CN106657581B - E-book reading plan recommendation system and method - Google Patents
E-book reading plan recommendation system and method Download PDFInfo
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- CN106657581B CN106657581B CN201610868738.9A CN201610868738A CN106657581B CN 106657581 B CN106657581 B CN 106657581B CN 201610868738 A CN201610868738 A CN 201610868738A CN 106657581 B CN106657581 B CN 106657581B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
- H04M1/72451—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to schedules, e.g. using calendar applications
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Abstract
The invention discloses a reading plan recommendation system and a method thereof for an electronic book, comprising the following steps: acquiring personal information of a target user and a book name of an electronic book currently read by the target user; constructing a similar user group according to the personal information and the book title, and acquiring reading characteristics of other users in the similar user group; reading characteristics of other users in the same type of user group are obtained, a first recommended reading plan is generated, and the first recommended reading plan is recommended to a target user; obtaining historical reading information of a target user accessing a reading book, and counting the reading behavior characteristics of the target user according to the historical reading information; and adjusting the first recommended reading plan according to the reading behavior characteristics, generating a second recommended reading plan, and recommending the second recommended reading plan to the target user. All data are based on the basic information, the reading behavior information and the personalized information of the user without the setting of the user, and meet the personalized requirements of the user.
Description
Technical Field
The invention relates to the technical field of electronic book reading, in particular to a reading plan recommendation system and method for an electronic book.
Background
In a conventional reading system, a general reading assistant manually sets a timing task through a user, and a server background sends a message prompt according to the timing task of the user to prompt the user to execute a scheduled plan next time. It has the following disadvantages: the function is single, can only play the warning function, and the condition of repeated warning often takes place moreover, under the condition that the user finishes reading, can repeatedly send the warning message. And at the same time, the reading plan cannot be reasonably arranged for the user.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a reading plan recommendation system of an electronic book, a method, a terminal and a server, and the system and the method solve the problems that how to effectively utilize the fragment time of a user in the reading process of the user, improve the effective reading of the user, intelligently divide the reading task of the server, reasonably arrange a reading plan for the user, intelligently send a reminding message, reduce the interference of message reminding on the user, help the user to finish reading and experience the whole reading process, and clear the reading progress and the reading speed of the user. The user is helped read pleasantly and effectively, and finally reading is finished.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a method for recommending a reading plan of an electronic book is characterized by comprising the following steps:
acquiring personal information of a target user and a book name of an electronic book currently read by the target user; constructing a similar user group according to the personal information and the book title, wherein the similar user group is used for collecting other users related to the personal information of the target user in a server and acquiring the reading characteristics of the other users in the similar user group;
reading characteristics of other users in the same type of user group are obtained, a first recommended reading plan is generated, and the first recommended reading plan is recommended to the target user;
obtaining historical reading information of a target user reading book, and obtaining reading behavior characteristics of the target user according to the historical reading information;
and adjusting the first recommended reading plan according to the reading behavior characteristics, generating a second recommended reading plan, and recommending the second recommended reading plan to the target user.
Preferably, the first recommended reading plan recommendation information includes: the reading starting time, the reading time length and the reading time interval, wherein the step of generating the first recommended reading plan comprises the following steps:
splitting the electronic book into a plurality of segments according to a preset electronic book splitting rule, and numbering the segments according to a reading sequence;
counting the difficulty coefficient and the wonderful degree coefficient of the electronic book segments uploaded by the same user group, generating daily reading tasks for the users by combining the reading characteristics of the same user group, distributing different numbers of content segments in each reading task, and lengthening the reading time or reducing the reading tasks for the content segments with large difficulty coefficient or large wonderful degree coefficient.
Preferably, the adjusting the first recommended reading plan according to the reading behavior characteristics is:
and adjusting the reading starting time, the reading time length and the reading time interval recommended in the first recommended reading plan according to the reading behavior characteristics of the target user.
Preferably, the method further comprises the following steps:
judging whether the currently read electronic book is read to the last chapter, if so, counting the book types interested by the target user according to the historical reading information of the target user and marking the book abstract content of one book as the reading reminding message content;
otherwise, marking the book excerpt of the subsequent section of the currently read electronic book as the content of the reading reminding message;
preferably, the method further comprises the following steps:
according to the reading behavior characteristics of the target user, sending the reading reminding message content to the target user before the normal reading starting time of the target user;
judging whether the target user completes the reading task, if so, not sending the content of the reading reminding message;
otherwise, judging whether the target user starts reading the task, if so, not sending the content of the reading reminding message;
otherwise, the reading reminding message content is sent to the target user within or after the reading time range of the target user.
Preferably, the method further comprises the following steps:
after the target user reads, counting reading information of the target user, including: reading time, the number of words which are completely read, the number of pages which are completely read and the number of chapters which are completely read, and adding statistical information into the current reading progress of a target user;
and calculating the current reading progress ranking of the target user in the same type of user group for providing transverse comparison.
Preferably, the method further comprises the following steps:
comparing the current reading progress of the target user with the second recommended reading plan;
judging whether the current reading progress is larger than the second recommended reading plan, if so, sending a reward reminding message to the target user;
and if not, judging whether the current reading progress is equal to the second recommended reading plan, if so, sending an incentive reminding message to the target user, otherwise, sending an incentive reminding message to the target user.
Preferably, the method further comprises the following steps:
the target user can modify the recommended minimum reading time per day, the total planned reading days and the operations of increasing and deleting the idle time in the recommended reading plan according to the actual conditions of the target user.
Preferably, the reading reminding message content comprises three modes of short message reminding, voice reminding and client application internal reminding.
A reading plan recommendation system for an electronic book is characterized by comprising the following modules:
the initial reading information acquisition module is used for acquiring personal information of a target user and a book name of an electronic book currently read by the target user;
the same-type reading information acquisition module is used for constructing a same-type user group according to the personal information and the book title, collecting other users related to the personal information of the target user in the server, and acquiring the reading characteristics of the other users in the same-type user group;
the first recommending module is used for acquiring the reading characteristics of other users in the same type of user group, generating a first recommended reading plan and recommending the first recommended reading plan to the target user;
the reading characteristic acquisition module is used for acquiring historical reading information of a target user accessing a reading website and acquiring the reading behavior characteristic of the target user according to the historical reading information;
and the second recommending module is used for adjusting the first recommended reading plan according to the reading behavior characteristics, generating a second recommended reading plan and recommending the second recommended reading plan to the target user.
A terminal of a reading plan recommendation system for an electronic book, the terminal comprising:
the uploading information module is used for uploading the basic information of the user to the server by the user through the network;
and the sending reminding module is used for receiving the message reminding after the message is transferred through the network, and selecting proper pushing time, message content and pushing mode to send the reading reminding message to the user.
A server of a reading plan recommendation system for an electronic book, the server comprising:
the log collection module is used for collecting daily reading behavior logs of the user by the server, transmitting log data into the big data analysis server and analyzing the daily reading behavior logs of the user;
a data analysis module: the reading characteristic statistic system is used for analyzing the user behavior data and counting the reading characteristics of the user;
a reading plan generation module: the reading system is used for arranging reading tasks, generating a reading plan and simultaneously taking charge of self-adjustment and improvement of the user plan;
the progress ranking module is used for comparing the reading progress ranking and the reading plan of the user and tracking the reading progress of the user;
and the message reminding module is used for scheduling the message push service according to the requirement and sending a reminding message to the user.
The invention has the beneficial effects that:
no user settings are required. On the premise that the user does not set related information, the background of the server side can automatically collect the reading behavior information of the user, and after analysis, a reading plan is generated for the user.
All data is based on basic information of the user, reading behavior information of the user and personalized information. The data analysis module can process the reading behavior log of the user on the same day and record the reading behavior of the user on each day. By comparing the reading behaviors of the latest time (such as 60 days), the reading characteristics of the user are counted from the reading behaviors, wherein the reading characteristics comprise reading speed, reading time interruption (fragment time) and the like, the fragment time is reasonably utilized, and customized service is performed for each user according to the characteristics of occupation, hobbies, habits and the like. Meanwhile, the reading characteristics can be changed along with the change of the reading habits of the user, and the personalized requirements of the user are met.
Drawings
FIG. 1 is a diagram of a full flow architecture for implementing the present invention;
FIG. 2 is a flow chart of a logical process of implementing the present invention;
FIG. 3 is a personal details page setup interface diagram of the present invention;
FIG. 4 is a diagram of a user reading interface of the present invention;
FIG. 5 is a diagram of a reading assistant control interface provided by the present invention;
FIG. 6 is a view of the reading assistant interface of the present invention;
FIG. 7 is a diagram of a daily reading time interface recommended by the user modification system of the present invention;
FIG. 8 is a diagram of a total reading time interface recommended by the user modification system of the present invention;
FIG. 9 is a schematic view illustrating a user viewing current book reading information according to the present invention;
FIG. 10 is a user-selected reading detail date diagram of the present invention;
FIG. 11 is a diagram of a user viewing specific read information on a specific date in accordance with the present invention;
FIG. 12 is a display diagram of an alert message of the present invention;
FIG. 13 is a schematic view of the reading interface with a reminder message according to the present invention;
FIG. 14 is a schematic view of a message interface for reminding a user of completing a reading plan during reading according to the present invention;
FIG. 15 is a flow chart of the main steps of the present invention.
Detailed Description
The invention is further illustrated below with reference to the figures and examples.
The first embodiment is as follows:
the configuration operation entrance recommended by the traditional reading plan is hidden deeply, and the configuration is troublesome. If the user needs the reminding function, the reminding function is manually set through a client (such as a mobile phone APP). The reminding pushing device is single in setting and can only be fixed at certain time points. The reminding mode and the reminding content are single, the message notification can be only carried out in the client APP, the user cannot receive effective reminding under the condition of network disorder, and the reminding message content is simple and single and cannot well stimulate the user to read; and a reading comparison and feedback presentation mode is lacked, so that the user cannot visually see the reading progress and the reading plan of the user. The reading condition of the user cannot be tracked due to the lack of user behavior analysis. Such as: reading progress, reading time, daily reading time period, reading speed, book type preference and the like.
The invention provides a reading plan recommendation system of an electronic book, a method, a terminal and a server thereof, comprising the following steps:
s101, acquiring personal information sent by a target through an intelligent terminal and a book name of an electronic book currently read by a target user;
after the user finishes registering, setting personal information through a personal detail page, wherein the personal information comprises the following steps: basic information such as birth date, sex, occupation, hobby and the like. After the user fills in the personal information, the server background collects the basic information of the user as the basic information for later-stage user data statistics. Aiming at different understanding degrees of users with different ages and sexes on things, the reading assistant can use different modes and sentences to pick the essence abstract contents from the book and send a reminding message to the users, so that the reading assistant is closer to the users.
S102, acquiring book name information of an electronic book which is currently read by a target user, constructing a similar user group according to the personal information and the book name, collecting other users related to the personal information of the target user in a server, and acquiring reading characteristics of the other users in the similar user group;
s103, reading characteristics of other users in the same type of user group are obtained, a first recommended reading plan is generated, and the first recommended reading plan is recommended to the target user;
the server background classifies the users by comparing the basic data of the target users and other reading fans, and establishes a similar user group. The same-class user group is other users related to the personal information of the target user in the server. Different reading plans are generated for the target user through the user reading attributes counted by the reading behaviors of other users, and the user is helped to reasonably arrange the reading task.
The user can also modify the daily reading time and the planned reading days recommended by the system according to the self needs, and can increase and delete the idle time according to the self actual conditions.
The step of generating the first recommended reading plan by the system is as follows: the length of the electronic book is analyzed, the content of the electronic book is divided into N content segments by taking the number of segments, pages, words and the like as units, and the N content segments are numbered in sequence according to the content sequence. And distributing daily reading tasks for the user from multiple dimensions by combining the reading characteristics of the user and the reading behavior information of the similar user, the book understanding difficulty coefficient, the wonderful degree coefficient and the like, and generating a recommended reading plan. And marking the segments with long reading time of the same type of user groups as the segments with large understanding difficulty coefficient and the segments with large collection times as the segments with large wonderful coefficient.
And tracking the reading progress of the user, comparing the reading plan, and sending a reading reminding message to the user when the user does not complete the reading task on time. By tracking the reading progress of the user and comparing the reading plan, the server can send message reminding to the user when the user deviates from the reading plan, help remind the user to finish the reading plan, and encourage the user to effectively read.
S104, obtaining historical reading information of a target user accessing a reading website, and obtaining reading behavior characteristics of the target user according to the historical reading information;
collecting the historical reading information of the daily reading books of the target user, analyzing the reading behavior log, counting and analyzing the reading characteristics of the target user, further perfecting the reading plan according to the counting result, and generating a second recommended reading plan customized for the user;
after the target user reads for a period of time, the server records the reading behavior of the user and analyzes the reading behavior of the user, such as: reading time, reading speed, reading time period and the like, and simultaneously recording characteristics of book types, fragment time and the like read by a user. And adjusting the reading plan recommended to the user according to the information, and further perfecting the reading plan.
And the server background takes the book essence content fragments which are not read by the user as a part of the content of the reminding message by counting the book type, the book essence content and the recorded book abstract content which are read by the target user at ordinary times, and is used for reminding and guiding the user to finish reading.
S105, adjusting the first recommended reading plan according to the reading behavior characteristics, generating a second recommended reading plan, and recommending the second recommended reading plan to the target user.
After basic information and reading behavior of a user are analyzed, counting reading occurrence time of the user, and sending a reading reminding message to the user by a server within the normal reading occurrence time of the user or before and after the reading sending time;
after analyzing the basic information and behavior habits of the user through the background, for example: analysis of the user's typical reading time by background statistics occurs at 12 noon: 30-13: 30, the server background will be at 12 pm: 20 (or 12: 30, 13: 00, 13: 30, 13: 40, etc.) sends the reading reminding message with the reminding message content set to the user. And when the server statistically analyzes that a reminding message needs to be sent to the user to prompt the user to finish reading, the reminding can be performed in various modes such as short message reminding, voice reminding or client application internal message notification.
And sending different message reminders to the user according to the comparison result of the reading progress and the reading plan of the user.
In the reading assistant, the daily reading plan time and the page number range of the reading plan task of the user are shown, and when the reading time is reached or the page number of the task is read, the user finishes the reading task of the day.
In the reading process of the user, the background of the server monitors the reading condition of the user, compares the reading progress of the user with the reading plan, and sends reminding messages with different properties such as reminding, encouraging, incentive and rewarding to the user according to different comparison results so as to encourage the user to finish reading. Such as: after the user reads for a long time, the server background sends a rest prompt to the user; when the reading progress of the user is faster than the reading plan, the server background sends a reward prompt to the user to encourage the user to continue to read carefully and effectively; and sending an incentive prompt to the user in combination with the user self condition under the condition that the user does not finish the reading plan on the same day, so as to remind the user to finish the contents of the plan and perform careful and effective reading.
Advantages of the invention
The user may not need to set up. On the premise that the user does not set related information, the background of the server automatically collects information, and generates a reading plan for the user after analysis;
all data is based on basic information of the user, user behavior information and personalization information. Reasonably utilizing the fragment time, and performing customized service for each user according to the characteristics of occupation, hobbies, habits and the like;
and a reading plan is automatically generated to help the user finish reading. Generating a customized reading plan for the user, so that the user can perform self-comparison;
in the reading process, a horizontal comparison is provided for the user, and the ranking of all reading users in the same book by the current user is calculated. After reading is finished, summarizing the whole reading process of the user;
and sending different types of reminding messages to the user according to different reading progress conditions. The method comprises the steps of sending reward information to users who are reading actively, sending encouragement information to common users, sending incentive information to users who are not reading frequently, and motivating the users to develop reading hobbies;
and intelligently sending a prompt. Such as: when the user finishes a reading plan on the same day, and the user suddenly interrupts reading and the like, the server background does not send message reminding, so that the current situation of the user is prevented from being disturbed, the current work of the user is interrupted, and reminding is sent to the user at the later stage to prompt the user to read;
and a plurality of reminding modes. The ability to avoid reading aids is limited by network limitations.
The message content is simplified and enriched. And sending content with the properties of prompting, encouraging, exciting and rewarding to the user in combination with the reading condition of the user. Such as: reading the essence book abstract of the subsequent section of the book, and inspiring the famous languages and the like.
Example two:
the electronic book reading recommendation system provided by the invention provides a method for intelligently generating a reading plan and sending a message recommendation prompt. The user does not need to manually configure, the background server analyzes and processes the user log data and the user basic information automatically, fragment time and split reading tasks are reasonably utilized, a proper reading plan is generated for the user, and a proper reminding mode and reminding content are selected for the user.
Because the reading process of the user is a regular process, such as:
1) searching books which are interested by the user according to the basic information of the user, such as interests, hobbies, occupation and the like;
2) reading time is not fixed and fragmented, and not all users can arrange a long time period every day to efficiently and seriously read;
3) reading speed, periodic variation in length of reading time.
Aiming at the characteristics, the reading assistant can effectively help the user to fully utilize the fragment time, reasonably segment the reading task through background data analysis, and generate a proper reading plan for the user. And by analyzing the user behavior, the reading reminding is sent to the user at irregular time, so that the user is encouraged to read. If the server background detects that the user finishes reading in advance, the reminding is not performed any more, and the interference of unnecessary reminding messages is reduced.
Aiming at the understanding degree of people of different ages and sexes on things, the server background collects basic information of users, such as: basic information such as age, gender, hobbies, occupation, etc. And then sending a reminding message to the user by using different reminding modes and reminding contents according to the information. And the essence content or the book abstract content of the subsequent chapters which are not read by the user is picked from the book to be used as the reminding message content, so that the book is closer to the user and guides the user to read.
The server background can always analyze the daily reading behavior of each user and track the reading progress of the user, so that reading message prompts at different stages are sent to the user, a user recommended reading plan is perfected, the user is helped to effectively read in different modes, and the user is encouraged and encouraged to complete a reading task.
The method for acquiring the basic information of the user in the system comprises the following steps: the user enters a user basic information setting page, and can set own basic information in the page. And the server background acquires the basic information of the user from the information filled by the user.
The method for the background statistical analysis of the user behavior of the server comprises the following steps: after the user reads daily, the server background records the characteristics and attributes of the user such as reading behavior, reading book type, fragment time and the like, and analyzes the daily reading time, reading speed, reading time period and reading habit of the user.
The system reminding time generation method comprises the following steps: and the server background counts the reading habits of the user by analyzing the reading plan of the user. And determining the message pushing time point according to the concentrated reading time of the user every day.
The reading plan generating method of the system comprises the following steps: the server analyzes the length of each electronic book, counts user behavior information, and divides the content into 0 to N stages by taking the number of segments, pages and words as units in cooperation with the idle time of the user. An independent reading plan is generated for each user recommendation by combining the reading characteristics (such as reading speed and reading book type) of the users. The reading plan is then compared by tracking the user's reading progress. And when the user deviates from the reading plan, the server background sends a message prompt to the user. The reading plan is completed by the user, and the user is encouraged to effectively read.
The system sending reminding method comprises the following steps: when the server statistically analyzes that a reminding message needs to be sent to the user to prompt the user to finish reading, the server can use various modes such as short message reminding, voice reminding, client application internal message notification and the like.
The reading assistant setting method comprises the following steps: and popping up a reading assistant interface before the user finishes the first reading and returns to the main interface. Or the user clicks a reading assistant button in a tool bar of the reading main interface to enter the reading assistant interface.
The method for the system to respond to the user click message comprises the following steps:
1. when the push message is a reading reminding message, directly entering a reading interface after clicking the reminding message;
2. when the push message is an incentive and a reward message for the user to finish reading, the user can click to continue reading and leave in the reading interface, or click other buttons to enter other interfaces.
The reading task presentation method comprises the following steps: in the reading assistant, the reading plan time and the range of the number of pages of the reading plan task of the user each day are shown. In the case that the user finishes the reading time of the task or finishes the number of pages of the task, the user finishes the reading task of the day.
The system sends the reminding type as follows: and the server background compares the reading progress with the reading plan and sends different reminding messages according to different comparison results. Such as: after the user reads for a long time, the reading assistant sends a rest system to the user; when the reading progress of the user is faster than the reading plan, the server background sends rewards to the user for reminding, and the user is encouraged to continue to read carefully and effectively; and sending a prompt to the user when the user does not finish the reading plan on the day, reminding the user of finishing the contents of the plan, and the like. The user is encouraged to read carefully and effectively by combining the self condition of the user, the current reading progress and other aspects, the reading level is improved, and the user obtains fun from reading.
Example three:
as shown in fig. 3, after the user finishes registration, the user sets basic personal information through a personal details page, wherein basic information such as birth date, gender, occupation, hobbies and the like of the user is filled in a 301 area, and the area is clicked 302 for saving.
After the user completes the basic information, the server background collects the basic information of the user as the basic data of later-stage statistical user data.
As shown in FIG. 4, the user clicks on the center area of the reading interface 401 and pops up a toolbar.
As shown in FIG. 5, the user clicks the reading assistant control in the area 502, or after the user reads the book for the first time, clicks the return button in the area 501, and the reading assistant interface automatically pops up.
As shown in fig. 6, the server background generates a recommended reading plan for the user by comparing the big data of the current user with the big data of other user profiles. In fig. 6: the area 602 is the daily reading scheduled time recommended by the system to the user, and the area 603 is the total number of days of reading completion recommended by the system to the user. In fig. 6, the user can delete and increase the free time of reading each day by clicking the buttons 604 and 605, and click the area 606 to save the reading plan after the modification is completed. At the same time, the user can turn on or off the reading assistant function in the area 601, click the area 602, and enter the interface of fig. 7.
As shown in fig. 7, the user may modify the planned daily reading time recommended by the system.
Click 603 area to enter the interface of fig. 8.
As shown in fig. 8, the user may modify the planned reading days recommended by the system.
After the user reads for a period of time, daily reading behavior logs of the user are collected, the daily reading behavior of the user is analyzed, the server further perfects a reading plan according to an analysis result, and finally an intelligent customized reading plan without user intervention is generated.
Clicking 607 on the area in fig. 6 enters the user reading progress interface.
As shown in fig. 9, the user reading situation is shown, from which we can see the whole reading process of the user, and the user is stimulated to read:
a. the user starts reading the book on day 6.30;
b. the user forgets to read at 7.7;
c.7.8-7.14 do not complete the reading planning task for the day
D.7.15-7.25 excess completed the reading plan
e. Reading is completed at 7.26
In fig. 9, the date jumper in area 901 is dragged and the date selection interface appears.
As shown in fig. 10, after selecting a specific date, the reading of the user for the current day is shown.
As shown in FIG. 11, the user views details of 2016-07-11, such as planning a task, completing a task, and comprehensively ranking, and clicks 1101 an area determination button to return to the initial interface (FIG. 9). Initial interface in fig. 9, the bar is at the date of the day or the end date of reading and does not display the reading statistics for the day.
After analyzing the basic information and behavior habits of the user through the background, for example: analysis of the user's typical reading time by background statistics occurs at 12 noon: 30-13: 30, the server background will be at 13 pm: 00 sending a reading reminding message to the user.
As shown in fig. 12, a display mode of the reminder message is shown, and after clicking 1201 area, the user will directly enter the reading interface, as shown in fig. 13.
In addition, according to different network conditions of the user, besides pushing the message notification in the application to the user, the method can also send a reminding short message, a reminding voice and other modes to the user, and the interference of the pushing of the message notification by environmental factors is reduced.
And counting the book abstract content and the book essence content by analyzing the books and the book reading data of the user at the background server according to the reminding message content. In the process of sending the message reminding to the user, the reminding message with the properties of reminding, encouraging, motivation, reward and the like is sent to the user in combination with the reading condition of the user. And (5) encouraging and encouraging the user to finish reading.
When the user finishes the reading task of the day, the background service sends a task completion message prompt to the user and gives the user a little reward (such as points).
As shown in fig. 14: the user clicks the 1401 region to continue reading, and the interface returns to the reading interface; after the user clicks 1402 area to rest for a meeting, the interface jumps to other content interfaces of APP.
What has been described above is only a preferred embodiment of the present invention, and the present invention is not limited to the above examples. It is to be understood that other modifications and variations directly derivable or suggested by those skilled in the art without departing from the basic concept of the present invention are to be considered as included within the scope of the present invention.
Claims (10)
1. A method for recommending a reading plan of an electronic book is characterized by comprising the following steps:
acquiring personal information of a target user and a book name of an electronic book currently read by the target user;
constructing a similar user group according to the personal information and the book name, and collecting other users related to the personal information of the target user in a server;
reading characteristics of other users in the same type of user group are obtained, a first recommended reading plan is generated, and the first recommended reading plan is recommended to the target user;
obtaining historical reading information of a target user accessing a reading book, and obtaining reading behavior characteristics of the target user according to the historical reading information;
and adjusting the first recommended reading plan according to the reading behavior characteristics, generating a second recommended reading plan, and recommending the second recommended reading plan to the target user.
2. The reading plan recommendation method according to claim 1, wherein the first recommended reading plan recommendation information includes: the reading starting time, the reading time length and the reading time interval, wherein the step of generating the first recommended reading plan comprises the following steps:
splitting the electronic book into a plurality of segments according to a preset electronic book splitting rule, and numbering the segments according to a reading sequence;
counting the difficulty coefficient and the wonderful degree coefficient of the electronic book segments uploaded by the same user group, generating daily reading tasks for the users by combining the reading characteristics of the same user group, distributing different numbers of content segments in each reading task, and lengthening the reading time or reducing the reading tasks for the content segments with large difficulty coefficient or large wonderful degree coefficient.
3. The reading plan recommendation method according to claim 1 or 2, wherein adjusting the first recommended reading plan according to the reading behavior characteristics is:
and adjusting the reading starting time, the reading time length and the reading time interval recommended in the first recommended reading plan according to the reading behavior characteristics of the target user.
4. The reading plan recommendation method according to claim 1, further comprising:
judging whether the currently read electronic book is read to the last chapter, if so, counting the book types interested by the target user according to the historical reading information of the target user and marking the book abstract content of one book as the reading reminding message content;
otherwise, marking the book excerpt of the subsequent section of the currently read electronic book as the content of the reading reminding message.
5. The reading plan recommendation method according to claim 1, further comprising:
according to the reading behavior characteristics of the target user, sending the reading reminding message content to the target user before the normal reading starting time of the target user;
judging whether the target user completes the reading task, if so, not sending the content of the reading reminding message;
otherwise, judging whether the target user starts reading the task, if so, not sending the content of the reading reminding message;
otherwise, the reading reminding message content is sent to the target user within or after the reading time range of the target user.
6. The reading plan recommendation method according to claim 1, further comprising:
after the target user reads, counting reading information of the target user, including: reading time, the number of words which are completely read, the number of pages which are completely read and the number of chapters which are completely read, and adding statistical information into the current reading progress of a target user;
and calculating the current reading progress ranking of the target user in the same type of user group for providing transverse comparison.
7. The reading plan recommendation method according to claim 1, further comprising:
comparing the current reading progress of the target user with the second recommended reading plan;
judging whether the current reading progress is larger than the second recommended reading plan, if so, sending a reward reminding message to the target user;
and if not, judging whether the current reading progress is equal to the second recommended reading plan, if so, sending an incentive reminding message to the target user, otherwise, sending an incentive reminding message to the target user.
8. The reading plan recommendation method according to claim 1, further comprising:
the target user can modify the recommended minimum reading time per day, the total planned reading days and the operations of increasing and deleting the idle time in the recommended reading plan according to the actual conditions of the target user.
9. The reading plan recommendation method according to claim 4 or 5, wherein the reading reminding message content comprises three modes of short message reminding, voice reminding and client application internal reminding.
10. A reading plan recommendation system for an electronic book is characterized by comprising the following modules:
the initial reading information acquisition module is used for acquiring personal information of a target user and a book name of an electronic book currently read by the target user;
the same-type reading information acquisition module is used for constructing a same-type user group according to the personal information and the book title and collecting other users related to the personal information of the target user in the server;
the first recommending module is used for acquiring the reading characteristics of other users in the same type of user group, generating a first recommended reading plan and recommending the first recommended reading plan to the target user;
the reading characteristic obtaining module is used for obtaining historical reading information of a target user reading books and obtaining reading behavior characteristics of the target user according to the historical reading information;
and the second recommending module is used for adjusting the first recommended reading plan according to the reading behavior characteristics, generating a second recommended reading plan and recommending the second recommended reading plan to the target user.
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| CN201610868738.9A CN106657581B (en) | 2016-09-30 | 2016-09-30 | E-book reading plan recommendation system and method |
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| CN201610868738.9A CN106657581B (en) | 2016-09-30 | 2016-09-30 | E-book reading plan recommendation system and method |
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