Book recommendation method, terminal and storage medium

Abstract

The present invention relates to a book recommendation method, terminal and storage medium. The book recommendation method includes: obtaining a target book and multiple books to be recommended, where the number of the target book is one; determining the user sets corresponding to the target book and the books to be recommended, and the reading intensity information corresponding to the multiple users included in each user set; based on the user sets and the reading intensity information, determining multiple one-way similarities between the target book and the multiple books to be recommended; wherein the one-way similarity represents the similarity calculated with the target book as the reference direction; determining the recommended books based on the multiple one-way similarities, and recommending the recommended books. The present invention can filter out books that users who have read the target book are more interested in for recommendation, thereby improving the accuracy of book recommendations, and further improving the reading stickiness and interest of users.

Description

Technical Field

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

[0002] With the rapid development of Internet technology, electronic books are increasingly used and paid more attention to. Users can choose books of interest to read based on recommended books.

[0003] Currently, recommended books can be determined by the similarities between different books, but the accuracy of the above book recommendations is low and cannot meet the needs. Summary of the invention

[0004] In order to solve the technical problem of low accuracy of book recommendations, the present disclosure provides a book recommendation method, a terminal, and a storage medium to improve the accuracy of book recommendations.

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

[0006] Obtain a target book and a plurality of books to be recommended, where the number of the target book is one;

[0007] Determine the user sets corresponding to the target book and the book to be recommended, respectively, and the reading intensity information corresponding to the multiple users included in each user set;

[0008] Based on the user set and the reading intensity information, a plurality of one-way similarities between the target book and the plurality of books to be recommended are respectively determined; wherein the one-way similarities represent similarities calculated with the target book as a reference direction;

[0009] Recommended books are determined based on the multiple one-way similarities, and the recommended books are recommended.

[0010] In a second aspect, an embodiment of the present disclosure provides a terminal, including:

[0011] processor;

[0012] A memory for storing executable instructions;

[0013] The processor is used to read the executable instructions from the memory and execute the executable instructions to perform the following operations:

[0014] Obtain a target book and a plurality of books to be recommended, where the number of the target book is one;

[0015] Determine the user sets corresponding to the target book and the book to be recommended, respectively, and the reading intensity information corresponding to the multiple users included in each user set;

[0016] Based on the user set and the reading intensity information, a plurality of one-way similarities between the target book and the plurality of books to be recommended are respectively determined; wherein the one-way similarities represent similarities calculated with the target book as a reference direction;

[0017] Recommended books are determined based on the multiple one-way similarities, and the recommended books are recommended.

[0018] In a third aspect, an embodiment of the present disclosure provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the book recommendation method of the first aspect.

[0019] One of the above technical solutions has the following advantages or beneficial effects:

[0020] According to the book recommendation method, terminal and storage medium of the embodiment of the present disclosure, it is possible to obtain a target book and multiple books to be recommended, determine the user sets corresponding to the target book and the books to be recommended, and the reading intensity information corresponding to the multiple users included in each user set, and then determine multiple one-way similarities between the target book and the multiple books to be recommended based on the user sets and the reading intensity information, determine the recommended books based on the multiple one-way similarities, and recommend the recommended books. The embodiment of the present disclosure can determine the one-way similarity between a single target book and other books through the reading intensity information, and then recommend books based on the one-way similarity. Since the one-way similarity is calculated with the target book as the reference direction, compared with the lack of directional similarity in the related art, for users who have read the target book, it is possible to screen out books that they are more interested in for recommendation, thereby improving the accuracy of book recommendations, and thereby improving the reading stickiness and interest of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0022] Figure 1 A flowchart of a book recommendation method provided by an embodiment of the present disclosure;

[0023] Figure 2 A flowchart of another book recommendation method provided by an embodiment of the present disclosure;

[0024] Figure 3 A schematic diagram of a book recommendation provided by an embodiment of the present disclosure;

[0025] Figure 4 A schematic diagram of the structure of a terminal provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] Embodiments of the present disclosure will be described in more detail below 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 being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0027] 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.

[0028] The term "including" and its variations used herein are open inclusions, 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"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0029] It should be noted that the concepts such as "first" and "second" mentioned in the present 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.

[0030] It should be noted that the modifications of "one" and "plurality" 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, it should be understood as "one or more".

[0031] 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.

[0032] In the related art, recommended books can be determined by the similarity between different books, but the similarity between two books lacks directionality when determining. For example, many people read book A before, but fewer people read book A now, and many people turn to read book B. These two books actually have certain similarities. Suppose there is a new reader now, he first opens book A and starts reading, then he hopes to recommend book B to him; and if the new reader opens book B first, because book A is not read by many people now, he does not want to recommend book A to him at this time; but if the above recommendation method is adopted, the similarity between the two books does not have directionality, so for any one of the two books, the other book will be recommended as a similar book to it, which is not the expected result, and the accuracy of the recommendation is low, which cannot meet the needs.

[0033] In order to solve the above problems, the embodiments of the present disclosure provide a book recommendation method, which is introduced below in conjunction with specific embodiments.

[0034] Figure 1 The flowchart of a book recommendation method provided by the embodiment of the present disclosure is shown in FIG. 1 , which can be executed by a book recommendation device, wherein the device can be implemented by software and / or hardware and can generally be integrated in a terminal or a server. Figure 1 As shown, the method includes:

[0035] Step 101: Obtain a target book and multiple books to be recommended.

[0036] The target book may be a book related to the current user, and is also the basis for determining the recommended book. In the embodiment of the present disclosure, the number of target books is one, and the target book may be the current book that the user is reading. The books to be recommended may be some books related to the target book. In the embodiment of the present disclosure, the books to be recommended may have at least one attribute information identical to that of the target book, and the attribute information may include information such as the book type, writing time, and author, that is, the books to be recommended may be some books with the same attribute information as that of the target book.

[0037] In the embodiment of the present disclosure, the book recommendation apparatus may obtain a target book and a plurality of books to be recommended that are related to the current user.

[0038] Step 102: Determine the user sets corresponding to the target book and the book to be recommended, respectively, and the reading intensity information corresponding to the multiple users included in each user set.

[0039] The user set corresponding to the book may be a set of multiple users who have read the book. The reading intensity information may be information representing the depth and interest of the user in reading the book. The reading intensity information may include one or more types of information. For example, the reading information may include reading time, interaction data, and payment status, etc. The specific information may be determined according to actual conditions.

[0040] In an embodiment of the present disclosure, determining the user sets corresponding to the target book and the books to be recommended, respectively, and the reading intensity information corresponding to the multiple users included in each user set may include: determining, based on pre-acquired target relationship data, a first user set corresponding to the target book and the reading intensity information corresponding to the multiple users included in the first user set, and determining a second user set corresponding to each book to be recommended and the reading intensity information corresponding to the multiple users included in the second user set.

[0041] The target relationship data may be obtained from a database, and the target relationship data may include information such as the corresponding relationship between users and books and the reading intensity information of each user for each book. The book recommendation device may obtain the target relationship data from the database, and search for multiple users corresponding to the target book and the reading intensity information corresponding to each user according to the target relationship data to obtain a first user set, and search for multiple users corresponding to each book to be recommended and the reading intensity information corresponding to each user according to the target relationship data to obtain a second user set. The number of the second user sets is the same as the number of books to be recommended, and the number of the second user sets may be multiple.

[0042] Step 103: Based on the user set and the reading intensity information, a plurality of one-way similarities between the target book and a plurality of books to be recommended are determined respectively.

[0043] Among them, the one-way similarity can represent the similarity calculated with the target book as the reference direction. The one-way similarity can be a similarity with a reference direction, that is, it has a tendency and emphasis when calculating the similarity. The one-way similarity in the embodiment of the present disclosure takes the target book as the reference direction, that is, it filters the books of interest to users who have read the target book.

[0044] For example, Figure 2 A flowchart of another book recommendation method provided by an embodiment of the present disclosure is shown in FIG. Figure 2 The method of determining multiple one-way similarities between the target book and multiple books to be recommended based on the user set and the reading intensity information may include the following steps:

[0045] Step 201: Determine a first user subset from a first user set according to a first selection condition, and determine a second user subset from a second user set according to a second selection condition.

[0046] The first selection condition and the second selection condition can be two selection ranges or two selection conditions with different selection strictness, and the difference is relatively large. Since this solution selects books of interest to users who have read the target book, the first selection condition for the target book has a low degree of strictness, while the second selection condition for the recommended book has a high degree of strictness, that is, the second selection condition has a much higher degree of strictness than the first selection condition.

[0047] The first selection condition can be used to select users whose reading intensity information of the target book is greater than or equal to the first threshold, and the second selection condition can be used to select users whose reading intensity information of each to-be-recommended book is greater than or equal to the second threshold, and the second threshold is much greater than the first threshold. The first threshold and the second threshold can be values ​​that respectively characterize the strictness of the selection of the first selection condition and the second selection condition. The first threshold is relatively low and can cover most of the users who have read the target book as much as possible, and the second threshold is relatively high and can select most of the interested users as much as possible.

[0048] The first threshold and the second threshold can be set and adjusted according to the actual situation. For example, when the reading intensity information is the reading time, the first threshold can be 0.1 hours and the second threshold can be 10 hours. When the first threshold is increased, the number of users in the first user subset decreases, the traffic is concentrated, and the quality becomes higher. When the second threshold is lowered, the number of users in the second user subset increases, the traffic is not concentrated, and the quality becomes lower. If you want to recommend books that users are more interested in, increase the second threshold. If you want to recommend more books to users, lower the second threshold.

[0049] Specifically, the book recommendation device can compare the reading intensity information of each user in the first user set with the first threshold according to the first selection condition, and select the users whose reading intensity information is greater than or equal to the first threshold to obtain the first user subset; and can compare the reading intensity information of each user in the second user set with the second threshold according to the second selection condition, and select the users whose reading intensity information is greater than or equal to the second threshold to obtain the second user subset.

[0050] Step 202: Determine the intersection between the first user subset and the second user subset to obtain a third user set.

[0051] After determining the first user subset and the second user subset, the book recommendation device may determine the intersection of the first user subset and the second user subset, that is, determine the users included in both the first user subset and the second user subset, to obtain a third user set.

[0052] Step 203: Determine multiple one-way similarities between the target book and multiple books to be recommended based on the third user set, the number of books read by two users in the third user set, and a preset similarity formula.

[0053] After the book recommendation device determines the third user set, it can combine multiple users in the third user set in pairs to obtain user pairs, determine the number of books read by the two users in each user pair, and then determine the one-way similarity between the target book and each book to be recommended according to a preset similarity formula to obtain multiple one-way similarities.

[0054] The above preset similarity formula can be:

[0055]

[0056] Among them, i represents the target book, j represents a book to be recommended, and U i represents users who have read the target book, U j Indicates users who have read the recommended books. u represents the number of books read by user u, I v represents the number of books read by user v, U i ∩U j represents the third user set, I u ∩I v represents the number of books read by user u and user v together, where user u and user v may be any two users in the third user set.

[0057] Exemplarily, assuming that the reading intensity information is the reading time, taking the first book and the second book as examples, the first threshold in the first selection condition can be 0.1 hours, and the second threshold in the second selection condition can be 10 hours. When the first book is the target book and the second book is a book to be recommended, the number of users whose reading time for the first book is determined to be greater than or equal to 0.1 hours is 20,000, and the number of users whose reading time for the second book is determined to be greater than or equal to 10 hours is 1,000. According to the above preset similarity formula, sim(first book, second book) is determined; when the second book is the target book and the first book is a book to be recommended, the number of users whose reading time for the second book is determined to be greater than or equal to 0.1 hours is 20,000, and the number of users whose reading time for the first book is determined to be greater than or equal to 10 hours is 10. According to the above preset similarity formula, sim(second book, first book) is determined, sim(first book, second book) is lower, and sim(second book, first book) is higher. Therefore, a user will not recommend the second book when reading the first book, but will recommend the first book when reading the second book. Compared with the related art, sim (first book, second book) is the same as sim (second book, first book), and there is no indiscriminate recommendation caused by directionality. The recommended books of this solution are more accurate.

[0058] Step 104: Determine recommended books based on multiple one-way similarities, and recommend the recommended books.

[0059] The recommended books may be the books that are finally determined to be recommended to the current user. Since the target book is the current book that the user is reading, the recommended books may be determined based on the current book and the recommended books may be recommended. Specifically, the recommendation may be implemented by displaying the recommendation information including the recommended books.

[0060] In some embodiments of the present disclosure, determining recommended books based on multiple one-way similarities may include: determining the recommended books whose one-way similarities are greater than or equal to the similarity threshold as recommended books. The similarity threshold may be set according to actual conditions. When the target book is the current book, the book recommendation device may compare the multiple one-way similarities with the similarity threshold in sequence, and determine the recommended books whose one-way similarities are greater than or equal to the similarity threshold as recommended books.

[0061] According to the book recommendation method of the embodiment of the present disclosure, it is possible to obtain a target book and multiple books to be recommended, determine the user sets corresponding to the target book and the books to be recommended, and the reading intensity information corresponding to the multiple users included in each user set, and then determine multiple one-way similarities between the target book and the multiple books to be recommended based on the user sets and the reading intensity information, determine the recommended books based on the multiple one-way similarities, and recommend the recommended books. The embodiment of the present disclosure can determine the one-way similarity between the target book and other books through the reading intensity information, and then recommend books based on the one-way similarity. Since the one-way similarity is calculated with the target book as the reference direction, compared with the lack of directional similarity in the related art, for users who have read the target book, it is possible to screen out books that they are more interested in for recommendation, thereby improving the accuracy of book recommendations, and thereby improving the reading stickiness and interest of users.

[0062] In some embodiments, the reading intensity information may include at least one of the following:

[0063] Total reading time;

[0064] The number of interactions includes at least one of the number of likes, the number of comments, and the number of shares;

[0065] Total reading level, which includes the sum of reading levels of each reading. The reading level is determined based on the reading time, the time difference between the reading time and the current time, and the decay value;

[0066] Total number of payments or total payment amount;

[0067] The decay value of the reading time.

[0068] The total reading time may be the sum of the reading time of a book read by a user at different times. The number of interactions may be at least one of the number of likes, comments, and shares of a book by a user.

[0069] The reading degree can be understood as the depth of a user's reading of a book, which can be determined based on the reading time, the time difference between the reading time and the current time, the decay value and the reading degree formula. The reading degree formula can be expressed as x = t*a y , where x represents the reading degree of a single reading, t represents the reading time, a represents the decay value, which can be a fixed value, for example, the decay value can be set to 0.95, and y represents the time difference between the reading time and the current time. For example, the current user read a book for 3 hours 50 days ago, and the decay value is set to 0.95, then the reading degree x = 3*0.95 50, which means 3 times 0.95 raised to the power of 50. The total reading degree may be equal to the sum of the reading degrees of multiple readings of a book by the user, that is, the total reading degree may be obtained by adding up the reading degrees of multiple readings.

[0070] The total number of payments and the total payment amount may be representations related to payment, and the reading intensity information may be the sum of the number of payments made by the user for a book, or the sum of the amounts paid by the user for multiple times for a book.

[0071] The decay value of reading time may be the decay value corresponding to the most recent reading time of a book by the user. The decay value may be inversely proportional to the reading time. The earlier the reading time, the smaller the decay value. For example, if the reading time is from the current time to one month ago, the decay value is 0.95; if the reading time is from one month to one year ago, the decay value is 0.5; if the reading time is any time before one year ago, the decay value is 0.1. The above are only examples. And the change of the decay value may follow a certain rule. For example, the decay value may decay in a linear, broken line, or exponential manner.

[0072] The reading intensity information in the disclosed embodiment may be represented by various types of information, which improves the richness and diversity of the reading intensity information and facilitates subsequent book recommendations.

[0073] For example, Figure 3 A schematic diagram of a book recommendation provided by an embodiment of the present disclosure, such as Figure 3 As shown, the figure shows a display page 300 of book A. Book A is the current book that the user is reading, and the recommended book determined for book A is book B. The display page 300 can display recommendation information 301 including book B. The recommendation information 301 may include information such as the name and cover of book B, which is used to prompt the user to read book B related to book A.

[0074] Above Figure 3 This corresponds to the scenario where the target book is the current book recommendation. Figure 3 The recommendation methods in the article are only examples, and other methods of recommending books are applicable.

[0075] The disclosed embodiment also provides a terminal, which may include a processor and a memory, and the memory may be used to store executable instructions. The processor may be used to read the executable instructions from the memory, and execute the executable instructions to perform the following operations: obtain a target book and multiple books to be recommended, where the number of the target book is one; determine the user sets corresponding to the target book and the books to be recommended, and the reading intensity information corresponding to the multiple users included in each user set; based on the user set and the reading intensity information, determine multiple one-way similarities between the target book and the multiple books to be recommended; wherein the one-way similarity represents the similarity calculated with the target book as the reference direction; determine the recommended book based on the multiple one-way similarities, and recommend the recommended book.

[0076] Figure 4 The terminal 400 in the embodiment of the present invention may be the electronic device described above. It should also be noted that: Figure 4 The terminal 400 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0077] The terminal 400 conventionally includes a processor 410 and a computer program product or computer-readable medium in the form of a memory 420. The memory 420 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 420 has a storage space 421 for executable instructions (or program codes) 4211 for executing any method steps in the above-mentioned book recommendation method. For example, the storage space 421 for executable instructions may include individual executable instructions 4211 respectively used to implement various steps in the above-mentioned book recommendation 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 disk (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 storage device having a storage medium ... Figure 4 The memory 420 in the terminal is similarly arranged as a storage segment or storage space, etc. The executable instructions can be compressed, for example, in an appropriate form. Generally, the storage unit includes executable instructions for executing the steps of the book recommendation method according to the present invention, that is, codes that can be read by a processor such as the processor 410, and when these codes are executed by the terminal, the terminal executes the various steps in the book recommendation method described above.

[0078] An embodiment of the present invention further provides a computer-readable storage medium on which computer program instructions are stored. 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.

[0079] 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 include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. 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 of the above.

[0080] The computer-readable medium may be included in the terminal, or may exist independently without being installed in the terminal.

[0081] In an embodiment of the present invention, computer program code for performing the operation 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 a user's computer, partially on a user's computer, as a separate software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of 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).

[0082] The various component embodiments of the present invention may be implemented in hardware in all or part of the steps, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the book recommendation device according to an embodiment of the present invention. The present invention may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0083] According to one or more embodiments of the present disclosure, the present disclosure provides a book recommendation method, comprising:

[0084] Obtain a target book and a plurality of books to be recommended, where the number of the target book is one;

[0085] Determine the user sets corresponding to the target book and the book to be recommended, and the reading intensity information corresponding to the multiple users included in each user set;

[0086] Based on the user set and the reading intensity information, multiple one-way similarities between the target book and the multiple books to be recommended are determined respectively; wherein the one-way similarity represents the similarity calculated with the target book as the reference direction;

[0087] Determine recommended books based on multiple one-way similarities and recommend the recommended books.

[0088] Optionally, determining the user sets corresponding to the target book and the book to be recommended, respectively, and the reading intensity information corresponding to the multiple users included in each user set, includes:

[0089] According to the pre-acquired target relationship data, the first user set corresponding to the target book and the reading intensity information corresponding to the multiple users included in the first user set are determined, and the second user set corresponding to each to-be-recommended book and the reading intensity information corresponding to the multiple users included in the second user set are determined.

[0090] Optionally, the target relationship data includes the corresponding relationship between users and books and the reading intensity information of each user for each book.

[0091] Optionally, based on the user set and the reading intensity information, multiple one-way similarities between the target book and multiple books to be recommended are determined respectively, including:

[0092] Determine a first user subset in the first user set according to a first selection condition, and determine a second user subset in the second user set according to a second selection condition;

[0093] Determine an intersection between the first user subset and the second user subset to obtain a third user set;

[0094] According to the third user set, the number of books read by two users in the third user set, and a preset similarity formula, multiple one-way similarities between the target book and the multiple books to be recommended are determined.

[0095] Optionally, the first selection condition is used to select users whose reading intensity information for the target book is greater than or equal to a first threshold, and the second selection condition is used to select users whose reading intensity information for each book to be recommended is greater than or equal to a second threshold, and the second threshold is much greater than the first threshold.

[0096] Optionally, the target book is the current book that the user is reading, and the book to be recommended has at least one attribute information identical to that of the target book.

[0097] Optionally, determine recommended books based on multiple one-way similarities, including:

[0098] The books to be recommended whose one-way similarities are greater than or equal to the similarity threshold among the multiple one-way similarities are determined as recommended books.

[0099] Optionally, the reading intensity information includes at least one of the following:

[0100] Total reading time;

[0101] The number of interactions includes at least one of the number of likes, the number of comments, and the number of shares;

[0102] Total reading level, which includes the sum of reading levels of each reading. The reading level is determined based on the reading time, the time difference between the reading time and the current time, and the decay value;

[0103] Total number of payments or total payment amount;

[0104] The decay value of the reading time.

[0105] According to one or more embodiments of the present disclosure, the present disclosure provides a terminal, including:

[0106] processor;

[0107] a memory for storing processor-executable instructions;

[0108] A processor that reads executable instructions from memory and executes the instructions to:

[0109] Obtain a target book and a plurality of books to be recommended, where the number of the target book is one;

[0110] Determine the user sets corresponding to the target book and the book to be recommended, and the reading intensity information corresponding to the multiple users included in each user set;

[0111] Based on the user set and the reading intensity information, multiple one-way similarities between the target book and the multiple books to be recommended are determined respectively; wherein the one-way similarity represents the similarity calculated with the target book as the reference direction;

[0112] Determine recommended books based on multiple one-way similarities and recommend the recommended books.

[0113] Optionally, the executable instructions further cause the processor to perform the following operations:

[0114] The process of determining the user sets corresponding to the target book and the books to be recommended and the reading intensity information corresponding to the multiple users included in each user set includes:

[0115] According to the pre-acquired target relationship data, the first user set corresponding to the target book and the reading intensity information corresponding to the multiple users included in the first user set are determined, and the second user set corresponding to each to-be-recommended book and the reading intensity information corresponding to the multiple users included in the second user set are determined.

[0116] Optionally, the target relationship data includes the corresponding relationship between users and books and the reading intensity information of each user for each book.

[0117] Optionally, the executable instructions further cause the processor to perform the following operations:

[0118] Wherein, based on the user set and the reading intensity information, multiple one-way similarities between the target book and multiple books to be recommended are determined respectively, including:

[0119] Determine a first user subset in the first user set according to a first selection condition, and determine a second user subset in the second user set according to a second selection condition;

[0120] Determine an intersection between the first user subset and the second user subset to obtain a third user set;

[0121] According to the third user set, the number of books read by two users in the third user set, and a preset similarity formula, multiple one-way similarities between the target book and the multiple books to be recommended are determined.

[0122] Optionally, the first selection condition is used to select users whose reading intensity information for the target book is greater than or equal to a first threshold, and the second selection condition is used to select users whose reading intensity information for each book to be recommended is greater than or equal to a second threshold, and the second threshold is much greater than the first threshold.

[0123] Optionally, the target book is the current book that the user is reading, and the book to be recommended has at least one attribute information identical to that of the target book.

[0124] Optionally, the executable instructions further cause the processor to perform the following operations:

[0125] Among them, the recommended books are determined based on multiple one-way similarities, including:

[0126] The books to be recommended whose one-way similarities are greater than or equal to the similarity threshold among the multiple one-way similarities are determined as recommended books.

[0127] Optionally, the reading intensity information includes at least one of the following:

[0128] Total reading time;

[0129] The number of interactions includes at least one of the number of likes, the number of comments, and the number of shares;

[0130] Total reading level, which includes the sum of reading levels of each reading. The reading level is determined based on the reading time, the time difference between the reading time and the current time, and the decay value;

[0131] Total number of payments or total payment amount;

[0132] The decay value of the reading time.

[0133] According to one or more embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium, the storage medium storing a computer program, and the computer program is used to execute any of the book recommendation methods provided by the present disclosure.

[0134] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) by each other.

[0135] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present invention. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0136] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A book recommendation method, characterized in that: include: Obtain a target book and a plurality of books to be recommended, where the number of the target book is one, and the target book is a book related to the current user; Determine user sets corresponding to the target book and the book to be recommended, respectively, and reading intensity information corresponding to a plurality of users included in each user set, wherein the user set corresponding to the target book is a first user set, and the user set corresponding to the book to be recommended is a second user set; Based on the user set and the reading intensity information, a plurality of one-way similarities between the target book and the plurality of books to be recommended are respectively determined; wherein the one-way similarities represent similarities calculated with the target book as a reference direction; Determining recommended books based on the multiple one-way similarities, and recommending the recommended books; The determining, based on the user set and the reading intensity information, a plurality of one-way similarities between the target book and the plurality of books to be recommended, respectively, comprises: A first user subset is determined in the first user set according to a first selection condition, and a second user subset is determined in the second user set according to a second selection condition; the first selection condition is used to select users whose reading intensity information of the target book is greater than or equal to a first threshold, and the second selection condition is used to select users whose reading intensity information of each of the books to be recommended is greater than or equal to a second threshold, and the second threshold is much greater than the first threshold; Determine an intersection between the first user subset and the second user subset to obtain a third user set; Determine the multiple one-way similarities between the target book and the multiple books to be recommended according to the third user set, the number of books read by two users in the third user set, and a preset similarity formula; Wherein, the preset similarity formula is: Wherein, i represents the target book, j represents a book to be recommended, indicates users who have read the target book. Indicates users who have read the book to be recommended. represents the number of books read by user u, represents the number of books read by user v, represents the third user set, represents the number of books read together by the user u and the user v, where the user u and the user v are any two users in the third user set.

2. The method according to claim 1, characterized in that Determining the user sets corresponding to the target book and the book to be recommended respectively and the reading intensity information corresponding to the multiple users included in each user set, including: According to the pre-acquired target relationship data, the first user set corresponding to the target book and the reading intensity information corresponding to the multiple users included in the first user set are determined, and the second user set corresponding to each of the books to be recommended and the reading intensity information corresponding to the multiple users included in the second user set are determined.

3. The method according to claim 2, characterized in that The target relationship data includes the corresponding relationship between users and books and the reading intensity information of each user for each book.

4. The method according to claim 1, characterized in that The target book is the current book that the user is reading, and the to-be-recommended book has at least one attribute information identical to that of the target book.

5. The method according to claim 4, characterized in that Determining recommended books based on the multiple one-way similarities includes: The books to be recommended whose one-way similarities are greater than or equal to the similarity threshold are determined as recommended books.

6. The method according to claim 1, characterized in that The reading intensity information includes at least one of the following: Total reading time; The number of interactions includes at least one of the number of likes, the number of comments, and the number of shares; A total reading degree, the total reading degree includes the sum of the reading degrees of each reading, and the reading degree is determined based on the reading time, the time difference between the reading time and the current time, and the decay value; Total number of payments or total payment amount; The decay value of the reading time.

7. A terminal, characterized in that: include: processor; A memory for storing executable instructions; The processor is used to read the executable instructions from the memory and execute the executable instructions to perform the following operations: Obtain a target book and a plurality of books to be recommended, where the number of the target book is one, and the target book is a book related to the current user; Determine user sets corresponding to the target book and the book to be recommended, respectively, and reading intensity information corresponding to a plurality of users included in each user set, wherein the user set corresponding to the target book is a first user set, and the user set corresponding to the book to be recommended is a second user set; Based on the user set and the reading intensity information, a plurality of one-way similarities between the target book and the plurality of books to be recommended are respectively determined; wherein the one-way similarities are used to calculate similarity based on the target book; Determining recommended books based on the multiple one-way similarities, and recommending the recommended books; The determining, based on the user set and the reading intensity information, a plurality of one-way similarities between the target book and the plurality of books to be recommended, respectively, comprises: A first user subset is determined in the first user set according to a first selection condition, and a second user subset is determined in the second user set according to a second selection condition; the first selection condition is used to select users whose reading intensity information of the target book is greater than or equal to a first threshold, and the second selection condition is used to select users whose reading intensity information of each of the books to be recommended is greater than or equal to a second threshold, and the second threshold is much greater than the first threshold; Determine an intersection between the first user subset and the second user subset to obtain a third user set; Determine the multiple one-way similarities between the target book and the multiple books to be recommended according to the third user set, the number of books read by two users in the third user set, and a preset similarity formula; Wherein, the preset similarity formula is: Wherein, i represents the target book, j represents a book to be recommended, indicates users who have read the target book. Indicates users who have read the book to be recommended. represents the number of books read by user u, represents the number of books read by user v, represents the third user set, represents the number of books read together by the user u and the user v, where the user u and the user v are any two users in the third user set.

8. The terminal according to claim 7, characterized in that: The executable instructions further cause the processor to perform the following operations: The step of determining the user sets corresponding to the target book and the book to be recommended and the reading intensity information corresponding to the multiple users included in each user set includes: According to the pre-acquired target relationship data, the first user set corresponding to the target book and the reading intensity information corresponding to the multiple users included in the first user set are determined, and the second user set corresponding to each of the books to be recommended and the reading intensity information corresponding to the multiple users included in the second user set are determined.

9. The terminal according to claim 8, characterized in that: The target relationship data includes the corresponding relationship between users and books and the reading intensity information of each user for each book.

10. The terminal according to claim 7, characterized in that: The target book is the current book that the user is reading, and the to-be-recommended book has at least one attribute information identical to that of the target book.

11. The terminal according to claim 10, characterized in that: The executable instructions further cause the processor to perform the following operations: Wherein, determining the recommended books based on the multiple one-way similarities includes: The books to be recommended whose one-way similarities are greater than or equal to the similarity threshold are determined as recommended books.

12. The terminal according to claim 7, characterized in that: The reading intensity information includes at least one of the following: Total reading time; The number of interactions includes at least one of the number of likes, the number of comments, and the number of shares; A total reading degree, the total reading degree includes the sum of the reading degrees of each reading, and the reading degree is determined based on the reading time, the time difference between the reading time and the current time, and the decay value; Total number of payments or total payment amount; The decay value of the reading time.

13. 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 6.

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