CN113111163A

CN113111163A - Word recommendation method and device and computing equipment

Info

Publication number: CN113111163A
Application number: CN202110535752.8A
Authority: CN
Inventors: 魏晨; 王尚; 冯伟东; 朱君妍; 薛颜
Original assignee: Nanjing Beiwan Information Technology Co ltd
Current assignee: Nanjing Beiwan Information Technology Co ltd
Priority date: 2021-05-17
Filing date: 2021-05-17
Publication date: 2021-07-13

Abstract

The invention discloses a word recommendation method, which is suitable for being executed in computing equipment and comprises the following steps: recommending a course word book matched with the course for the user based on the course currently learned by the user; determining a first number of words to be reviewed from the words that have been recited by the user based on a predetermined algorithm; when the word book currently recited by the user is the curriculum word book, taking a word list associated with the next-day curriculum content from the curriculum word book, and screening out words which are not recited by the user from the word list; determining a second number of words to be recited based on the screened out words not recited by the user; and recommending the determined words to be reviewed and the words to be recited to the user. The invention also discloses a corresponding device and a computing device.

Description

Word recommendation method and device and computing equipment

Technical Field

The invention relates to the field of internet, in particular to a word recommendation method and device and a computing device.

Background

In recent years, with the development of internet education, the number of users who learn a foreign language course using the internet has increased remarkably, and word reciting is an essential important link in foreign language learning, and thus many applications related to word reciting are also promoted on the market. Typically, such applications recommend word books for various examinations to the user and mechanically recite the words in the word books in order to the user. However, the words and courses recited by the user (e.g., examination courses, skill courses, problem courses) are split, and there is no guarantee that the user's progress in learning words is consistent with the courses, resulting in a great loss of motivation and effectiveness in reciting.

The existing scheme can not efficiently recommend words for the user by combining the progress of the curriculum learned by the user and the learning condition of the user. In view of the foregoing, there is a need for an improved word recommendation scheme for accurately recommending words suitable for the learning progress of the user.

Disclosure of Invention

To this end, the present invention provides a method, apparatus and computing device for word recommendation in an attempt to solve or at least alleviate the above-identified problems.

According to an aspect of the present invention, there is provided a word recommendation method adapted to be executed in a computing device, the method comprising the steps of: recommending a course word book matched with the course for the user based on the course currently learned by the user; determining a first number of words to be reviewed from the words that have been recited by the user based on a predetermined algorithm; when the word book currently recited by the user is the curriculum word book, taking a word list associated with the next-day curriculum content from the curriculum word book, and screening out words which are not recited by the user from the word list; determining a second number of words to be recited based on the screened out words not recited by the user; and recommending the determined words to be reviewed and the words to be recited to the user.

Optionally, in the method according to the present invention, the step of determining a first number of words to be reviewed based on a predetermined algorithm from the words that have been recited by the user comprises: determining the familiarity of the user with the words based on feedback from the user on the words during recitation of the words; based on the familiarity and the recitation interval, acquiring a ranking index of each word in the recited words, wherein the recitation interval is the number of days from the last time the word was recited on the same day, and the higher the familiarity, the larger the ranking index, the larger the recitation interval, and the larger the ranking index; a first number of words to review is determined from the words that the user has recited based on the ranking indicator.

Optionally, in the method according to the present invention, the step of determining the familiarity of the user with the word based on the user's feedback on the word during recitation of the word comprises: when the feedback of the user to the word is known, increasing the familiarity of the user to the word by a first preset value; and when the feedback of the user to the word is unknown, increasing the error coefficient of the user to the word by a second preset value, feeding back the feedback that the user is still unknown after receiving the prompt, further increasing the error coefficient of the word by a third preset value, and determining the familiarity of the user to the word based on the error coefficient, wherein the familiarity is in negative correlation with the error coefficient.

Optionally, in the method according to the present invention, the second predetermined value is 1, the third predetermined value is 0.5, and the step of determining the familiarity of the user with the word based on the error coefficient includes: if the error coefficient of the word is 0, increasing the familiarity of the word by 3; if the error coefficient of the word is greater than 0 and less than 2, increasing the familiarity of the word by 2; if the error coefficient of a word is greater than or equal to 2, the familiarity of the word is increased by 1.

Optionally, in the method according to the present invention, the step of determining a first number of words to be reviewed from the words that have been recited by the user based on the ranking indicator comprises: the first number of words to be reviewed are retrieved from the words that the user has recited in order of descending ranking index.

Optionally, in the method according to the present invention, the ranking index is obtained by: x is retention-0.0001/(min (20, Δ day) +10), where x is the ranking index, retention is familiarity, and Δ day is recitation interval.

Optionally, in a method according to the present invention, the step of determining a second number of words to be recited based on the screened user unrerecited words comprises: selecting a second number of words from the unrerecited words if the number of unrerecited words is greater than or equal to the second number; if the number of unrerecited words is less than the second number, then all unrerecited words are selected and a third number of words is selected from the lesson word book, the third number being the difference between the second number and the number of unrerecited words.

Optionally, in the method according to the present invention, before determining the second number of words to be recited based on the screened out words not recited by the user, further comprising: when the word book currently recited by the user is not a curriculum word book, words not recited by the user are screened from the word book currently recited by the user.

Optionally, in the method according to the present invention, the step of recommending the determined word to be reviewed and the word to be recited to the user includes: firstly, recommending words to be reviewed, and then recommending words to be recited to a user.

According to another aspect of the invention, there is provided an apparatus for word recommendation, residing in a computing device, the apparatus comprising: the word book recommending unit is suitable for recommending curriculum word books matched with curriculum for the user based on the curriculum currently learned by the user; a word selection unit adapted to determine a first number of words to be reviewed from the words that have been recited by the user based on a predetermined algorithm; when the word book currently recited by the user is the curriculum word book, taking a word list associated with the next-day curriculum content from the curriculum word book, and screening out words which are not recited by the user from the word list; and determining a second number of words to be recited based on the screened out words not recited by the user; and the word recommending unit is suitable for recommending the determined words to be reviewed and the words to be recited to the user.

According to yet another aspect of the present invention, there is provided a computing device comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions comprising instructions for performing the word recommendation method according to the present invention.

According to the technical scheme of the invention, based on the word book selected by the user, the words are recommended efficiently for the user to recite by combining the reciting and reviewing targets set by the user. When the user selects the word book matched with the course, the next-day course is combined, and the new words suitable for the learning progress of the user are accurately and efficiently recommended for the user to recite. And recommending the proper words for the user to review based on a predetermined algorithm in combination with the user's familiarity with the words and the time interval between reciting the words. According to the scheme, the problems that when the user recites the word currently, the word recommended to recite is disjointed with courses, the recommended word is not matched with the learning progress, and the recommending accuracy of the word review is not high are solved, the word recommending precision is improved, the proper word is efficiently recommended by combining the learning progress of the user, and the efficiency and the effect of reciting the user are improved.

The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.

Drawings

To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which are indicative of various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description read in conjunction with the accompanying drawings. Throughout this disclosure, like reference numerals generally refer to like parts or elements.

FIG. 1 shows a block diagram of a computing device 100, according to one embodiment of the invention;

FIG. 2 illustrates a block diagram of a word recommendation system 200 according to one embodiment of the invention;

FIG. 3 shows a flow diagram of a word recommendation method 300 according to one embodiment of the invention;

FIG. 4 shows a detailed flow diagram of a word recommendation method according to one embodiment of the invention;

fig. 5 shows a schematic structural diagram of a word recommending apparatus 500 according to another embodiment of the present invention.

Detailed Description

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 to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

FIG. 1 is a block diagram of a computing device 100 according to one embodiment of the invention. In a basic configuration 102, computing device 100 typically includes system memory 106 and one or more processors 104. A memory bus 108 may be used for communication between the processor 104 and the system memory 106.

Depending on the desired configuration, the processor 104 may be any type of processing, including but not limited to: a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 104 may include one or more levels of cache, such as a level one cache 110 and a level two cache 112, a processor core 114, and registers 116. The example processor core 114 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 118 may be used with the processor 104, or in some implementations the memory controller 118 may be an internal part of the processor 104.

Depending on the desired configuration, system memory 106 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 106 may include an operating system 120, one or more applications 122, and program data 124. In some embodiments, application 122 may be arranged to operate with program data 124 on an operating system. The program data 124 includes instructions, and in the computing device 100 according to the present invention, the program data 124 contains instructions for performing the word recommendation method 300.

Computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to the basic configuration 102 via the bus/interface controller 130. The example output device 142 includes a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices, such as a display or speakers, via one or more a/V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 158. An example communication device 146 may include a network controller 160, which may be arranged to facilitate communications with one or more other computing devices 162 over a network communication link via one or more communication ports 164.

A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media, such as carrier waves or other transport mechanisms, in a modulated data signal. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media.

Computing device 100 may be implemented as a server, such as a file server, a database server, an application server, a WEB server, etc., or as part of a small-form factor portable (or mobile) electronic device, such as a cellular telephone, a Personal Digital Assistant (PDA), a personal media player device, a wireless WEB-watch device, a personal headset device, an application specific device, or a hybrid device that include any of the above functions. Computing device 100 may also be implemented as a personal computer including both desktop and notebook computer configurations. In some embodiments, the computing device 100 is configured to perform the word recommendation method 300.

FIG. 2 shows a block diagram of a word recommendation system 200 according to one embodiment of the invention. As shown in FIG. 2, the word recommendation system 200 includes a server 210 and a user computing device 230. Generally speaking, a user may interact with the server 210 with a corresponding user account by operating the user computing device 230. Where computing device 100 may communicate with server 210 over one or more networks 220, such as a Local Area Network (LAN) or a Wide Area Network (WAN) like the internet. The network 220 may be any type or combination of communication network and may include any number of wired or wireless links. In general, communications through the network 220 may be carried using various communication protocols (e.g., TCP/IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML, JSON), and/or protection schemes (e.g., VPN, HTTPS, SSL) via any type of wired and/or wireless connection. Server 210 may include one or more server computing devices. Where the server 210 includes multiple server computing devices, the server computing devices may operate according to a sequential computing architecture, a parallel computing architecture, or some combination thereof.

The user computing device 230 may be any type of computing device, including but not limited to a personal computing device (e.g., desktop computer, notebook computer, etc.), a mobile computing device (e.g., cell phone, tablet computer, etc.), a gaming console or controller, a smart wearable device, an embedded computing device, an edge computing device, or any other type of computing device. The user computing device 230 may be deployed as a smart device at a user site and interact with a user to process user input. In one embodiment, the user computing device 230 may be a computing device equipped with a word recitation client where the recommended words are retrieved by the user computing device and displayed, and may also be used to retrieve word books from a server for recommendations and recommend words based on the user's word mastering and recitation.

In embodiments of the present invention, server 210 may store or include one or more computing programs for methods of recommending words that a user may invoke in computing device 100 to obtain recommended words.

FIG. 3 shows a flow diagram of a word recommendation method 300 according to one embodiment of the invention. The method 300 is performed in a computing device, such as the computing device 100 described above. The word recommendation method of fig. 3 can be understood in conjunction with fig. 4. As shown in fig. 3, the method 300 begins at step S310.

In step S310, based on the curriculum currently learned by the user, a curriculum word book associated with the curriculum is recommended for the user.

According to one embodiment, the user may be recommended to select based on one or more word books corresponding to the curriculum currently being learned by the user, i.e., the curriculum word books associated with the curriculum. Based on the selection of the user to the word book, words more suitable for the user to recite and review consolidation are recommended to the user in a more targeted manner. Alternatively, the word book is stored in a server, or may be downloaded to the user's computing device at the user's option.

In one embodiment, when the curriculum contents acquired to the user are changed, words matched with the changed curriculum contents are recommended to the user correspondingly so as to provide words highly related to the curriculum contents learned by the user on the same day.

In step S320, a first number of words to be reviewed is determined from the words that the user has recited based on a predetermined algorithm.

First, the words that the user has recited, i.e., words that were recited in the past, are retrieved to select a number of words therefrom as the words to be reviewed. Typically, the user's past recitation of the learned word is recorded and stored in the user's device or server. Alternatively, the recited word may be recalled from the user's historical recitation word record.

Then, according to one embodiment, a ranking indicator for each of the words that have been recited is obtained based on familiarity and recitation intervals, wherein the recitation intervals are days from the last time the word was recited on the day, wherein the higher the familiarity, the greater the ranking indicator; the larger the recitation interval, the larger the ranking index.

Specifically, the familiarity of each word is generated based on the user's feedback on the word during recitation of the word. Alternatively, when the user learns a word, the familiarity and error coefficient of the word are initial values of zero. According to one embodiment, recitation learning of words can be done in a selective manner, for example, giving options such as "know", "not know", "prompt" for user selection. For one or more words, when the user selects the option of "know" (or other options equivalent to the user's understanding of the word, such as "know", "too simple"), feedback of the user to the word is obtained as a knowledge, the familiarity of the word is increased by a first predetermined value; when the user selects the option of "not knowing" (or other options equivalent to the user not knowing the word, such as "uncertain" or "unclear"), feedback is obtained that the user does not know the word, then the error factor for the word by the user is increased by a second predetermined value. And after the word which is fed back by the user to be unknown is obtained, prompting is carried out to the user or after the user selects a prompting option, if the user still selects 'does not remember' (or other options which are equivalent to the meaning of the word which is not remembered by the user, such as 'do not know' and 'do not know') after the prompting, and the error coefficient of the word is further increased by a third preset value after the user receives the feedback that the user does not know after the prompting. If the user selects "think of" (or other options equivalent to the user's meaning, such as "know") after the prompt, and feedback is obtained that the user is aware after receiving the prompt, the familiarity and error coefficient of the word remain unchanged, wherein the familiarity and error coefficient are inversely related. The first predetermined value is higher than the second predetermined value, which is higher than the third predetermined value. Alternatively, the first predetermined value may be set to a higher value than the second predetermined value. Preferably, the first predetermined value is 1000, the second predetermined value is 1, and the third predetermined value is 0.5.

For a word containing an error coefficient, if the error coefficient of the word is 0, increasing the familiarity of the word by 3; if the error coefficient of a word is greater than 0 and less than 2, increasing the familiarity of the word by 2; if the error coefficient for a word is greater than or equal to 2, the familiarity of the word is increased by 1. In some cases, a word may reappear or recur during review, and the user's familiarity with the word may be accumulated over the calculations based on the familiarity the word has generated.

A recitation interval for each word is obtained, the recitation interval being the number of days that the word was last recited that is the current day, e.g., the user recites a word yesterday, then the recitation interval for the word today is 1 day. The recitation interval is one of the important indexes for calculating the ranking index so as to determine words recommended to the user for review according to the ranking index. Based on the forgetting rule of the user in learning, after the user finishes learning words, the forgetting speed is very high at the beginning, and the forgetting speed is reduced along with the time. The recitation interval is used as an index to help a user to repeat words at intervals, namely, when the fact that the user forgets a word is predicted, the word is reviewed in time. Optionally, the number of days corresponding to the difference between the current time and the time the user last spoken or reviewed a word is retrieved is obtained.

Alternatively, the ranking index may be obtained by:

x＝retention-0.0001/(min(20,Δday)+10)

where x is the ranking index, retentivity is the familiarity, and Δ day is the recitation interval.

In this formula, min (20, Δ day) represents taking the minimum value of 20 and Δ day, and therefore min (20, Δ day) is less than or equal to 20. According to the forgetting rule of the user, the influence of the recitation interval on forgetting is less changed along with the increase of the time of the recitation interval. For example, when reciting interval is between 1 day and 2 days, reciting interval has a large impact on forgetting; the recitation interval is between 20 and 30 days, and the influence of the recitation interval on forgetting is small. Thus, if the recitation interval is longer, then the recitation interval has a lesser degree of influence on forgetting. To avoid the word recommendation model overestimating the effect of recitation intervals on forgetting, an upper limit of 20 was set for this parameter of recitation intervals. Optionally, the parameter (upper limit) may be adjusted, or the parameter may be intelligently adjusted based on the forgetting rate of the user reciting the word in the past or the user may adjust himself in combination with his own memory.

Subsequently, for the retrieved words that the user has recited, a ranking indicator is generated for each of the words. And according to the review task amount set by the user, namely the number (first number) of the words reviewed every day set by the user, and the order of the ranking indexes from small to large, the words with the number as the review task amount are obtained as the words to be reviewed. Optionally, the word to be reviewed is added to the present-day recitation word sequence.

In step S330, when the word book currently recited by the user is the lesson word book, the word list associated with the next day lesson content is retrieved from the lesson word book, and words not recited by the user are screened out from the word list.

Specifically, when the word book currently recited by the user is a course word book matched with the course currently learned by the user, a word list associated with the next-day course content is taken from the course word book, wherein the course word book has a plurality of word lists, and each word list corresponds to each course or daily content in the course. And taking out the word list associated with the contents of the course on the next day so that the user recites the words of the course on the next day in advance, helping the user to remove vocabulary barriers in the course learning and improving the learning efficiency and effect of the user. Optionally, the word list associated with the course content of the next day may be changed to the word list associated with the course content of the current day or the word list associated with a certain course to be learned by the user, which is obtained by combining with the user behavior analysis.

Through screening out the word that the user has not recited from the word list, avoid reciting the word as new word repeated reciting, further improve the efficiency that the user carried the word. Optionally, the word list is filtered based on the user's recited words. If a word in the word list is simultaneously present in the recited word, the word is removed from the word list.

In step S340, a second number of words to be recited is determined based on the screened out words not recited by the user.

According to one embodiment, a quantitative relationship is obtained between the number of unrerecited words and the user-set number of pronouncing tasks, i.e., the user-set number of daily recitations (second number).

Specifically, if the number of unrerecited words is greater than or equal to the second number, selecting a second number of words from the unrerecited words; optionally, a second number of words is selected in the order in which the unrerecited words are in their corresponding word list associated with the next day lesson content. Optionally, a second number of words is selected from the recited words in order of high word frequency to low word frequency. Optionally, a second number of words to be recited is added to the sequence of today's recitation words.

If the number of unrerecited words is less than the second number, then all unrerecited words are selected and a third number of words is selected from the lesson word book, wherein the third number is the difference between the second number and the number of unrerecited words. Optionally, the second and third numbers of words to be recited are added to the sequence of today's reciting words.

Optionally, when the number of unrerecited words is less than the second number, when a third number of words is retrieved from the remaining words that have not been recited by the user in the lesson word book after all of the unrerecited words are retrieved, if the number of remaining words is less than the third number, then all of the remaining words are retrieved or a new word book is recommended for the user.

In step S350, the determined word to be reviewed and the word to be recited are recommended to the user.

Specifically, the words to be reviewed and the words to be recited determined in the above steps are recommended to the user. Optionally, the word to be reviewed may be preferentially recommended to the user, or the word to be reviewed or the word to be recited may be preferentially recommended according to a user-defined mode. Optionally, words in the sequence of today's recitation words are recommended to the user.

Subsequently, after the user has completed the first and second number of tasks of recitation and review, the user is optionally presented with an option to "re-learn a set" or "increase the task volume" and, based on the user's selections, the steps described in method 300 are repeated to tailor the recommended word based on the user's learning habits.

According to another embodiment, if the word book selected by the user is not a lesson matched word book, then the method according to step S320 determines the first number of words to be reviewed from the words that the user has recited based on a predetermined algorithm. And selecting words with the second number from the word books currently recited by the user as words to be recited according to the word generation task amount (the second number) set by the user, and recommending the words to be reviewed and the words to be reviewed to the user.

FIG. 5 shows a block diagram of a word recommendation system 500 according to one embodiment of the invention.

The word recommendation apparatus 500 shown in fig. 5 includes a word book recommendation unit, a word extraction unit, and a word recommendation unit.

The word book recommending unit is suitable for recommending curriculum word books matched with curriculum for the user based on the curriculum currently learned by the user. Specifically, the method as described in step S310 may be employed.

A word selection unit adapted to determine a first number of words to be reviewed from the words that have been recited by the user based on a predetermined algorithm; when the word book currently recited by the user is the curriculum word book, taking a word list associated with the next-day curriculum contents from the curriculum word book, and screening out words which are not recited by the user from the word list; and determining a second number of words to be recited based on the screened out words not recited by the user. Specifically, the method as described in steps S320-S340 may be employed.

And the word recommending unit is suitable for recommending the determined words to be reviewed and the words to be recited to the user. Specifically, the method as described in step S350 may be employed.

The details of the word recommendation apparatus 500 according to the present invention are disclosed in detail in the description based on fig. 1 to 4, and are not described herein again.

According to the technical scheme of the invention, word books matched with the current learning courses are recommended to the user, and a customized recommended word list is generated for the user according to the learning task amount (including review task amount and new word task amount) set by the user in combination with the course learning progress of the user and the learning habits of the user. By adopting the word recommendation method adaptive to the course progress, the user is helped to clear word generation obstacles in course learning, and meanwhile, the course learning efficiency and effect of the user are further improved. The words to be reviewed generated based on the predetermined algorithm can efficiently and accurately predict the words which are probably forgotten by the user, recommend the words to be reviewed urgently for the user, and improve the reciting effect of the user. The words to be recited selected based on the course matching word book can be combined with the learning condition of the user, the formulated target and the learning habit to generate words with high matching degree with the learning progress of the user so as to assist the user in learning the course.

A5: the method of a2, wherein the step of determining a first number of words to be reviewed from the words that have been recited by the user based on the ranking indicator comprises: and acquiring a first number of words to be reviewed from the words recited by the user according to the sequence of the ranking indexes from small to large.

The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as removable hard drives, U.S. disks, floppy disks, CD-ROMs, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention.

In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to execute the word recommendation method of the present invention according to instructions in the program code stored in the memory.

By way of example, and not limitation, readable media may comprise readable storage media and communication media. Readable storage media store information such as computer readable instructions, data structures, program modules or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of readable media.

In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with examples of this invention. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.

In the description provided herein, numerous specific details are set forth. It is 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 should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: 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 appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any 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 while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

Furthermore, some of the described embodiments are described herein as a method or combination of method elements that can be performed by a processor of a computer system or by other means of performing the described functions. A processor having the necessary instructions for carrying out the method or method elements thus forms a means for carrying out the method or method elements. Further, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.

As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense with respect to the scope of the invention, as defined in the appended claims.

Claims

1. A word recommendation method adapted to be executed in a computing device, the method comprising the steps of:

recommending a course word book matched with the course for the user based on the course currently learned by the user;

determining a first number of words to be reviewed from the words that have been recited by the user based on a predetermined algorithm;

when the word book currently recited by the user is the curriculum word book, taking a word list associated with the next-day curriculum contents from the curriculum word book, and screening out words which are not recited by the user from the word list;

determining a second number of words to be recited based on the screened out words not recited by the user; and

recommending the determined words to be reviewed and the words to be recited to the user.

2. The method of claim 1 wherein the step of determining a first number of words to be reviewed based on a predetermined algorithm from the words recited by the user comprises:

determining the familiarity of the user with the words based on feedback from the user on the words during recitation of the words;

obtaining a ranking indicator for each of the recited words based on familiarity and recitation intervals, the recitation intervals being the number of days that the word was last recited on the same day, wherein the higher the familiarity the greater the ranking indicator, the greater the recitation intervals the greater the ranking indicator;

based on the ranking indicator, a first number of words to review is determined from the words that the user has recited.

3. The method of claim 2 wherein the step of determining the user's familiarity with words based on user feedback on words in reciting words comprises:

when the feedback of the user to the word is known, increasing the familiarity of the user to the word by a first preset value;

and when the feedback of the user to the word is unknown, increasing a second preset value for the error coefficient of the user to the word, and after the user receives the prompt, feeding back that the feedback is unknown, further increasing a third preset value for the error coefficient of the word, and determining the familiarity of the user to the word based on the error coefficient, wherein the familiarity is in negative correlation with the error coefficient.

4. The method of claim 3, wherein the second predetermined value is 1 and the third predetermined value is 0.5, and the step of determining the familiarity of the user with the word based on the error coefficient comprises:

if the error coefficient of a word is 0, increasing the familiarity of the word by 3;

if the error coefficient of a word is greater than 0 and less than 2, increasing the familiarity of the word by 2;

if the error coefficient of a word is greater than or equal to 2, the familiarity of the word is increased by 1.

5. The method of any of claims 2-4, wherein the ranking indicator is obtained by:

x＝retention-0.0001/(min(20,Δday)+10)

6. The method of claim 1 wherein the step of determining a second number of words to be recited based on the screened out user unrerecited words comprises:

selecting a second number of words from the unrerecited words if the number of unrerecited words is greater than or equal to the second number;

if the number of unrerecited words is less than a second number, then all of the unrerecited words are selected and a third number of words is selected from the lesson word book, the third number being the difference between the second number and the number of unrerecited words.

7. The method of claim 1 further comprising, prior to said determining a second number of words to be recited based on the screened out words not recited by the user:

when the word book currently recited by the user is not the curriculum word book, the words not recited by the user are screened from the word book currently recited by the user.

8. The method of claim 1 wherein the step of recommending the determined words to review and words to recite to the user comprises:

and recommending the words to be reviewed first and then recommending the words to be recited to a user.

9. An apparatus for word recommendation residing in a computing device, the apparatus comprising:

the word book recommending unit is suitable for recommending curriculum word books matched with curriculum for the user based on the curriculum currently learned by the user;

a word selection unit adapted to determine a first number of words to be reviewed from the words that have been recited by the user based on a predetermined algorithm; when the word book currently recited by the user is the curriculum word book, taking a word list associated with the next-day curriculum contents from the curriculum word book, and screening out words which are not recited by the user from the word list; and determining a second number of words to be recited based on the screened out words not recited by the user;

and the word recommending unit is suitable for recommending the determined words to be reviewed and the words to be recited to the user.

10. A computing device, comprising:

at least one processor; and

a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions comprising instructions for performing the method of any of claims 1-8.