CN114637914A - List processing method, computing device and storage medium - Google Patents

Abstract

The invention discloses a list processing method, a computing device and a storage medium, wherein the list processing method comprises the following steps: collecting historical behavior data of a user aiming at books in a list to be processed; analyzing the behavior path depth of the historical behavior data to obtain negative feedback level data of the user for the book; and adjusting the arrangement sequence of the books in the list to be processed according to the negative feedback grade data to obtain the processed list. According to the technical scheme provided by the invention, the negative feedback level data of the user for the books is determined by analyzing the behavior path depth of the historical behavior data, and the arrangement sequence of the books in the list is adjusted according to the negative feedback level data, so that the arrangement sequence of the books which are not interested by the user can be effectively adjusted, and the arrangement sequence of the books in the list can be determined by fusing the preference of the user.

Description

List processing method, computing device and storage medium

technical field

The invention relates to the technical field of data processing, in particular to a method for processing a list, a computing device and a storage medium.

Background technique

Books in the form of e-books are favored by a large number of users due to their advantages such as easy access. The e-book platform usually includes a book list page, and recommends books to users through the book list page. The user's behaviors such as clicking and reading on books can reflect their preferences for books to a certain extent. However, in the existing list processing methods, books are usually ranked according to the book popularity value, the number of book collectors and other strategies to obtain the list, and the results of their preference for books are not fed back to the list based on user behavior. , so that the books that the user sees each time he browses the book list page are roughly the same. For example, the books that the user is not interested in browsing the list last time still exist when he browses the list next time. This list processing method cannot well integrate users' preferences for book recommendation, resulting in poor book recommendation effect.

SUMMARY OF THE INVENTION

In view of the above problems, the present invention is proposed to provide a list processing method, a computing device and a storage medium that overcome the above problems or at least partially solve the above problems.

According to one aspect of the present invention, a method for processing a list is provided, comprising:

Collect historical behavior data of users for the books in the list to be processed;

Depth analysis of the behavior path of the historical behavior data to obtain the user's negative feedback level data for books;

According to the negative feedback level data, adjust the order of books in the list to be processed to obtain the processed list.

According to another aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface communicate with each other through the communication bus;

The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform the following operations:

Collect historical behavior data of users for the books in the list to be processed;

Depth analysis of the behavior path of the historical behavior data to obtain the user's negative feedback level data for books;

According to the negative feedback level data, adjust the order of books in the list to be processed to obtain the processed list.

According to another aspect of the embodiments of the present invention, a computer storage medium is provided, where at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform operations corresponding to the above method for processing a list.

According to the technical solution provided by the present invention, the user's historical behavior data for books is introduced into the ranking mechanism, and the user's negative feedback level data for books is determined by analyzing the behavior path depth of the historical behavior data, and the negative feedback level data It can be used to reflect the user's disinterest in books, adjust the order of books in the list according to the negative feedback level data, so as to feed back the preference results of books to the list based on the user's behavior. The effective adjustment of the arrangement order of books that the user is not interested in makes it possible to integrate the user's preferences to determine the arrangement order of the books in the list, optimize the processing method of the book list, and help obtain better book recommendation effects.

The above description is only an overview of the technical solutions of the present invention, in order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand , the following specific embodiments of the present invention are given.

Description of drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are for the purpose of illustrating preferred embodiments only and are not to be considered limiting of the invention. Also, the same components are denoted by the same reference numerals throughout the drawings. In the attached image:

1 shows a schematic flowchart of a method for processing a list according to Embodiment 1 of the present invention;

2a shows a schematic flowchart of a method for processing a list according to Embodiment 2 of the present invention;

Figure 2b shows a schematic diagram of a user's behavioral path for books;

Figure 2c shows a schematic diagram of the arrangement order of multiple books with the same book popularity value from front to back;

Figure 2d shows a schematic diagram 1 of the display of the book list page;

Fig. 2e shows the second display schematic diagram of the book list page;

FIG. 3 shows a schematic structural diagram of a computing device according to Embodiment 4 of the present invention.

Detailed ways

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be more thoroughly understood, and will fully convey the scope of the present disclosure to those skilled in the art.

Example 1

FIG. 1 shows a schematic flowchart of a method for processing a list according to Embodiment 1 of the present invention. As shown in FIG. 1 , the method includes the following steps:

Step S101 , collecting historical behavior data of the user with respect to the books in the list to be processed.

The list to be processed refers to a ranking list formed by arranging books in the e-book platform in advance according to the initial ranking index of the list, and the list to be processed records the order of the multiple books. Considering that the user's click, reading and other behaviors on books can reflect his preference for books to a certain extent, in this embodiment, the user's historical behavior data on books is introduced into the ranking mechanism, and the user's historical behavior data is introduced into the ranking mechanism. If the data affects the arrangement order of the books in the list to be processed, then in step S101 , historical behavior data of the user for the books in the list to be processed needs to be collected. The user's historical behavior data for the books in the pending list is the data used to describe the interaction between the user and the book, which may specifically include: exposing book behavior data, clicking book behavior data, reading book behavior data, and adding bookshelf behavior data Wait. The user's preference for books can be reflected through historical behavior data.

In step S102, the depth of the behavior path of the historical behavior data is analyzed to obtain the negative feedback level data of the user for the book.

On the book list page, books will be displayed according to the order of the books. Specifically, book information such as book covers, book titles, and book description texts of multiple books can be displayed. The user's behavior path for books displayed on the book list page mainly includes multiple nodes such as exposing books, clicking books, entering book details pages, reading books, and adding bookshelves. Specifically, the user can browse book information such as the book cover, book title, book description text, etc. of the book on the book list page, and enter the book details by clicking on the book cover, book title, book description text, etc. on the book list page. Page to browse book details, users can enter the book reading page to read books by triggering the read button on the book details page, and users can also add books to their own bookshelf by triggering the add bookshelf button. Then, in order to facilitate the determination of the negative feedback level data of the user for the book, a multi-level feedback node may be set on the user's behavior path according to the depth of the behavior path. After collecting the historical behavior data, the behavior path depth can be obtained by analyzing the historical behavior data, and then the node data of the feedback node corresponding to the behavior path depth can be used as the negative feedback level data of the book.

Step S103, according to the negative feedback level data, adjust the arrangement order of the books in the list to be processed, and obtain the processed list.

Among them, the negative feedback level data is data used to indicate the degree of the user's disinterest in the book. If a book has negative feedback level data, indicating that the user is not very interested in the book, the recommendation weight can be correspondingly reduced according to the negative feedback level data, so that the book is ranked in the pending list. back row, so as to get the processed list. Specifically, the higher the level of negative feedback level data, the higher the degree of user disinterest in the book, the more the recommendation weight is reduced, and the later in the list to be processed, so that Books that are not of interest to the user are placed in the back row.

Using the list processing method provided by this embodiment, the user's historical behavior data for books is introduced into the ranking mechanism, and the user's negative feedback level data for books is determined by analyzing the behavior path depth of the historical behavior data. The feedback level data can be used to reflect the user's disinterest in books, and adjust the order of books in the list according to the negative feedback level data, so as to feed back the results of the user's preference for books into the list based on the user's behavior. , realizes the effective adjustment of the arrangement order of the books that the user is not interested in, makes it possible to integrate the user's preference to determine the arrangement order of the books in the list, optimizes the processing method of the book list, and helps to obtain better book recommendations Effect.

Embodiment 2

Fig. 2a shows a schematic flowchart of a method for processing a list according to Embodiment 2 of the present invention. As shown in Fig. 2a, the method includes the following steps:

Step S201, collecting historical behavior data of the user with respect to the books in the list to be processed.

In this embodiment, considering that the user's historical behavior data for books in the list to be processed can reflect the user's preference for books, the user's historical behavior data for books is introduced into the ranking mechanism. The historical behavior data affects the order of the books in the list to be processed. In step S201, the user's historical behavior data for the books in the list to be processed may be collected from an e-book platform or the like. The historical behavior data may include behavior data of exposing books, behavior data of clicking books, behavior data of reading books, and behavior data of adding bookshelves. The historical behavior data may also include other data of interaction between the user and the book, which is not limited here.

Step S202, setting multi-level feedback nodes on the user's behavior path for the book according to the behavior path depth.

By analyzing the user's behavior path for books, it can be seen that the behavior path mainly includes multiple nodes such as exposing books, clicking books, entering book details pages, reading books, and adding bookshelves. Figure 2b shows a schematic diagram of the user's behavior path for books. As shown in Figure 2b, the behavior path mainly includes: exposing the book information of the book to the user on the book list page; judging whether the user clicks the book information of the book, if clicked , then enter the book details page, if not clicked, the user can return to the book list page, and expose the book information of other books to the user on the book list page; after entering the book details page, determine whether the user triggers the read button, if If it is triggered, it will enter the book reading page. If it is not triggered, the user can return to the book list page and expose the book information of other books to the user on the book list page; after entering the book reading page, if the user finds that he likes this book through reading When the book wants to continue reading in the future, it can be added to the bookshelf. If the user finds that he does not like this book through reading, he can return to the book list page and expose the book information of other books to the user on the book list page.

According to the behavior path, the user's preference for books can be predicted according to the user's operation behavior on the books in the list to be processed. The user likes the book; on the contrary, if the user performs a series of operations of "expose the book", "click the book" and "read the book", but finally does not add the book to the bookshelf, it can be determined that the user is not interested in the book; Since the more the user knows about a book, the higher the possibility of his decision, the more the above series of operations are performed on the premise of not being added to the bookshelf, it means that the user is determined to The higher the probability that books are not of interest.

In step S202, analyze the user's behavior path for the book, search for nodes that can reflect the user's negative feedback, and set multi-level feedback nodes on the behavior path according to the depth of the behavior path. Specifically, considering that the book was exposed but the user did not click on the book, the user clicked the book but did not read, and read the book but did not add it to the bookshelf can represent negative feedback from the user, which can be used to reflect the user's disinterest in the book. , then the set multi-level feedback nodes may include: a feedback node corresponding to exposing a book but not clicking a book, a feedback node corresponding to clicking a book but not reading a book, and a feedback node corresponding to reading a book but not adding to the bookshelf. Among them, since the more the user knows about a book, the higher the possibility of his decision, the deeper the behavior path is without adding to the bookshelf, it means that the user does not feel the book. The higher the probability of interest, the higher the level of its corresponding feedback node. As shown in Figure 2b, the feedback node corresponding to exposing the book without clicking the book is the first-level feedback node, the feedback node corresponding to clicking the book but not reading the book is the second-level feedback node, and the feedback node corresponding to reading the book but not adding to the bookshelf is the third-level feedback node Level feedback node, wherein the level data of three different levels of feedback nodes can be used to reflect the degree of user disinterest in books, the level of the third level feedback node is higher than the level of the second level feedback node, the level of the second level feedback node A level higher than the first-level feedback node.

Step S203: Analyze the behavior path depth of the historical behavior data to obtain a feedback node corresponding to the behavior path depth, and use the node data of the feedback node as the user's negative feedback level data for the book.

Specifically, the behavior path depth of the historical behavior data collected in step S201 is analyzed. If the analyzed behavior path depth has a corresponding feedback node, the node data of the feedback node is used as the negative feedback level data of the book. The node data of the feedback node may specifically include data such as the level of the feedback node, the depth of the behavior path corresponding to the feedback node, and the like. Those skilled in the art can also set the node data to include other data, which is not limited here.

For example, a multi-level feedback node is shown in Figure 2b. If the collected behavior path depth of the user's historical behavior data for book 1 in the list to be processed is the exposed book without clicking on the book, it corresponds to the first-level feedback node. , the node data of the first-level feedback node is taken as the negative feedback level data of book 1. For example, the negative feedback level data of book 1 may include level one, and the corresponding behavior path depth is exposure books without clicking books.

After the negative feedback level data is obtained, the arrangement order of the books in the list to be processed can be adjusted according to the negative feedback level data to obtain the processed list. If a book has negative feedback level data, indicating that the user is not very interested in the book, the recommendation weight can be correspondingly reduced according to the negative feedback level data, so that the book is ranked in the pending list. back row. In practical application scenarios, the list to be processed refers to a list formed by arranging the books in the e-book platform according to the initial ranking index of the list in advance. The list to be processed contains many books, such as a list to be processed. There are 300,000 books, and there will be many books under the same initial ranking index of the list, so under the same initial ranking index of the list, the negative feedback level data of these books can be used to adjust their waiting list. Handle the sorting order in the list. For books with different initial ranking indicators of the list, they are still sorted according to the preset order of the initial ranking indicators of the list. Specifically, it is implemented through steps S204 to S208.

Step S204, collecting the initial ranking index of each book in the list to be processed.

Among them, the initial ranking indicators of the list include: book popularity value, number of readers, number of book likes, number of book collectors, book rating, update time, author attention parameters and/or cumulative reading time. The parameters of the author's attention may be the number of the author's followers, etc.

Step S205, it is judged whether the initial ranking index of any two books in the list to be processed is the same; if so, step S206 is executed; if not, step S207 is executed.

Step S206, according to the negative feedback level data, lower the recommendation weight of the books corresponding to the negative feedback level data, and adjust the arrangement order of any two books in the list to be processed according to the recommendation weight.

In the case where it is judged that the initial ranking index of any two books is the same, the recommendation weight of the book corresponding to the negative feedback level data is lowered according to the negative feedback level data. The order of the two books in the list to be processed. The recommendation weight corresponds to the negative feedback level data. The higher the level of the negative feedback level data, the lower the recommendation weight.

The following is an introduction based on the initial ranking index of the list as the book popularity value. In the to-be-processed list, as a whole, the books are sorted in descending order of popularity value of books, wherein the popularity value of books is usually in units of 10,000. For multiple books with the same book popularity value, the recommendation weight of the book corresponding to the negative feedback level data is lowered, so that the higher the level of the negative feedback level data, the lower the recommendation weight of the corresponding book. . That is to say, for multiple books with the same book popularity value, among the multiple books, the recommendation weight of unexposed books > the recommendation weight of exposed but unclicked books > the recommendation weight of clicked but unread books > read As for the recommendation weight of the books that are not added to the bookshelf, the higher the recommendation weight, the higher the order of the corresponding books in the multiple books with the same book popularity value. Figure 2c shows a schematic diagram of the arrangement order of multiple books with the same book popularity value from front to back. As shown in Figure 2c, among the multiple books with the same book popularity value, the unexposed books are ranked the most in order Before, arrange the exposed but not clicked books behind the unexposed books, the clicked but not read books after the exposed but not clicked books, the read but not added bookshelf books after the clicked but not read the back of the book.

Step S207, according to the preset order of the initial ranking index of the list, determine the ranking order of any two books in the list to be processed.

The preset order may be from high to low or from low to high. Take the initial ranking index of the list as the popularity value of books, and the preset order is from high to low as an example. Suppose two of the books are Book 1 and Book 2. The user has negative feedback level data for Book 1, and the user has negative feedback level data for Book 1. 2 does not have negative feedback level data, but since the book popularity value of book 1 is higher than that of book 2, the order of book 1 in the pending list is before the order of book 2, that is, Book 1 comes before Book 2.

In step S208, the processed list is obtained.

After completing the above sorting for all the books in the list to be processed, the processed list can be obtained.

Step S209, adding a behavior mark to the book according to the historical behavior data.

In order to facilitate users to quickly identify which books on the book list page are the books they read but have not added to the bookshelf, and which books are the books that they have added to the bookshelf, behavior tags can be added to the books corresponding to the historical behavior data according to the historical behavior data. Among them, the behavior mark includes the mark that has been read and/or the mark on the bookshelf. Those skilled in the art can set the specific mark form of the behavior mark according to actual needs, which is not limited here.

Specifically, the read mark is used to identify books that have been read by the user but not added to the bookshelf, and the bookshelf mark is used to identify books that have been added to the bookshelf by the user. If it is known based on the historical behavior data of a certain book that the user has read the book but has not added it to the bookshelf, a read mark will be added to the book; if known based on the historical behavior data of a certain book, the user has added the book to the bookshelf Bookshelf, add the Bookshelf mark for the book.

In step S210, the book information of each book in the processed list and the behavior mark of each book are displayed on the book list page.

When the user enters the book list page, the book information of each book in the processed list and the behavior mark of each book may be displayed on the book list page. Fig. 2d shows the first display diagram of the book list page. As shown in Fig. 2d, the book list page shows the list obtained by arranging the books of the publication type according to the book popularity value from high to low. In the book list page, the book card area 21 of multiple books is displayed, and the book information of the book is displayed in the book card area 21 of each book, and the book information includes book cover, book title, book description Text, where "X" represents a character. For books that the user has read but not added to the bookshelf, a read mark 22 is displayed in the upper right corner of the book card area 21, that is, the mark in the form of "read" text as shown in Figure 2d; for books that the user has added to the bookshelf, In the upper right corner of the book card area 21, an on-shelf mark 23 is displayed, that is, a mark in the form of "on the bookshelf" as shown in FIG. 2d.

Optionally, on the book list page, a function of shielding the books that the user has read but not added to the bookshelf in the list may also be provided. Wherein, a read shielding switch assembly can be set at a preset position on the book list page, and those skilled in the art can set the preset position according to actual needs. The user can turn on or off the muting function by triggering the read muting switch component.

Specifically, in response to the user's request for turning on the read blocking switch component on the book list page, the user's read books are removed from each book in the processed list displayed on the book list page. The read books may specifically include books that the user has read but not added to the bookshelf. Those skilled in the art can also set that the read books also include books that the user has read and added to the bookshelf, etc., which is not limited here.

That is to say, only the unread books of the user may be displayed on the book list page, but the read books of the user may not be displayed, so that the user can quickly select unread books from the list for browsing. In addition, when the user has turned on the blocking function, the blocking function can be turned off by triggering the read blocking switch component again. Specifically, in response to the user's request for closing the read blocking switch component in the book list page, the user is restored Display of the read books on the book list page, that is, the book information of each book in the processed list is displayed on the book list page, wherein the displayed books include the user's unread books Books and Books Read.

As shown in FIG. 2d , a read shielding switch component 24 is provided at the upper right corner of the book list page, and the user can turn on or off the shielding function by triggering the read shielding switch component 24 . If the user activates the blocking function by triggering the read blocking switch component 24, the updated book list page can be as shown in FIG. 2e, and the books with the read mark are removed from the list displayed on the book list page. , that is, book 1 is removed, and the book information of more books is displayed in the order of the books in the list. If the user continues to trigger the read shielding switch component 24 in the page state shown in FIG. 2e, the shielding function will be turned off, and the book list page will be restored to the page state shown in FIG. 2d.

Using the list processing method provided in this embodiment, the user's historical behavior data for books is introduced into the ranking mechanism, multi-level feedback nodes are set on the user's behavior path for books according to the depth of the behavior path, and the historical behavior data is analyzed by analyzing the historical behavior data. The depth of the behavior path to determine the user’s negative feedback level data for books, and for multiple books with the same initial ranking index of the list, the recommendation weight of the books corresponding to the negative feedback level data is adjusted down, so as to make the negative feedback level data lower. The higher the level of feedback level data, the lower the recommendation weight of the corresponding book, so that the books that the user is not interested in are ranked backward according to the recommendation weight, so that the order of the books in the list can be well integrated with the user's preference. , which is helpful to obtain better book recommendation effect; in addition, it can also add behavior tags to books based on historical behavior data, so that users can quickly and easily identify which books on the book list page are read but not added to the bookshelf Books, which books are the books that have been added to the bookshelf; in addition, a read blocking function is also provided to filter out the read books from the list for users, which greatly facilitates users to find unread books.

Embodiment 3

Embodiment 3 of the present invention provides a non-volatile storage medium, where the storage medium stores at least one executable instruction, and the executable instruction can execute the list processing method in any of the foregoing method embodiments.

The executable instructions can specifically be used to cause the processor to perform the following operations: collect the historical behavior data of the user for the books in the list to be processed; analyze the behavior path depth of the historical behavior data to obtain the negative feedback level data of the user for the books ;According to the negative feedback level data, adjust the order of books in the list to be processed, and get the processed list.

In an optional implementation manner, the historical behavior data includes behavior data of exposing books, behavior data of clicking books, behavior data of reading books, and behavior data of adding bookshelves.

In an optional embodiment, the executable instructions further cause the processor to perform the following operations: set multi-level feedback nodes on the user's behavior path for the book according to the behavior path depth; analyze the behavior path depth of the historical behavior data, The feedback node corresponding to the depth of the behavior path is obtained, and the node data of the feedback node is used as the negative feedback level data of the user for the book.

In an optional embodiment, the multi-level feedback nodes include: a feedback node corresponding to exposing a book without clicking on a book, a feedback node corresponding to clicking a book without reading a book, and a feedback node corresponding to reading a book without adding to the bookshelf.

In an optional implementation manner, the executable instructions further cause the processor to perform the following operations: collect the initial ranking index of each book in the list to be processed; determine the ranking of any two books in the list to be processed Whether the initial ranking indicators of the list are the same; if so, according to the negative feedback level data, lower the recommendation weight of the books corresponding to the negative feedback level data, and adjust the order of any two books in the pending list according to the recommendation weight ; if not, determine the order of any two books in the list to be processed according to the preset order of the initial ranking index of the list.

In an optional implementation manner, the initial ranking indicators of the list include: book popularity value, number of readers, number of likes of the book, number of book collectors, book rating, update time, author attention parameter and/or cumulative reading time.

In an optional embodiment, the executable instructions further cause the processor to perform the following operations: add a behavior mark to the book according to the historical behavior data; wherein, the behavior mark includes a read mark and/or a mark on the bookshelf; The book information of each book in the processed list and the behavior mark of each book are displayed on a single page.

In an optional implementation manner, the executable instructions further cause the processor to perform the following operation: in response to the user's turning on operation of the read blocking switch component in the book list page, from the processed post-processing displayed on the book list page Remove the user's read books from the books in the list.

Embodiment 4

FIG. 3 shows a schematic structural diagram of a computing device according to Embodiment 4 of the present invention. The specific embodiment of the present invention does not limit the specific implementation of the computing device.

As shown in FIG. 3 , the computing device may include: a processor (processor) 302 , a communications interface (Communications Interface) 304 , a memory (memory) 306 , and a communication bus 308 .

in:

The processor 302 , the communication interface 304 , and the memory 306 communicate with each other through the communication bus 308 .

The communication interface 304 is used for communicating with network elements of other devices such as clients or other servers.

The processor 302 is configured to execute the program 310, and specifically may execute the relevant steps in the above-mentioned embodiments of the list processing methods.

Specifically, the program 310 may include program code including computer operation instructions.

The processor 302 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the computing device may be the same type of processors, such as one or more CPUs; or may be different types of processors, such as one or more CPUs and one or more ASICs.

The memory 306 is used to store the program 310 . Memory 306 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

The program 310 can specifically be used to make the processor 302 perform the following operations: collect the historical behavior data of the user for the books in the list to be processed; analyze the behavior path depth of the historical behavior data to obtain the negative feedback level data of the user for the books ;According to the negative feedback level data, adjust the order of books in the list to be processed, and get the processed list.

In an optional implementation manner, the historical behavior data includes behavior data of exposing books, behavior data of clicking books, behavior data of reading books, and behavior data of adding bookshelves.

In an optional embodiment, the program 310 further causes the processor 302 to perform the following operations: set multi-level feedback nodes on the user's behavior path for the book according to the behavior path depth; analyze the behavior path depth of the historical behavior data, The feedback node corresponding to the depth of the behavior path is obtained, and the node data of the feedback node is used as the negative feedback level data of the user for the book.

In an optional embodiment, the multi-level feedback nodes include: a feedback node corresponding to exposing a book without clicking on a book, a feedback node corresponding to clicking a book without reading a book, and a feedback node corresponding to reading a book without adding to the bookshelf.

In an optional implementation manner, the program 310 further causes the processor 302 to perform the following operations: collect initial ranking indicators of each book in the list to be processed; determine the ranking of any two books in the list to be processed Whether the initial ranking indicators of the list are the same; if so, according to the negative feedback level data, lower the recommendation weight of the books corresponding to the negative feedback level data, and adjust the order of any two books in the pending list according to the recommendation weight ; if not, determine the order of any two books in the list to be processed according to the preset order of the initial ranking index of the list.

In an optional implementation manner, the initial ranking indicators of the list include: book popularity value, number of readers, number of likes of the book, number of book collectors, book rating, update time, author attention parameter and/or cumulative reading time.

In an optional embodiment, the program 310 further causes the processor 302 to perform the following operations: add a behavior mark to the book according to the historical behavior data; wherein the behavior mark includes a read mark and/or a mark on the bookshelf; The book information of each book in the processed list and the behavior mark of each book are displayed on a single page.

In an optional implementation manner, the program 310 further causes the processor 302 to perform the following operations: in response to the user's turning on the read blocking switch component in the book list page, from the processed post-processing displayed on the book list page Remove the user's read books from the books in the list.

For the specific implementation of each step in the program 310, reference may be made to the description corresponding to the corresponding step in the above-mentioned list processing embodiment, which is not repeated here. Those skilled in the art can clearly understand that, for the convenience and brevity of description, for the specific working process of the device described above, reference may be made to the corresponding process description in the foregoing method embodiments, which will not be repeated here.

Through the solution provided in this embodiment, the user's negative feedback level data for books is determined by analyzing the behavior path depth of historical behavior data, and the arrangement order of books in the list is adjusted according to the negative feedback level data, so that the user's negative feedback level data is adjusted. The effective adjustment of the arrangement order of uninteresting books makes it possible to integrate the user's preference to determine the arrangement order of books in the list.

The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used with teaching based on this. The structure required to construct such a system is apparent from the above description. Furthermore, the present invention is not directed to any particular programming language. It should be understood that various programming languages may be used to implement the inventions described herein, and that the descriptions of specific languages above are intended to disclose the best mode for carrying out the invention.

In the description provided herein, numerous specific details are set forth. It will be understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it is to be understood that in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or its description. However, this disclosure should not be construed as reflecting an intention that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art will understand that the modules in the device in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment. The modules or units or components in the embodiments may be combined into one module or unit or component, and further they may be divided into multiple sub-modules or sub-units or sub-assemblies. All features disclosed in this specification (including accompanying claims, abstract and drawings) and any method so disclosed may be employed in any combination, unless at least some of such features and/or procedures or elements are mutually exclusive. All processes or units of equipment are combined. Each feature disclosed in this specification (including accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that although some of the embodiments described herein include certain features, but not others, included in other embodiments, that combinations of features of different embodiments are intended to be within the scope of the invention within and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

It should be noted that the above-described embodiments illustrate rather than limit the invention, and that alternative embodiments may be devised by those skilled in the art without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. The use of the words first, second, and third, etc. do not denote any order. These words can be interpreted as names.

The present invention discloses:

A1. A list processing method, including:

Collect historical behavior data of users for the books in the list to be processed;

Analyzing the behavior path depth of the historical behavior data to obtain the negative feedback level data of the user for the book;

According to the negative feedback level data, the arrangement order of the books in the to-be-processed list is adjusted to obtain a processed list.

A2. The method according to A1, wherein the historical behavior data includes: exposing book behavior data, clicking book behavior data, reading book behavior data, and adding bookshelf behavior data.

A3. The method according to A1, before the depth of the behavior path of the historical behavior data is analyzed to obtain the negative feedback level data of the user for the book, the method further comprises: according to the depth of the behavior path Set multi-level feedback nodes on the user's behavior path for books;

The depth analysis of the behavior path of the historical behavior data to obtain the negative feedback level data of the user for the book further includes:

The behavior path depth of the historical behavior data is analyzed to obtain a feedback node corresponding to the behavior path depth, and the node data of the feedback node is used as the negative feedback level data of the user for the book.

A4. According to the method of A3, the multi-level feedback nodes include: a feedback node corresponding to exposing a book but not clicking a book, a feedback node corresponding to clicking a book but not reading a book, and a feedback node corresponding to reading a book but not adding to the bookshelf.

A5. The method according to any one of A1-A4, wherein according to the negative feedback level data, adjusting the arrangement order of the books in the to-be-processed list, and obtaining the processed list further includes:

Collect the initial ranking index of each book in the to-be-processed list;

Determine whether the initial ranking index of any two books in the to-be-processed list is the same;

If yes, adjust the recommendation weight of the book corresponding to the negative feedback level data according to the negative feedback level data, and adjust the ranking of any two books in the list to be processed according to the recommendation weight. Order;

If not, according to the preset sequence of the initial ranking index of the list, determine the sequence of the any two books in the list to be processed.

A6. According to the method described in A5, the initial ranking indicators of the list include: book popularity, number of readers, number of likes of books, number of book collectors, book ratings, update time, author attention parameters and/or cumulative reading time.

A7. The method according to any one of A1-A6, the method further comprises:

Add a behavior mark to the book according to the historical behavior data; wherein, the behavior mark includes a read mark and/or a book shelf mark;

The book information of each book in the processed list and the behavior mark of each book are displayed on the book list page.

A8. The method according to any one of A1-A7, the method further comprises:

In response to the user's turning on the read blocking switch component on the book list page, the read books of the user are removed from the books in the processed list displayed on the book list page.

B9. A computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform the following operations:

Collect historical behavior data of users for the books in the list to be processed;

Analyzing the behavior path depth of the historical behavior data to obtain the negative feedback level data of the user for the book;

According to the negative feedback level data, the arrangement order of the books in the to-be-processed list is adjusted to obtain a processed list.

B10. The computing device according to B9, wherein the historical behavior data includes behavior data of exposing books, behavior data of clicking books, behavior data of reading books, and behavior data of adding bookshelves.

B11. The computing device of B9, the executable instructions further cause the processor to perform the following operations:

Set multi-level feedback nodes on the user's behavior path for books according to the depth of the behavior path;

The behavior path depth of the historical behavior data is analyzed to obtain a feedback node corresponding to the behavior path depth, and the node data of the feedback node is used as the negative feedback level data of the user for the book.

B12. The computing device according to B11, the multi-level feedback nodes include: feedback nodes corresponding to exposing books without clicking on books, feedback nodes corresponding to clicking books without reading books, and feedback nodes corresponding to reading books without adding bookshelves.

B13. The computing device of any of B9-B12, the executable instructions further cause the processor to perform the following operations:

Collect the initial ranking index of each book in the to-be-processed list;

Determine whether the initial ranking index of any two books in the to-be-processed list is the same;

If yes, adjust the recommendation weight of the book corresponding to the negative feedback level data according to the negative feedback level data, and adjust the ranking of any two books in the list to be processed according to the recommendation weight. Order;

If not, according to the preset order of the initial ranking index of the list, determine the order of the any two books in the list to be processed.

B14. The computing device according to B13, the initial ranking index of the list includes: book popularity value, number of readers, number of book likes, number of book collectors, book score, update time, author attention parameters and/or cumulative reading time .

B15. The computing device of any of B9-B14, the executable instructions further cause the processor to perform the following operations:

Add a behavior mark to the book according to the historical behavior data; wherein, the behavior mark includes a read mark and/or a book shelf mark;

The book information of each book in the processed list and the behavior mark of each book are displayed on the book list page.

B16. The computing device of any of B9-B15, the executable instructions further cause the processor to perform the following operations:

In response to the user's turning on the read blocking switch component on the book list page, the read books of the user are removed from the books in the processed list displayed on the book list page.

C17. A computer storage medium, where at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to perform an operation corresponding to the method for processing a ranking list according to any one of A1-A8.

Claims (10)

1. A method for processing a list, comprising: Collect historical behavior data of users for the books in the list to be processed; Analyzing the behavior path depth of the historical behavior data to obtain the negative feedback level data of the user for the book; According to the negative feedback level data, the arrangement order of the books in the to-be-processed list is adjusted to obtain a processed list. 2 . The method according to claim 1 , wherein the historical behavior data comprises: exposing book behavior data, clicking book behavior data, reading book behavior data, and adding bookshelf behavior data. 3 . 3. The method according to claim 1, before the depth of the behavior path of the historical behavior data is analyzed to obtain the negative feedback level data of the user for the book, the method further comprises: according to the behavior path Deeply set multi-level feedback nodes on the user's behavior path for books; The depth analysis of the behavior path of the historical behavior data to obtain the negative feedback level data of the user for the book further includes: The behavior path depth of the historical behavior data is analyzed to obtain a feedback node corresponding to the behavior path depth, and the node data of the feedback node is used as the negative feedback level data of the user for the book. 4 . The method according to claim 3 , the multi-level feedback nodes include: feedback nodes corresponding to exposing books but not clicking books, feedback nodes corresponding to clicking books but not reading books, and feedback nodes corresponding to reading books but not adding bookshelves. 5. The method according to any one of claims 1-4, wherein according to the negative feedback level data, the arrangement order of the books in the to-be-processed list is adjusted, and the processed list is further obtained. include: Collect the initial ranking index of each book in the to-be-processed list; Determine whether the initial ranking index of any two books in the to-be-processed list is the same; If yes, adjust the recommendation weight of the book corresponding to the negative feedback level data according to the negative feedback level data, and adjust the ranking of any two books in the list to be processed according to the recommendation weight. Order; If not, according to the preset order of the initial ranking index of the list, determine the order of the any two books in the list to be processed. 6. The method according to claim 5, wherein the initial ranking index of the list comprises: book popularity value, the number of readers, the number of people who like the book, the number of people in the book collection, the book score, the update time, the author's attention parameter and/or the cumulative reading. duration. 7. The method of any one of claims 1-6, further comprising: Add a behavior mark to the book according to the historical behavior data; wherein, the behavior mark includes a read mark and/or a book shelf mark; The book information of each book in the processed list and the behavior mark of each book are displayed on the book list page. 8. The method of any one of claims 1-7, further comprising: In response to the user's turning on the read blocking switch component on the book list page, the read books of the user are removed from the books in the processed list displayed on the book list page. 9. A computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform the following operations: Collect historical behavior data of users for the books in the list to be processed; Analyzing the behavior path depth of the historical behavior data to obtain the negative feedback level data of the user for the book; According to the negative feedback level data, the arrangement order of the books in the to-be-processed list is adjusted to obtain a processed list. 10. A computer storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to perform an operation corresponding to the method for processing a ranking list according to any one of claims 1-8 .

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