation system recalls a group A of candidate books and a group B of candidate books, each group having 100 books. After obtaining the sorted group A candidate books and the sorted group B candidate books, the system can first select a target number of books A from the sorted group A candidate books according to the order of the books in the sorted group A candidate books, and select a target number of books B from the sorted group B candidate books according to the order of the books in the sorted group B candidate books. Then, according to the order of the group A candidate books and the group B candidate books, the target number of books A and the target number of books B are spliced ​​together. Then, according to the order of the books in the sorted group A candidate books, the target number of books C is continued to be selected from the sorted group A candidate books, and the target number of books D is continued to be selected from the sorted group B candidate books according to the order of the books in the sorted group B candidate books. And according to the order of the group A candidate books and the group B candidate books, the target number of books C and the target number of books D are spliced ​​after the splicing result of books A and books B, and so on, until all books are completely spliced ​​together to obtain the target recommended book list.

[0153] Therefore, in the embodiment of the present disclosure, the book recommendation system can generate a backup target recommended book list of the target book type for the client, further shortening the waiting time for the user to browse the book list of the same book type next time, thereby improving the user experience.

[0154] Next, continue to combine Figures 5 to 8 The book recommendation system provided by the embodiment of the present disclosure is described.

[0155] Figure 5 A structural diagram of a book recommendation system provided by an embodiment of the present disclosure is shown.

[0156] like Figure 5 As shown, the book recommendation system 500 may include an interactive node 501, a first query node 505, and a list generation node 503 on the server side. The interactive node 501, the first query node 505, and the list generation node 503 may establish a connection and exchange information through a specified protocol such as a network protocol such as Hyper Text Transfer Protocol over Secure Socket Layer (HTTPS). The interactive node 501, the first query node 505, and the list generation node 503 may be servers of the book recommendation system, or may be a service process in the server. The server may include a cloud server or a server cluster, etc., which have storage and computing functions.

[0157] In an embodiment of the present disclosure, the interactive node 501 can be used to receive a book recommendation request sent by a client and send the first book list generated by the list generation node 503 to the client. The book recommendation request includes the target book type to be recommended. The first query node 505 can be used to respond to the book recommendation request and query the first book list corresponding to the target book type in the first recent recommendation result. The first recent recommendation result includes a recommended book list generated within a first preset time period. The first book list is a recommended book list generated for the client. The list generation node 503 can be used to generate a first book list based on the first book list if the first book list is found in the first recent recommendation result.

[0158] The client may be a website platform or an application of an e-book platform, and may be installed in an electronic device, which may include but is not limited to a mobile terminal device and an electronic reading device.

[0159] When a user uses the book recommendation function of the e-book platform through a client, a book recommendation request can be initiated to the interactive node 501 through the client. The interactive node 501 can receive the book recommendation request and send the book recommendation request to the first query node 505. The first query node 505 can respond to the received book recommendation request and query the first book list corresponding to the target book type in the first recent recommendation result. If the first book list is found in the first recent recommendation result, the first query node 505 sends the queried first book list, the target book type and the target identification information in the book recommendation request to the list generation node 503. The list generation node 503 can generate a first book list corresponding to the target book type based on the received first book list, and send the generated first book list and the target identification information to the interactive node 501. The interactive node 501 sends the first book list to the client for display based on the target identification information.

[0160] Optionally, the list generation node 503 can be specifically used to start from the target book in the first book list, select a preset number of consecutive first books to be recommended in the first book list, and the target book is the first book that has not been recommended within a first preset time period; generate a first book list based on the selected first books to be recommended.

[0161] Optionally, the book recommendation request may include any one of the following: a recommendation request generated when the user requests a book recommendation for the target book type for the first time within the first preset time period; a recommendation request generated when the user requests a book recommendation for the target book type for a non-first time within the first preset time period.

[0162] In an embodiment of the present disclosure, after receiving a book recommendation request sent by a client, a first book list corresponding to the target book type included in the book recommendation request can be queried in the first recent recommendation result. If the first book list is found in the first recent recommendation result, a first book list is generated based on the first book list, and the first book list is sent to the client. Since the first recent recommendation result may include a recommended book list generated within a first preset time period and the first book list may be a recommended book list generated for the client, when the user uses the book recommendation function, the books in the recommended book list generated for the user within the first preset time period can be directly recommended to the user, which can meet the needs of personalized book recommendations for different users and save the time of generating book recommendation lists. When using the book recommendation function of the e-book platform, users do not need to wait for a long time to see the books recommended for them according to their reading needs, which meets the needs of users to quickly view the book recommendation list and improves the user experience.

[0163] Figure 6 A structural diagram of another book recommendation system provided by an embodiment of the present disclosure is shown.

[0164] like Figure 6 As shown, the book recommendation system 600 may include an interaction node 601, a first query node 602, a list generation node 603, and a second query node 604. Each node may establish a connection and exchange information using a specified protocol, such as HTTPS. Each node may be a server of the book recommendation system or a service process within a server. The server may include a cloud server or server cluster, or other device with storage and computing capabilities.

[0165] Among them, the interactive node 601, the first query node 602 and the list generation node 603 are already in Figure 5 The description of the illustrated embodiment will not be repeated here.

[0166] In an embodiment of the present disclosure, the second query node 604 can be used to query the first book list corresponding to the target book type in the first recent recommendation result. If the first book list is not found in the first recent recommendation result, the second book list corresponding to the target book type is searched in the second recent recommendation result. The second recent recommendation result includes a recommended book list generated within a second preset time period. The second book list is a recommended book list generated for the client, and the second preset time period includes the first preset time. Furthermore, the list generation node 603 can also be used to generate a second book list based on the second book list if a second book list is found in the second recent recommendation result. The interactive node 601 can also be used to send the second book list to the client.

[0167] Specifically, if the first query node 602 does not find the first book list corresponding to the target book type in the first recent recommendation result, the first query node 602 can forward the book recommendation request to the second query node 604. The second query node 604 can respond to the received book recommendation request and query the second book list corresponding to the target book type in the second recent recommendation result. If the second book list is found in the second recent recommendation result, the second query node 604 can send the queried second book list and the target book type and target identification information in the book recommendation request to the list generation node 603. The list generation node 603 can generate a second book list corresponding to the target book type based on the received second book list, and send the generated second book list and target identification information to the interactive node 601. The interactive node 601 sends the second book list to the client for display based on the target identification information.

[0168] Optionally, the book recommendation system 600 may also include a first recommendation node 605 and a result cache node 606. Connections can be established and information can be exchanged between each node and between each node and other nodes through a specified protocol such as HTTPS. Each node can be a server of the book recommendation system or a service process in the server.

[0169] In an embodiment of the present disclosure, a book recommendation request may also include the user's historical reading data. The first recommendation node 605 may be configured to query the second recent recommendation result for a second book list corresponding to the target book type and, based on the user's historical reading data, generate a target recommended book list corresponding to the target book type for the client. The result cache node 606 may be configured to cache the target recommended book list in the first recent recommendation result and the second recent recommendation result.

[0170] Specifically, if the second query node 604 does not find the second book list corresponding to the target book type in the second recent recommendation result, the second query node 604 can also send the user's historical reading data, the target book type and target identification information in the book recommendation request to the first recommendation node 605. The first recommendation node 605 can generate a target recommended book list corresponding to the target book type based on the user's historical reading data, and send the target recommended book list, target book type and target identification information to the result cache node 606. The result cache node 606 can associate the target recommended book list with the target book type and target identification information and cache them in the first recent recommendation result and the second recent recommendation result.

[0171] Optionally, the first recommendation node 605 can be specifically used to obtain multiple groups of candidate books corresponding to the target book type based on the user's historical reading data; sort the multiple groups of candidate books respectively to obtain multiple groups of sorted candidate books; and combine the multiple groups of sorted candidate books according to the recall order of the multiple groups of sorted candidate books to obtain a target recommended book list.

[0172] Optionally, the first recommendation node 605 may be further configured to combine the multiple groups of sorted candidate books according to their recall order and the order of books in each group of sorted candidate books to obtain a target recommended book list.

[0173] Figure 7 A structural diagram of another book recommendation system provided by an embodiment of the present disclosure is shown.

[0174] like Figure 7 As shown, the book recommendation system 700 may include an interaction node 701, a first query node 702, a list generation node 703, a second query node 704, a first recommendation node 705, a result cache node 706, and a cold start node 707. Each node may establish a connection and exchange information using a specified protocol, such as HTTPS. Each node may be a server of the book recommendation system or a service process within a server. The server may include a cloud server or a server cluster, such as a device with storage and computing capabilities.

[0175] Among them, the interactive node 701, the first query node 702 and the list generation node 703 are already in Figure 5 The second query node 704, the first recommendation node 705 and the result cache node 706 are already in Figure 6 The description of the illustrated embodiment will not be repeated here.

[0176] In an embodiment of the present disclosure, cold start node 707 can be used to query the second recent recommendation results for a second book list corresponding to the target book type. If the second book list is not found in the second recent recommendation results, a popular book list corresponding to the target book type is obtained. Furthermore, list generation node 703 can also be used to generate a third book list based on the popular book list. Interaction node 701 can also be used to send the third book list to the client.

[0177] Specifically, if the second query node 704 does not find the second book list corresponding to the target book type in the second recent recommendation result, the second query node 704 can send a book recommendation request to the cold start node 707. The cold start node 707 can respond to the book recommendation request to obtain a popular book list corresponding to the target book type, and send the obtained popular book list, target book type and target identification information to the list generation node 703. The list generation node 703 can generate a third book list corresponding to the target book type based on the received popular book list, and send the generated third book list and target identification information to the interactive node 701. The interactive node 701 sends the third book list to the client for display based on the target identification information.

[0178] Figure 8 A structural diagram of another book recommendation system provided by an embodiment of the present disclosure is shown.

[0179] like Figure 8 As shown, the book recommendation system 800 may include an interaction node 801, a first query node 802, a list generation node 803, a second query node 804, a first recommendation node 805, a result cache node 806, a cold start node 807, and a second recommendation node 808. Each node may establish a connection and exchange information using a specified protocol, such as HTTPS. Each node may be a server of the book recommendation system or a service process within a server. The server may include a cloud server or server cluster, or other device with storage and computing capabilities.

[0180] Among them, the interactive node 801, the first query node 802 and the list generation node 803 are already in Figure 5 The second query node 804, the first recommendation node 805 and the result cache node 806 are already in Figure 6 The description of the embodiment shown is omitted here. Figure 7 The description of the illustrated embodiment will not be repeated here.

[0181] In the disclosed embodiment, the second recommendation node 808 can be used to obtain the client's corresponding user's historical behavior data before the list generation node 803 generates the first book list based on the selected first book to be recommended; and based on the user's historical behavior data, obtain the second book to be recommended corresponding to the target book type. Furthermore, the list generation node 803 can be specifically used to generate the first book list based on the selected first book to be recommended and the second book to be recommended.

[0182] Specifically, before the list generation node 803 generates the first book list, the second recommendation node 808 can obtain the user historical behavior data, target book type and target identification information corresponding to the client, and obtain the second book to be recommended corresponding to the target book type based on the user historical behavior data, and then send the obtained second book to be recommended, target book type and target identification information to the list generation node 803. The list generation node 803 generates the first book list corresponding to the target book type based on the first book to be recommended corresponding to the target identification information and the second book to be recommended corresponding to the target identification information.

[0183] The embodiment of the present disclosure also provides a server for implementing the aforementioned book recommendation method.

[0184] Figure 9 A schematic structural diagram of a server provided by an embodiment of the present disclosure is shown.

[0185] The server provided in the embodiment of the present disclosure may include a cloud server or a server cluster and other devices with storage and computing functions.

[0186] It should be noted that Figure 9 The server 900 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0187] The server 900 conventionally includes a processor 910 and a computer program product or computer-readable medium in the form of a memory 920. The memory 920 may be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk or a ROM. The memory 920 has a storage space 921 for executable instructions (or program code) 9211 for executing any method steps in the above-mentioned note processing method. For example, the storage space 921 for executable instructions may include individual executable instructions 9211 for respectively implementing various steps in the above-mentioned note processing method. These executable instructions may be read from or written to one or more computer program products. These computer program products include program code carriers such as a hard disk, a compact disc (CD), a memory card or a floppy disk. Such a computer program product is typically a portable or fixed storage unit. The storage unit may have a memory device that is compatible with the computer program product. Figure 9The memory 920 in the server 900 is similarly arranged as a storage segment or storage space. The executable instructions may be compressed, for example, in an appropriate form. Generally, the storage unit includes executable instructions for executing the steps of the note processing method according to the present invention, i.e., codes that can be read by a processor such as the processor 910. When executed by the server 900, these codes cause the server 900 to execute the various steps of the book recommendation method described above.

[0188] Of course, to simplify, Figure 9 Only some of the components related to the present invention in the server 900 are shown, and components such as a bus, input / output interfaces, input devices, and output devices are omitted. In addition, the server 900 may also include any other appropriate components according to specific application scenarios.

[0189] An embodiment of the present invention further provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the processor executes the book recommendation method provided by each embodiment of the present invention.

[0190] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0191] The computer-readable medium may be included in the server, or may exist independently without being incorporated into the server.

[0192] In embodiments of the present invention, program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0193] The present invention discloses:

[0194] A1. A book recommendation method, comprising:

[0195] receiving a book recommendation request sent by a client, wherein the book recommendation request includes a target book type to be recommended;

[0196] In response to the book recommendation request, querying a first book list corresponding to the target book type in a first recent recommendation result, where the first recent recommendation result includes a recommended book list generated within a first preset time period, and the first book list is a recommended book list generated for the client;

[0197] If the first book list is found in the first recent recommendation result, generating a first book list based on the first book list;

[0198] The first book list is sent to the client.

[0199] A2. The method according to claim A1, wherein, after querying the first recent recommendation result for a first book list corresponding to the target book type, the method further comprises:

[0200] If the first book list is not found in the first recent recommendation result, searching for a second book list corresponding to the target book type in the second recent recommendation result, the second recent recommendation result including a recommended book list generated within a second preset time period, the second book list being a recommended book list generated for the client, and the second preset time period including the first preset time;

[0201] If the second book list is found in the second recent recommendation result, generating a second book list according to the second book list;

[0202] The second book list is sent to the client.

[0203] A3. The method according to claim A2, wherein, after querying the second recent recommendation results for a second book list corresponding to the target book type, the method further comprises:

[0204] If the second book list is not found in the second recent recommendation result, obtaining a popular book list corresponding to the target book type;

[0205] generating a third book list according to the popular book list;

[0206] The third book list is sent to the client.

[0207] A4. The method according to claim A2, wherein the book recommendation request further includes user historical reading data;

[0208] After searching the second recent recommendation result for a second book list corresponding to the target book type, the method further includes:

[0209] Based on the user's historical reading data, generating a target recommended book list corresponding to the target book type for the client;

[0210] The target recommended book list is cached in the first recent recommendation result and the second recent recommendation result.

[0211] A5. The method according to claim A4, wherein generating a target recommended book list corresponding to the target book type for the client based on the user's historical reading data comprises:

[0212] Acquire multiple groups of candidate books corresponding to the target book type based on the user's historical reading data;

[0213] Sorting the multiple groups of candidate books respectively to obtain multiple groups of sorted candidate books;

[0214] The plurality of groups of sorted candidate books are combined according to the recall order of the plurality of groups of sorted candidate books to obtain a target recommended book list.

[0215] A6. The method according to claim A5, wherein the step of combining the plurality of groups of sorted candidate books according to their recall order to obtain a target recommended book list comprises:

[0216] The plurality of groups of sorted candidate books are combined according to the recall order of the plurality of groups of sorted candidate books and the order of books in each group of sorted candidate books to obtain the target recommended book list.

[0217] A7. The method according to any one of claims A1 to A6, wherein generating a first book list based on the first book list comprises:

[0218] Starting from a target book in the first book list, a preset number of consecutive first books to be recommended are selected from the first book list, wherein the target book is the first book that has not been recommended within the first preset time period;

[0219] The first book list is generated based on the selected first book to be recommended.

[0220] A8. The method according to claim A7, wherein, before generating the first book list based on the selected first book to be recommended, the method further comprises:

[0221] Obtaining historical user behavior data corresponding to the client;

[0222] Obtaining a second to-be-recommended book corresponding to the target book type according to the user historical behavior data;

[0223] The step of generating the first book list based on the selected first book to be recommended includes:

[0224] The first book list is generated according to the selected first books to be recommended and the second books to be recommended.

[0225] A9. The method according to any one of claims A1 to A8, wherein the book recommendation request includes any one of the following:

[0226] A recommendation request generated by a user requesting a book recommendation for the target book type for the first time within the first preset time period;

[0227] The recommendation request is not the first time that the user requests a book recommendation for the target book type within the first preset time period.

[0228] B10. A server comprising a processor and a memory, wherein the memory is configured to store executable instructions, the executable instructions causing the processor to perform the following operations:

[0229] receiving a book recommendation request sent by a client, wherein the book recommendation request includes a target book type to be recommended;

[0230] In response to the book recommendation request, querying a first book list corresponding to the target book type in a first recent recommendation result, where the first recent recommendation result includes a recommended book list generated within a first preset time period, and the first book list is a recommended book list generated for the client;

[0231] If the first book list is found in the first recent recommendation result, generating a first book list based on the first book list;

[0232] The first book list is sent to the client.

[0233] B11. The server according to claim B10, wherein, after the processor executes the step of querying the first recent recommendation result for a first book list corresponding to the target book type, the executable instructions further cause the processor to execute:

[0234] If the first book list is not found in the first recent recommendation result, searching for a second book list corresponding to the target book type in the second recent recommendation result, the second recent recommendation result including a recommended book list generated within a second preset time period, the second book list being a recommended book list generated for the client, and the second preset time period including the first preset time;

[0235] If the second book list is found in the second recent recommendation result, generating a second book list according to the second book list;

[0236] The second book list is sent to the client.

[0237] B12. The server according to claim B11, wherein, after the processor executes the step of querying the second recent recommendation result for a second book list corresponding to the target book type, the executable instructions further cause the processor to execute:

[0238] If the second book list is not found in the second recent recommendation result, obtaining a popular book list corresponding to the target book type;

[0239] generating a third book list according to the popular book list;

[0240] The third book list is sent to the client.

[0241] B13. The server according to claim B11, wherein the book recommendation request further includes user historical reading data;

[0242] After the processor executes the step of querying the second recent recommendation result for a second book list corresponding to the target book type, the executable instruction further causes the processor to execute:

[0243] Based on the user's historical reading data, generating a target recommended book list corresponding to the target book type for the client;

[0244] The target recommended book list is cached in the first recent recommendation result and the second recent recommendation result.

[0245] B14. The server according to claim B13, wherein, when the processor generates a target recommended book list corresponding to the target book type for the client based on the user's historical reading data, the executable instructions specifically cause the processor to execute:

[0246] Acquire multiple groups of candidate books corresponding to the target book type based on the user's historical reading data;

[0247] Sorting the multiple groups of candidate books respectively to obtain multiple groups of sorted candidate books;

[0248] The plurality of groups of sorted candidate books are combined according to the recall order of the plurality of groups of sorted candidate books to obtain a target recommended book list.

[0249] B15. The server according to claim B14, wherein, when the processor performs the step of combining the multiple groups of sorted candidate books according to the recall order of the multiple groups of sorted candidate books to obtain a target recommended book list, the executable instructions specifically cause the processor to execute:

[0250] The plurality of groups of sorted candidate books are combined according to the recall order of the plurality of groups of sorted candidate books and the order of books in each group of sorted candidate books to obtain the target recommended book list.

[0251] B16. The server according to any one of claims B10 to B15, wherein, when the processor executes the step of generating a first book list based on the first book list, the executable instructions specifically cause the processor to execute:

[0252] Starting from a target book in the first book list, a preset number of consecutive first books to be recommended are selected from the first book list, wherein the target book is the first book that has not been recommended within the first preset time period;

[0253] The first book list is generated based on the selected first book to be recommended.

[0254] B17. The server according to claim B16, wherein, before the processor generates the first book list based on the selected first book to be recommended, the executable instructions further cause the processor to execute:

[0255] Obtaining historical user behavior data corresponding to the client;

[0256] Obtaining a second to-be-recommended book corresponding to the target book type according to the user historical behavior data;

[0257] When the processor generates the first book list based on the selected first book to be recommended, the executable instruction specifically causes the processor to execute:

[0258] The first book list is generated according to the selected first books to be recommended and the second books to be recommended.

[0259] B18. The server according to any one of claims B10 to B17, wherein the book recommendation request includes any one of the following:

[0260] A recommendation request generated by a user requesting a book recommendation for the target book type for the first time within the first preset time period;

[0261] The recommendation request is not the first time that the user requests a book recommendation for the target book type within the first preset time period.

[0262] C19. A book recommendation system includes an interaction node, a first query node, and a list generation node, wherein:

[0263] The interactive node is used to receive a book recommendation request sent by a client and send the first book list generated by the list generation node to the client, wherein the book recommendation request includes the target book type to be recommended;

[0264] The first query node is configured to query a first book list corresponding to the target book type in a first recent recommendation result in response to the book recommendation request, wherein the first recent recommendation result includes a recommended book list generated within a first preset time period, and the first book list is a recommended book list generated for the client;

[0265] The list generating node is configured to generate the first book list based on the first book list if the first book list is found in the first recent recommendation result.

[0266] C20. The system according to claim C19, wherein the system further comprises:

[0267] a second query node configured to query the first recent recommendation result for a first book list corresponding to the target book type, and if the first book list is not found in the first recent recommendation result, query a second recent recommendation result for a second book list corresponding to the target book type, wherein the second recent recommendation result includes a recommended book list generated within a second preset time period, the second book list being a recommended book list generated for the client, and the second preset time period includes the first preset time period;

[0268] The list generation node is further configured to generate a second book list based on the second book list if the second book list is found in the second recent recommendation result; and the interaction node is further configured to send the second book list to the client.

[0269] C21. The system according to claim C20, wherein the system further comprises:

[0270] a cold start node configured to query the second recent recommendation result for a second book list corresponding to the target book type, and if the second book list is not found in the second recent recommendation result, obtain a popular book list corresponding to the target book type;

[0271] The list generation node is further configured to generate a third book list based on the popular book list; and the interactive node is further configured to send the third book list to the client.

[0272] C22. The system according to claim C20, wherein the book recommendation request further includes user historical reading data;

[0273] Wherein, the system further includes:

[0274] a first recommendation node configured to generate a target recommended book list corresponding to the target book type for the client based on the user's historical reading data after querying the second recent recommendation result for a second book list corresponding to the target book type;

[0275] The result cache node is configured to cache the target recommended book list into the first recent recommendation result and the second recent recommendation result.

[0276] C23. The system according to claim C22, wherein the first recommendation node is specifically used to obtain multiple groups of candidate books corresponding to the target book type based on the user's historical reading data; sort the multiple groups of candidate books respectively to obtain multiple groups of sorted candidate books; and combine the multiple groups of sorted candidate books according to the recall order of the multiple groups of sorted candidate books to obtain a target recommended book list.

[0277] C24. The system according to claim C23, wherein the first recommendation node is further specifically used to combine the multiple groups of sorted candidate books according to the recall order of the multiple groups of sorted candidate books and the order of books in each group of sorted candidate books to obtain the target recommended book list.

[0278] C25. The system according to any one of claims C19 to C24, wherein the list generation node is specifically configured to start from a target book in the first book list, select a preset number of consecutive first books to be recommended in the first book list, wherein the target book is the first book that has not been recommended within the first preset time period; and generate the first book list based on the selected first books to be recommended.

[0279] C26. The system according to claim C25, wherein the system further comprises:

[0280] The second recommendation node is configured to obtain user history behavior data corresponding to the client before the list generation node generates the first book list based on the selected first book to be recommended; and obtain a second book to be recommended corresponding to the target book type based on the user history behavior data;

[0281] The list generating node is specifically configured to generate the first book list according to the selected first book to be recommended and the second book to be recommended.

[0282] C27. The system according to any one of claims C19 to C26, wherein the book recommendation request includes any one of the following:

[0283] A recommendation request generated by a user requesting a book recommendation for the target book type for the first time within the first preset time period;

[0284] The recommendation request is not the first time that the user requests a book recommendation for the target book type within the first preset time period.

[0285] C28. A computer-readable storage medium, characterized in that the storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the book recommendation method described in any one of claims A1 to A9.

Claims

1. A book recommendation method, characterized in that: include: receiving a book recommendation request sent by a client, wherein the book recommendation request includes a target book type to be recommended; In response to the book recommendation request, querying a first book list corresponding to the target book type in a first recent recommendation result, where the first recent recommendation result includes a recommended book list generated within a first preset time period, and the first book list is a recommended book list generated for the client; If the first book list is found in the first recent recommendation result, generating a first book list based on the first book list; Sending the first book list to the client; The step of generating a first book list according to the first book list includes: Starting from a target book in the first book list, a preset number of consecutive first books to be recommended are selected from the first book list, wherein the target book is the first book that has not been recommended within the first preset time period; The first book list is generated based on the selected first book to be recommended.

2. The method according to claim 1, characterized in that After querying the first recent recommendation result for a first book list corresponding to the target book type, the method further includes: If the first book list is not found in the first recent recommendation result, searching for a second book list corresponding to the target book type in the second recent recommendation result, the second recent recommendation result including a recommended book list generated within a second preset time period, the second book list being a recommended book list generated for the client, and the second preset time period including the first preset time; If the second book list is found in the second recent recommendation result, generating a second book list according to the second book list; The second book list is sent to the client.

3. The method according to claim 2, characterized in that After querying the second recent recommendation result for a second book list corresponding to the target book type, the method further includes: If the second book list is not found in the second recent recommendation result, obtaining a popular book list corresponding to the target book type; generating a third book list according to the popular book list; The third book list is sent to the client.

4. The method according to claim 2, characterized in that The book recommendation request also includes user historical reading data; After searching the second recent recommendation result for a second book list corresponding to the target book type, the method further includes: Based on the user's historical reading data, generating a target recommended book list corresponding to the target book type for the client; The target recommended book list is cached in the first recent recommendation result and the second recent recommendation result.

5. The method according to claim 4, characterized in that Generating a target recommended book list corresponding to the target book type for the client based on the user's historical reading data includes: Acquire multiple groups of candidate books corresponding to the target book type based on the user's historical reading data; Sorting the multiple groups of candidate books respectively to obtain multiple groups of sorted candidate books; The plurality of groups of sorted candidate books are combined according to the recall order of the plurality of groups of sorted candidate books to obtain a target recommended book list.

6. The method according to claim 5, characterized in that The step of combining the plurality of groups of sorted candidate books according to the recall order of the plurality of groups of sorted candidate books to obtain a target recommended book list includes: The plurality of groups of sorted candidate books are combined according to the recall order of the plurality of groups of sorted candidate books and the order of books in each group of sorted candidate books to obtain the target recommended book list.

7. The method according to claim 1, characterized in that Before generating the first book list based on the selected first book to be recommended, the method further includes: Obtaining historical user behavior data corresponding to the client; Obtaining a second to-be-recommended book corresponding to the target book type according to the user historical behavior data; The step of generating the first book list based on the selected first book to be recommended includes: The first book list is generated according to the selected first books to be recommended and the second books to be recommended.

8. The method according to any one of claims 1 to 7, characterized in that The book recommendation request includes any one of the following: A recommendation request generated by a user requesting a book recommendation for the target book type for the first time within the first preset time period; The recommendation request is not the first time that the user requests a book recommendation for the target book type within the first preset time period.

9. A server, characterized in that: The system comprises a processor and a memory, wherein the memory is configured to store executable instructions, and the executable instructions enable the processor to perform the following operations: receiving a book recommendation request sent by a client, wherein the book recommendation request includes a target book type to be recommended; In response to the book recommendation request, querying a first book list corresponding to the target book type in a first recent recommendation result, where the first recent recommendation result includes a recommended book list generated within a first preset time period, and the first book list is a recommended book list generated for the client; If the first book list is found in the first recent recommendation result, generating a first book list based on the first book list; Sending the first book list to the client; When the processor generates a first book list according to the first book list, the executable instruction specifically causes the processor to execute: Starting from a target book in the first book list, a preset number of consecutive first books to be recommended are selected from the first book list, wherein the target book is the first book that has not been recommended within the first preset time period; The first book list is generated based on the selected first book to be recommended.

10. The server according to claim 9, wherein: After the processor executes the step of querying the first recent recommendation result for a first book list corresponding to the target book type, the executable instructions further cause the processor to execute: If the first book list is not found in the first recent recommendation result, searching for a second book list corresponding to the target book type in the second recent recommendation result, the second recent recommendation result including a recommended book list generated within a second preset time period, the second book list being a recommended book list generated for the client, and the second preset time period including the first preset time; If the second book list is found in the second recent recommendation result, generating a second book list according to the second book list; The second book list is sent to the client.

11. The server according to claim 10, wherein: After the processor executes the step of querying the second recent recommendation result for a second book list corresponding to the target book type, the executable instructions further cause the processor to execute: If the second book list is not found in the second recent recommendation result, obtaining a popular book list corresponding to the target book type; generating a third book list according to the popular book list; The third book list is sent to the client.

12. The server according to claim 10, wherein: The book recommendation request also includes user historical reading data; After the processor executes the step of querying the second recent recommendation result for a second book list corresponding to the target book type, the executable instruction further causes the processor to execute: Based on the user's historical reading data, generating a target recommended book list corresponding to the target book type for the client; The target recommended book list is cached in the first recent recommendation result and the second recent recommendation result.

13. The server according to claim 12, wherein: When the processor generates a target recommended book list corresponding to the target book type for the client based on the user's historical reading data, the executable instruction specifically causes the processor to execute: Acquire multiple groups of candidate books corresponding to the target book type based on the user's historical reading data; Sorting the multiple groups of candidate books respectively to obtain multiple groups of sorted candidate books; The plurality of groups of sorted candidate books are combined according to the recall order of the plurality of groups of sorted candidate books to obtain a target recommended book list.

14. The server according to claim 13, wherein: When the processor performs the step of combining the multiple groups of sorted candidate books according to the recall order of the multiple groups of sorted candidate books to obtain a target recommended book list, the executable instructions specifically cause the processor to execute: The plurality of groups of sorted candidate books are combined according to the recall order of the plurality of groups of sorted candidate books and the order of books in each group of sorted candidate books to obtain the target recommended book list.

15. The server according to any one of claims 9, characterized in that Before the processor generates the first book list based on the selected first book to be recommended, the executable instructions further cause the processor to execute: Obtaining historical user behavior data corresponding to the client; Obtaining a second to-be-recommended book corresponding to the target book type according to the user historical behavior data; When the processor generates the first book list based on the selected first book to be recommended, the executable instruction specifically causes the processor to execute: The first book list is generated according to the selected first books to be recommended and the second books to be recommended.

16. The server according to any one of claims 9 to 15, characterized in that: The book recommendation request includes any one of the following: A recommendation request generated by a user requesting a book recommendation for the target book type for the first time within the first preset time period; The recommendation request is not the first time that the user requests a book recommendation for the target book type within the first preset time period.

17. A book recommendation system, characterized in that: It includes an interaction node, a first query node, and a list generation node, where: The interactive node is used to receive a book recommendation request sent by a client and send the first book list generated by the list generation node to the client, wherein the book recommendation request includes the target book type to be recommended; The first query node is configured to query a first book list corresponding to the target book type in a first recent recommendation result in response to the book recommendation request, wherein the first recent recommendation result includes a recommended book list generated within a first preset time period, and the first book list is a recommended book list generated for the client; The list generating node is configured to generate the first book list based on the first book list if the first book list is found in the first recent recommendation result; The list generation node is specifically configured to start from a target book in the first book list, select a preset number of consecutive first books to be recommended in the first book list, where the target book is the first book that has not been recommended within the first preset time period; and generate the first book list based on the selected first books to be recommended.

18. The system according to claim 17, wherein: The system further comprises: a second query node configured to query the first recent recommendation result for a first book list corresponding to the target book type, and if the first book list is not found in the first recent recommendation result, query a second recent recommendation result for a second book list corresponding to the target book type, wherein the second recent recommendation result includes a recommended book list generated within a second preset time period, the second book list being a recommended book list generated for the client, and the second preset time period includes the first preset time period; The list generation node is further configured to generate a second book list based on the second book list if the second book list is found in the second recent recommendation result; and the interaction node is further configured to send the second book list to the client.

19. The system according to claim 18, wherein: The system further comprises: a cold start node configured to query the second recent recommendation result for a second book list corresponding to the target book type, and if the second book list is not found in the second recent recommendation result, obtain a popular book list corresponding to the target book type; The list generation node is further configured to generate a third book list based on the popular book list; and the interactive node is further configured to send the third book list to the client.

20. The system according to claim 19, wherein: The book recommendation request also includes user historical reading data; Wherein, the system further includes: a first recommendation node configured to generate a target recommended book list corresponding to the target book type for the client based on the user's historical reading data after querying the second recent recommendation result for a second book list corresponding to the target book type; The result cache node is configured to cache the target recommended book list into the first recent recommendation result and the second recent recommendation result.

21. The system according to claim 20, wherein: The first recommendation node is specifically configured to obtain multiple groups of candidate books corresponding to the target book type based on the user's historical reading data; and sort the multiple groups of candidate books to obtain multiple groups of sorted candidate books. The plurality of groups of sorted candidate books are combined according to the recall order of the plurality of groups of sorted candidate books to obtain a target recommended book list.

22. The system according to claim 21, wherein: The first recommendation node is further specifically configured to combine the multiple groups of sorted candidate books according to the recall order of the multiple groups of sorted candidate books and the order of books in each group of sorted candidate books to obtain the target recommended book list.

23. The system according to claim 17, wherein: The system further comprises: The second recommendation node is configured to obtain user history behavior data corresponding to the client before the list generation node generates the first book list based on the selected first book to be recommended; and obtain a second book to be recommended corresponding to the target book type based on the user history behavior data; The list generating node is specifically configured to generate the first book list according to the selected first book to be recommended and the second book to be recommended.

24. The system according to any one of claims 17 to 23, characterized in that The book recommendation request includes any one of the following: A recommendation request generated by a user requesting a book recommendation for the target book type for the first time within the first preset time period; The recommendation request is not the first time that the user requests a book recommendation for the target book type within the first preset time period.

25. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the book recommendation method according to any one of claims 1 to 8.

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