Courseware content recommendation method, device, terminal and storage medium

Abstract

The embodiment of the present invention discloses a courseware content recommendation method, device, terminal and storage medium, the method comprising: performing word segmentation processing on the courseware content in the database to obtain word segmentation vocabulary, and counting the number of occurrences of each word segmentation vocabulary after word segmentation; recording the number of visits to each searched courseware and the number of searches for each searched vocabulary according to each search vocabulary and the courseware visited by the user, and respectively counting hot vocabulary and hot courseware according to corresponding preset rules; and recommending courseware to the user according to the hot vocabulary and the word segmentation vocabulary in the hot courseware. Full-text retrieval of courseware content ensures comprehensiveness, provides users with more accurate hot recommendations, and better helps users understand courseware dynamics.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to a courseware content recommendation method, device, terminal and storage medium. Background Art

[0002] In current learning websites, there are various courses and corresponding courseware. Popular courses will naturally have popular courseware. However, currently, only when creating the visible courseware, the type and content of the courseware are written into the table to realize the retrieval function. It is impossible to provide users with the most satisfactory courseware, nor can it automatically recommend popular courseware to users. Summary of the invention

[0003] In view of this, the present application provides a courseware content recommendation method, comprising:

[0004] Perform word segmentation processing on the courseware content in the database to obtain word segmentation vocabulary, and count the number of occurrences of each word segmentation vocabulary after word segmentation;

[0005] According to the search terms and courseware visited by the user each time, the number of visits to each search courseware and the number of searches for each search term are recorded, and the hot terms and hot courseware are counted according to the corresponding preset rules;

[0006] Recommending courseware to the user based on the popular words and the segmented words in the popular courseware.

[0007] Furthermore, the popular words counted according to the corresponding preset rules include:

[0008] Calculating the popularity score of each of the search terms;

[0009] According to the hot scores, the search terms are arranged from high to low, and a first preset number of search terms are selected as the hot terms.

[0010] Furthermore, the calculation formula for the popularity score of a single search term is as follows:

[0011] The popularity score of the search word = the number of times the search word appears in the courseware * 30% + the number of searches for the search word * 30% + the number of searches for the search word within a preset time period * 40%.

[0012] Furthermore, the popular courses are counted according to the corresponding preset rules, including:

[0013] Calculate the popularity score of each of the searched courses;

[0014] According to the arrangement from high to low according to the popularity score, the searched coursewares before the second preset number are selected as the popular coursewares.

[0015] Furthermore, the calculation formula for the popularity score of a single search courseware is as follows:

[0016] The popularity score of the searched courseware=the number of visits to the searched courseware*40%+the number of visits to the searched courseware within a preset time period*60%.

[0017] Furthermore, the recommending of courseware to the user based on the popular vocabulary and the segmented vocabulary in the popular courseware includes:

[0018] The matching degree between the popular words and the segmented words in the popular courseware is calculated, and the courseware associated with the popular words is selected according to the matching degree, and the courseware is recommended to the user.

[0019] Furthermore, it also includes:

[0020] The user's historically visited courseware and historically searched words are recorded, and the historically visited courseware and the historically searched words are arranged and displayed from high to low according to the number of searches, so that the user can make a quick selection.

[0021] Furthermore, the present application also provides a courseware content recommendation device, comprising:

[0022] The word segmentation module performs word segmentation processing on the courseware content in the database to obtain word segmentation vocabulary, and counts the number of occurrences of each word segmentation vocabulary after word segmentation;

[0023] A statistics module records the number of visits to each courseware and the number of searches for each search term according to the user's search terms and the courseware visited each time, and calculates the hot terms and hot courseware according to the corresponding preset rules;

[0024] The recommendation module recommends courseware to the user according to the popular words and the segmented words in the popular courseware.

[0025] Furthermore, the present application also provides a terminal device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program runs on the processor, it executes the courseware content recommendation method described in any of the above embodiments.

[0026] Furthermore, the present application also provides a readable storage medium storing a computer program, which executes the courseware content recommendation method described in any of the above embodiments when running on a processor.

[0027] The embodiment of the present invention provides a courseware content recommendation method, which includes: performing word segmentation processing on the courseware content in the database to obtain word segmentation vocabulary, and counting the number of occurrences of each word segmentation vocabulary after word segmentation; recording the number of visits to each searched courseware and the number of searches for each searched vocabulary according to the user's search vocabulary and the courseware visited each time, and respectively counting hot vocabulary and hot courseware according to corresponding preset rules; and recommending courseware to the user according to the hot vocabulary and the word segmentation vocabulary in the hot courseware. Full-text retrieval of courseware content ensures comprehensiveness, provides users with more accurate hot recommendations, and better helps users understand courseware dynamics. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solution of the present invention, the following will briefly introduce the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope of protection of the present invention. In each of the drawings, similar components are numbered similarly.

[0029] Figure 1 A schematic diagram of a courseware content recommendation method according to an embodiment of the present application is shown;

[0030] Figure 2 A schematic diagram of a courseware content recommendation device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0032] The components of the embodiments of the present invention generally described and shown in the drawings herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0033] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present invention, are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or adding the possibility of one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0034] Furthermore, the terms “first”, “second”, “third”, etc. are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0035] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art in the various embodiments of the present invention. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present invention.

[0036] Example 1

[0037] This embodiment provides a courseware content recommendation method, combining Figure 1 Understanding the flowchart in.

[0038] Step S100, performing word segmentation processing on the courseware content in the database to obtain word segmentation vocabulary, and counting the number of occurrences of each word segmentation vocabulary after word segmentation;

[0039] First, in this embodiment, the database is a database in the learning website, and all the courseware corresponding to each course is stored in the database. These courseware can be stored in formats such as PDF. In order to read the content of these courseware, the courseware content will be extracted first, and the text documents of these courseware will be obtained. Then, word segmentation processing will be performed, various words in the courseware will be extracted, and these words will be counted.

[0040] After implementing the above word segmentation and statistical operations for all courseware, the number of occurrences of each word in each courseware can be obtained. This data reflects the relevance of some words and courseware.

[0041] Step S200, according to the search words and the courseware visited by the user each time, the number of visits to each search courseware and the number of searches for each search word are recorded, and the hot words and hot courseware are counted according to the corresponding preset rules;

[0042] In addition to counting the number of times a word appears in the courseware, we also count the words that users search for and the courseware that they visit. The user here does not refer to a single user, but to all the courseware that users search and visit. In this way, we can get the number of times each word is searched and the number of times the courseware is visited. The number of searches for a word reflects the popularity of this word and the courseware associated with this word, as well as the popularity of each courseware visited. Based on these data, these words and courseware are sorted from high to low, and we can get a preliminary ranking of popular words and popular courseware.

[0043] Furthermore, the popularity of a courseware or vocabulary will actually change over time. For example, at the beginning of a semester, there may be a large number of users searching for courses related to sets or sequences, while in the middle or at the end of the semester, more people will search for courseware or related vocabulary on functions or geometry. Therefore, relying solely on the number of searches cannot perfectly reflect the popularity of a vocabulary.

[0044] Specifically, for each word, the popularity score of each word is calculated. The formula of the popularity score is as follows:

[0045] The popularity score of the word = the number of times the word appears in the courseware * 30% + the number of searches for the word * 30% + the number of searches for the word within a preset time period * 40%.

[0046] In the formula, the preset time period can be a short period of time such as the last week or two weeks, which is used to indicate the timeliness of a word being searched, so that when everyone starts to pay attention to this word, it can be reflected in the popularity score in a timely manner. At the same time, the number of times the word appears in each visible field is also calculated to reflect whether the word is objectively a common term in a courseware. Therefore, the popularity score calculated by the above formula can reflect the popularity of certain words within the preset time period, and can change with the passage of time. It should be understood that the various proportions in the above formula can be adaptively adjusted before and after the current proportion value according to actual needs.

[0047] After obtaining the popularity score of the vocabulary, the vocabulary is sorted according to the score, and the top vocabulary is recommended to the user as the popular vocabulary, such as the top 10 or top 15 vocabulary as the popular vocabulary to the user, and is recommended to the user by displaying it below the search bar or directly in the search box, such as displaying "Everyone is learning xx" in the search bar, where "xx" is the popular vocabulary.

[0048] Furthermore, for each courseware, the popularity score of each courseware will also be calculated. The formula for the popularity score is as follows:

[0049] The popularity score of the courseware = the number of visits to the courseware*40%+the number of visits to the courseware within a preset time period*60%.

[0050] From the above formula, we can know that the popularity score of a courseware is composed of the number of visits to the courseware and the number of visits in the time period. The number of visits in the preset time period accounts for a large proportion. Generally speaking, the preset time period is one to two weeks. It can reflect the changes in popularity at different times, so that the calculated popularity score can keep up with the real time and will not lag behind. Similarly, the proportions in the above formula can be adaptively adjusted before and after the current proportion value according to actual needs.

[0051] The courseware are sorted according to their popularity scores, and the ones in the front row are regarded as popular courseware, such as the top 5 or top 10 courseware.

[0052] Step S300, recommending courseware to the user based on the popular vocabulary and the segmented vocabulary in the popular courseware.

[0053] After obtaining the popular courseware in step S200, the matching degree between the popular words and the segmented words in these popular courseware is further calculated.

[0054] Specifically, the more searched words are, the more accessed the associated courseware will be. Similarly, if a certain word in a popular courseware is a common word in the field of this courseware, then the word will become an index for retrieving the courseware in the user's mind, making the word a popular word. It can be seen that there is an inevitable connection between popular words and popular courseware. Although the rankings of popular courseware and popular words are obtained through the popularity points, it is still necessary to further match the popular words and popular courseware for further analysis.

[0055] It can be seen from step S200 that the hot words finally selected will be more than the hot courseware, and in the actual user's search, one courseware can be searched by multiple words, and one word can also be associated with multiple views. In this embodiment, courseware recommendation is ultimately required. Therefore, the above-mentioned matching of hot words and hot courseware is to count how many hot words are associated with these hot courseware. For example, the hot courseware with the highest hot score is associated with three hot words, and the second hot courseware is associated with four hot words. After obtaining the results, the hot points of the hot courseware and the hot words associated with it are added to obtain new courseware hot points, and these hot courseware are re-sorted and then recommended to users in this order.

[0056] A specific recommendation method may be to recommend these courses in the form of a ranking list on the home page, or to recommend these courses in the form of a scrolling page, so as to tell users that these courses are the most popular courses recently.

[0057] In addition, the present embodiment will also record the user's historically visited courseware and historically searched terms, and arrange and display the historically visited courseware and the historically searched terms from high to low according to the number of searches, so that the user can make a quick selection.

[0058] Example 2

[0059] This application also provides a courseware content recommendation device, such as Figure 2 As shown, the device includes a word segmentation module 10, a statistics module 20 and a recommendation module 30. The specific functions of these modules are as follows:

[0060] The word segmentation module 10 is used to perform word segmentation processing on the courseware content in the database to obtain word segmentation vocabulary and count the number of occurrences of each word segmentation vocabulary after word segmentation;

[0061] The statistics module 20 is used to record the number of visits to each courseware and the number of searches for each search word according to the search words and the courseware visited by the user each time, and to count the hot words and hot courseware according to the corresponding preset rules;

[0062] The recommendation module 30 is used to recommend courseware to the user based on the popular words and the segmented words in the popular courseware.

[0063] Furthermore, the present application also provides a terminal device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed on the processor, the courseware content recommendation method described in any of the above embodiments is executed. Specifically, a terminal device such as a personal computer, a smart phone, and a tablet computer can execute the above method.

[0064] Furthermore, the present application also provides a readable storage medium storing a computer program, which executes the courseware content recommendation method described in any of the above embodiments when running on a processor.

[0065] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or the flow chart, and the combination of boxes in the structure diagram and / or the flow chart, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0066] In addition, the functional modules or units in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0067] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0068] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A courseware content recommendation method, characterized in that: include: Perform word segmentation processing on the courseware content in the database to obtain word segmentation vocabulary, and count the number of occurrences of each word segmentation vocabulary after word segmentation; According to the search terms and courseware visited by the user each time, the number of visits to each search courseware and the number of searches for each search term are recorded, and the hot terms and hot courseware are counted according to the corresponding preset rules; Recommending courseware to the user based on the popular words and the segmented words in the popular courseware; The hot words counted according to the corresponding preset rules include: Calculating the popularity score of each of the search terms; Arrange the search terms according to their popularity scores from high to low, and select the first preset number of search terms as the popular terms; The popularity score of the search word is calculated by the number of times the search word appears in the courseware, the number of searches for the search word, and the number of searches for the search word within a preset time period and their respective proportions; The popular courses counted according to the corresponding preset rules include: Calculate the popularity score of each of the searched courses; Arrange the searched courseware according to their popularity scores from high to low, and select the searched courseware before a second preset number as the popular courseware; The popularity score of the searched courseware is calculated by the number of visits to the searched courseware and the number of visits to the searched courseware within a preset time period and their respective proportions.

2. The courseware content recommendation method according to claim 1, characterized in that: The calculation formula for the popularity score of a single search term is as follows: The popularity score of the search word = the number of times the search word appears in the courseware * 30% + the number of searches for the search word * 30% + the number of searches for the search word within a preset time period * 40%.

3. The courseware content recommendation method according to claim 1, characterized in that: The calculation formula for the popularity score of a single search term is as follows: The popularity score of the search word = the number of times the search word appears in the courseware * 30% + the number of searches for the search word * 30% + the number of searches for the search word within a preset time period * 40%.

4. The courseware content recommendation method according to claim 1, characterized in that: The recommending courseware to the user according to the popular vocabulary and the segmented vocabulary in the popular courseware comprises: The matching degree between the popular words and the segmented words in the popular courseware is calculated, and the courseware associated with the popular words is selected according to the matching degree, and the courseware is recommended to the user.

5. The courseware content recommendation method according to claim 1, characterized in that: Also includes: The user's historically visited courseware and historically searched words are recorded, and the historically visited courseware and the historically searched words are arranged and displayed from high to low according to the number of searches, so that the user can make a quick selection.

6. A courseware content recommendation device, characterized in that: include: The word segmentation module performs word segmentation processing on the courseware content in the database to obtain word segmentation vocabulary, and counts the number of occurrences of each word segmentation vocabulary after word segmentation; A statistics module records the number of visits to each courseware and the number of searches for each search term according to the user's search terms and the courseware visited each time, and calculates the hot terms and hot courseware according to the corresponding preset rules; A recommendation module, which recommends courseware to the user based on the popular words and the segmented words in the popular courseware; The hot words counted according to the corresponding preset rules include: Calculating the popularity score of each of the search terms; Arrange the search terms according to their popularity scores from high to low, and select the first preset number of search terms as the popular terms; The popularity score of the search word is calculated by the number of times the search word appears in the courseware, the number of searches for the search word, and the number of searches for the search word within a preset time period and their respective proportions; The popular courses counted according to the corresponding preset rules include: Calculate the popularity score of each search courseware; Arrange the searched courseware according to their popularity scores from high to low, and select the searched courseware before a second preset number as the popular courseware; The popularity score of the searched courseware is calculated by the number of visits to the searched courseware and the number of visits to the searched courseware within a preset time period and their respective proportions.

7. A terminal device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and when the computer program is run on the processor, the method for recommending course content according to any one of claims 1 to 5 is executed.

8. A readable storage medium, characterized in that: It stores a computer program, which executes the courseware content recommendation method described in any one of claims 1 to 5 when running on a processor.

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