CN114491295B

CN114491295B - Android-based network community resource recommendation method and device

Info

Publication number: CN114491295B
Application number: CN202210389332.8A
Authority: CN
Inventors: 匡罡
Original assignee: Changsha Developer Technology Co ltd; Beijing Innovation Lezhi Network Technology Co ltd
Current assignee: Changsha Developer Technology Co ltd; Beijing Innovation Lezhi Network Technology Co ltd
Priority date: 2022-04-14
Filing date: 2022-04-14
Publication date: 2022-06-24
Anticipated expiration: 2042-04-14
Also published as: CN114491295A

Abstract

The invention discloses an android-based network community resource recommendation method and device, wherein the method comprises the following steps: acquiring scene information of a current user; determining a corresponding readable time based on the scene information; acquiring a technical article recommendation set of a current user; wherein each technical article in the technical article recommendation set has a corresponding reading time consumption level; and selecting the technical articles of which the time upper limit corresponding to the reading time consumption level does not exceed the readable time from the technical article recommendation set, and recommending the technical articles to the current user. The method and the device can more accurately recommend the technical articles which accord with the readable time in the current scene to the current user, and improve the reading experience of the user on the pushed technical articles in different scenes.

Description

Android-based network community resource recommendation method and device

Technical Field

The invention relates to the technical field of internet, in particular to an android-based network community resource recommendation method and device.

Background

With the rapid development of internet technology and the gradual expansion of network user scale, mobile terminals (such as android phones) have become articles carried by people, APP developers and operators need to improve user experience through various means, so that old customers are retained and new customers are developed, and recommending resources to users by APPs is one of means for improving user experience. When researching how a network community APP (such as CSDN) recommends an Internet technology article suitable for a current scene and a reading habit thereof to a user, the inventor finds that the readable time of the user in the current scene and the time consumed for reading the technology article need to be considered, and the user can be guaranteed to have good reading experience for the pushed technology article only by guaranteeing that the readable time is longer than the consumed time.

The following related prior arts are found through searching:

chinese patent publication No. CN108076439A discloses a method and apparatus for pushing messages based on wireless access points, which can predict that the user is likely to have long or short trips, and thus can recommend long reading articles or recreational reading, etc. for the user.

Chinese patent publication No. CN106971588A discloses a bus station-based vehicle query and reading management system, in which an article matching unit screens out electronic articles of a specific length according to the remaining time of the vehicle arriving at each station and the current station position.

Chinese patent publication No. CN108399529A discloses a time management method and system, wherein after a user enters a recommended application, the application recommends the most appropriate task according to the idle duration, for example: an article with a reading time closest to the idle duration, a video with a viewing time closest to the idle duration, etc.

In the prior art, it is considered that the time consumed by the user for reading is positively correlated with the article length, so that the articles with the lengths positively correlated with the article length are selected according to the readable time, and better reading experience is achieved for the user. However, the article pushed to the user in the prior art is different from the article pushed by the APP in the present invention, the article pushed by the APP in the present invention belongs to an internet technology article, and compared with a general literature article or a recreation article, the time consumed for the user to read is not positively related to the length of the article, and may also be related to the technical difficulty of the article itself or the technical level of the user itself, and the prior art cannot be directly applied to the APP in the present invention to improve the reading experience of the user, so it is necessary to improve the prior art.

Disclosure of Invention

The invention aims to solve at least one of the technical problems in the prior art, and provides an android-based network community resource recommendation method and device, which can improve the reading experience of a user on pushed technical articles in different scenes.

In a first aspect, the present invention provides an android-based network community resource recommendation method, where the method includes:

acquiring scene information of a current user;

determining a corresponding readable time based on the scene information;

acquiring a technical article recommendation set of a current user; wherein each technical article in the technical article recommendation set has a corresponding reading time consumption level;

and selecting the technical articles of which the time upper limit corresponding to the reading time consumption level does not exceed the readable time from the technical article recommendation set, and recommending the technical articles to the current user.

In a second aspect, the present invention provides an android-based network community resource recommendation device, including: the android-based network community resource recommendation method comprises the following steps of storing, processing and computer programs stored on the storing and running on the processing, wherein the processor executes the programs to realize the android-based network community resource recommendation method according to any one of the first aspect of the invention.

In a third aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the android-based web community resource recommendation method according to any one of the first aspect of the present invention.

Has the advantages that: according to the network community resource recommendation method and device based on the android, the readable time corresponding to the current user can be determined according to the scene information of the scene where the current user is located, and the technical article with the reading time consumption level matched with the readable time can be recommended to the user. In the invention, the technical articles in the technical article recommendation set are selected from the technical articles which are completely read by the target user with the same reading habit and similar reading capability as the current user, and the technical articles are not simply recommended according to the article length. It can be understood that if the current user and the target user have the same reading time consumption levels for a plurality of technical articles of the same technical category, the reading capacities of the current user and the target user are considered to be very close to each other, and obviously, for a second technical article which is completely read by the target user and is not read by the current user, the reading time consumption level corresponding to the reading of the second technical article by the current user is the same as that of the target user with a high probability, so that compared with a scheme in the prior art that an article which meets the reading time of the current scene is recommended only depending on the length of the article, the technical article which meets the reading time of the current scene can be recommended for the current user more accurately, and the reading experience of the pushed technical articles under different scenes by the user is improved.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The invention is further described below with reference to the accompanying drawings and examples;

FIG. 1 is an application environment diagram of an android-based web community resource recommendation method in an embodiment.

Fig. 2 is a schematic flow chart of an android-based network community resource recommendation method in an embodiment.

Fig. 3 is a schematic flow chart of an android-based web community resource recommendation method in an embodiment.

Fig. 4 is a schematic flow chart of an android-based network community resource recommendation method in an embodiment.

FIG. 5 is a flowchart illustrating an android-based web community resource recommendation method in an embodiment.

FIG. 6 is a flowchart illustrating an android-based web community resource recommendation method in an embodiment.

FIG. 7 is a flowchart illustrating an android-based web community resource recommendation method in an embodiment.

FIG. 8 is a flowchart illustrating an android-based web community resource recommendation method in an embodiment.

FIG. 9 is a diagram of an interactive interface of the web community APP, in one embodiment.

FIG. 10 is a diagram of an interaction interface of the web community APP in one embodiment.

FIG. 11 is an exemplary diagram of a web community APP in one embodiment.

FIG. 12 is a block diagram of a computer device in one embodiment.

FIG. 13 is a diagram of an interaction interface of the web community APP in one embodiment.

FIG. 14 is a diagram of an interaction interface of the web community APP, in one embodiment.

Reference numerals:

110. a terminal; 120. and (4) a server.

Detailed Description

Reference will now be made in detail to the present preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout.

FIG. 1 is an application environment diagram of an android-based web community resource recommendation method in an embodiment. Referring to fig. 1, the android-based network community resource recommendation method is applied to a network community APP. The network community APP may be implemented by using a B/S architecture, i.e., a browser and server architecture mode, or may be implemented by using a CS architecture (generally referred to as a server-client), as shown in fig. 1, a hardware system for building the CS architecture or the BS architecture includes a terminal 110 and a server 120. The terminal 110 and the server 120 are connected through a network. The terminal 110 may specifically be a desktop terminal 110 or a mobile terminal 110, and the mobile terminal 110 may specifically be at least one of a mobile phone, a tablet computer, a notebook computer, and the like. The server 120 may be implemented as a stand-alone server 120 or as a server cluster of multiple servers 120.

It can be known that, in the prior art, it is considered that the time consumed by the user for reading is positively correlated with the article length, so that the articles with the lengths positively correlated with the article length are selected according to the readable time, and a better reading experience is realized for the user. However, the article pushed to the user in the prior art is different from the article pushed by an APP (hereinafter referred to as APP) of the network community in the present invention, the article pushed by the APP in the present invention belongs to an article in the internet technology class, and compared with a general article in the literature class or a pastime class article, time consumed for the user to read is not positively related to the length of the article, and may also be related to technical difficulty of the article itself or technical level of the user itself, and the prior art cannot be directly applied to the APP of the present invention to improve the reading experience of the user, so it is necessary to improve the prior art.

In the following, the android-based web community resource recommendation method provided by the embodiment of the present invention will be described and explained in detail through several specific embodiments.

As shown in FIG. 2, in one embodiment, an android-based web community resource recommendation method is provided. The embodiment is mainly illustrated by applying the method to computer equipment. The computer device may specifically be the terminal 110 or the server 120 in fig. 1 described above.

Referring to fig. 2, a flowchart of a method S100 for implementing an android-based web community resource recommendation on a computer device in an embodiment of the present invention is shown. The android-based network community resource recommendation method specifically comprises the following steps:

the processing flow is started in step S110.

In step S120, scene information of the current user is acquired.

In step S130, a corresponding readable time is determined based on the scene information.

The scene information of the current user is information of a scene where the user is currently located. The idle time owned by the current user can be judged according to the information of the current scene of the current user. For example, if the current user is judged to be in a subway station according to wifi information connected with a GPS or a mobile phone of the user, and the arrival time of the next subway is 5 minutes later, it can be determined that the idle time of the user is 5 minutes, that is, the reading time is 5 minutes.

It can be understood that how to obtain the scene information of the user and determine the readable time corresponding to the scene of the user belongs to the prior art, and specific reference may be made to technical information disclosed in chinese patents with publication numbers CN108076439A, CN106971588A, and CN108399529A, which are not described herein again.

In step S140, a technical article recommendation set of the current user is obtained; wherein each technical article in the recommendation set of technical articles has a corresponding reading time consumption level.

Referring to fig. 3, in the process S140, a recommendation set of technical articles of a current user is obtained through the following steps:

in step S141, the processing flow is started.

In step S142, the first technical article that is completely read by the current user is classified according to technical categories.

In step S143, for each first technical article in each technical category, the reading time consumption level of each first technical article completely read by other users is counted piece by piece.

In step S144, the users in the same technology category having the same reading time consumption level as the current user and the largest number are set as the target users of the technology category.

In step S145, the second technical articles completely read by the target user of each technical category are used as the technical article recommendation set of the current user; the second technical article and the first technical article belong to different technical articles, and the reading time consumption level of each second technical article in the technical article recommendation set is the reading time consumption level corresponding to the second technical article which is completely read by the corresponding target user.

In step S146, the processing flow ends.

Referring to table 1, an example of the recommendation set of technical articles for the current user Zhang III obtained in one embodiment of step S140 is shown. In this example, the current user is zhang, and 150 first technical articles that are completely read through zhang can be counted in step S142, and the first technical articles include three technical categories and the number of the three technical articles are: a JAVA-50 fragment; c # -50; PYTHON-50. In step S143, 150 first technical articles corresponding to the three technical categories are counted one by one, for example, as shown in fig. 11, the technical category is JAVA, the first technical article named "unicode style" has a reading amount of 680, wherein 20 users completely read the article in total, wherein the reading time of 10 people is less than 5min, the reading time of 5 people is between 5min and 10min, and the reading time of 5 people is greater than 10min, and then the reading time level corresponding to each user may be determined by referring to table 1. If the time for completely reading the article by using Zhang III is less than 5min, 10 users of the article have the same reading time consumption level as Zhang III, and if the Li IV is one of the 10 users, 1 is added to a variable JAVA-CNT corresponding to the Li IV, wherein the variable JAVA-CNT is used for indicating the number of the Li IV having the same reading time consumption level as Zhang III in the technical category of JAVA. In step S144, it is counted that the number of the first 50 technical articles in the technical category of JAVA, which are the same in reading time consumption level as that of zhang san, is 35, i.e., JAVA-CNT = 35. If the number of other users having the same reading time consumption level as Zhang III in 50 first technical articles in the technical category of JAVA is less than 35, then Li IV is taken as a target user of JAVA. Statistics for each technology category gave the results shown in table 1. I.e., target user with king five as C #. Zhao Liu is the target user of PYTHON. In step S145, a technical article recommendation set is obtained by taking the technical articles completely read by lie four, king five and zhao six but unread by zhang as the second technical articles. In the technical article recommendation set of the JAVA technical category, the reading time consumption level corresponding to the second technical article read by liqu is marked as the reading time consumption level corresponding to the second technical article. In an example of step S150, as shown in fig. 11, if the name "Java real easy (twenty-nine) factory mode …" is a second technical article in the Java technology category of articles completely read by liqid, and the reading time consumption level of liqid corresponding to the article is two levels, when it is determined that the readable time of zhangsan in the current scene is 10 minutes, the article is recommended to zhangsan, so as to ensure that the current user can completely read the technical article in the current scene, and improve the reading experience of the user.

Fig. 4 shows a processing flow S200 of determining a reading time consumption level corresponding to a complete reading of a technical article by a user.

In step S210, the processing flow is started.

In step S220, a first time node when the user enters a reading page corresponding to the technical article is obtained; and the reading page is displayed with a corresponding scroll bar.

In step S230, a second time node when the slider on the scroll bar moves to the preset read-out position of the scroll bar track is obtained; when the scroll bar slider moves to a preset reading completion position of the scroll bar track, the reading page displays the content at the tail end of the technical article.

In step S240, the time interval between the second time node and the first time node is compared with the upper and lower time limits corresponding to different reading time consumption levels, so as to obtain a reading time consumption level corresponding to the user having completely read one technical article.

In step S250, the processing flow ends.

In step S220, when the user enters the reading page, the content of the technical article is displayed on the reading page, for example, the corresponding first time node is 20: 10. In step S230, it is monitored whether the position of the slider of the scroll bar reaches the preset read-out position of the scroll bar track, in this example, the entire read page is used to display the content of the technical article, so the preset read-out position of the scroll bar track is the bottommost portion of the scroll bar track, at this time, it is considered that the user completely reads the technical article, and at this time, the corresponding second time node is 20: 18. In step S240, the time interval between the second time node and the first time node is calculated to be 8min, and the corresponding reading time-consuming level is determined according to the conversion relationship between the preset reading time-consuming level and the time-consuming interval. For example, when calculating by technology class, as shown in Table 1, 8min corresponds to two levels for the JAVA and C # technology classes and three levels for the PYTHON technology class.

It should be noted that when counting the reading time consumption level of each first technical article completely read by other users, the technical categories are distinguished, and considering that the technical level and the reading capability of the user are different in different technical fields, the user with the technical level and the reading capability closer to the current user can be more finely matched by counting according to the technical categories, so that the second technical article more conforming to the actual reading time consumption of the current user is obtained, and the reading experience of the user is improved. For ease of understanding, the following are illustrated: for example, three users, namely a user, B user and C user exist, wherein a is the current user, and if a and B have the same reading time consumption level number of 10 and a and C are 8 without distinguishing the technical categories; but in the JAVA class of technology, C is 7 and B is 2; obviously, the technical level and reading ability of a and C in JAVA can be considered closer, and when recommending JAVA articles, C should be referred to instead of B, because of the difference of the technical level of users in each technical field.

TABLE 1

In step S150, a technical article whose upper time limit corresponding to the reading time consumption level does not exceed the readable time is selected from the technical article recommendation set, and the technical article is recommended to the current user.

In an embodiment of step S150, the selecting a technical article from the technical article recommendation set, where a time upper limit corresponding to a reading time consumption level does not exceed a readable time, and recommending the technical article to the current user specifically includes:

step S151, a first technical category corresponding to a page browsed by a current user is obtained.

Step S152, selecting a technical article belonging to the first technical category whose upper time limit corresponding to the reading time consumption level does not exceed the readable time from the technical article recommendation set, and recommending the technical article belonging to the first technical category to the current user.

Referring to fig. 11, in a scenario, in the process that a current user opens three subways such as a subway station, a network community APP is opened, and then three clicks a control corresponding to an icon named "Java" shown in a black frame in fig. 11, and the operation page shown in fig. 11 is entered to browse a technical article related to Java, that is, the first technical category in step S151 is Java. And pushing the upper time limit corresponding to the reading time consumption level not exceeding the readable time to the third user from the second technical article completely read by the target user Li IV.

It can be understood that, if the readable time is 20 minutes, it is obvious that the second technical articles corresponding to the first-level, second-level and third reading time consumption levels are all suitable for being recommended to the current user, at this time, in step S150, the following method is further performed to further improve the reading experience of the user:

when the situation that the current user is located in a public transportation place is detected according to the scene information, selecting an article with the minimum time consumption grade from a plurality of articles with reading time consumption grades meeting requirements, and recommending the article to the current user;

and when the place where the current user is located is detected as the home and the current user is located at the off-duty time according to the scene information, selecting the article with the largest time consumption level from the articles meeting the requirement of reading time consumption levels, and recommending the article to the current user.

It can be understood that the above-mentioned multiple reading time-consuming levels meeting the requirement mean that the upper time limit of the reading time-consuming level (e.g. 5min of the first level, 10min of the second level, and 30min of the third level of the JAVA technology category) does not exceed the readable time, and the readable time in each place can be obtained by processing corresponding parameters preset by the user in the network community APP provided by the embodiment of the present invention, in addition to the method described in the technical information disclosed in the chinese patent with publication numbers CN108076439A, CN106971588A, and CN 108399529A. For example, the user may set the time of going to work, the time of getting up and sleeping, and the like in the web community APP, and when it is detected that the user is at home and is in the time of going to work, the difference between the current time and the sleeping time may be used as the readable time.

Obviously, although the user has free time in the public transportation place (such as a map, a bus station and the like), the user is difficult to concentrate on reading, so that the article which consumes the least time is selected and recommended to the user, and when the user is at home, the user can concentrate on reading more, so that the article which consumes the most time is recommended to the user, and therefore the reading experience is improved.

The processing flow ends in step S160.

On the basis of the foregoing embodiment, fig. 5 shows a flowchart of implementing the android-based web community resource recommendation method S300 on a computer device in an embodiment of the present invention. The method comprises the following steps:

in step S310, the processing flow is started.

In step S320, equally dividing a portion between a reading start position and a reading end position of a scroll bar track of a scroll bar on the reading page into N segments; wherein each segment is respectively associated with the content of the third technical article displayed on the reading page.

In step S330, a ratio between the staying time of the scroll bar slider in each segment and the staying time of the scroll bar slider between the reading start position and the reading end position of the scroll bar track is obtained, and the ratio is used as an attention coefficient of the content of the third technical article associated with the segment.

In step S340, when it is detected that the attention coefficients of the contents of the nth related third technical article obtained by the users who exceed the set number while reading the third technical article all exceed the preset threshold, marking the contents of the nth related third technical article as key contents; wherein N is a positive integer greater than 1, and N is a positive integer not greater than N.

In step S350, the key content is highlighted on the reading page.

In step S360, the processing flow ends.

Referring to fig. 13, in this embodiment, the reading start position is the uppermost position of the vertical scroll bar track on the reading page, and the reading end position is the lowermost position of the vertical scroll bar track on the reading page. In one example, as shown in fig. 13, when the user enters the reading page, the slider of the scroll bar is located at the reading start position, the advertisement area is above the reading page for displaying the advertisement content, and the title and author of the third technical article are displayed below the reading page, and the text is not displayed at this time. When the user slides the page or moves the scroll bar slider through an interactive operation, referring to fig. 14, the scroll bar slider moves down along the scroll bar track to the position in fig. 14, at this time, the advertisement content moves up and hides in the page, the text content moves up from under the page to the screen range, and the content at the beginning of the text in the third technical article is displayed under the plane. By analogy, when the scroll bar slider moves to the reading end position, the reading page displays the last content in the third technical article, and the user is considered to finish reading the technical article at the moment. It will be readily appreciated that movement of the scrollbar slider from the position of figure 13 to the position of figure 14 if the distance of movement is exactly 1/N of the entire scrollbar track, the text content of the screen display in figure 14 is considered to be the content of the third technical article associated with the 1 st scrollbar track.

It can be understood that the content amount of different articles is different, the size of the mobile phone screen is fixed, and for the technical articles with different content amounts, the content amount displayed on the reading page is different when the scroll bar slider moves through the same length. In another example, assuming that the content of the third technical article occupies the length of two screens, when the scroll bar slider moves to the middle between the reading start position and the reading end position, the content of the first half of the article is displayed on the reading page of the screen; when the scroll bar slider continues to move to the reading end position, the first half of the content can be gradually hidden along with the movement of the scroll bar slider, and the second half of the content can be gradually displayed from the lower part of the reading page. In this example, if the length between the reading start position and the reading end position is H, the scroll bar slider is considered to have been read after moving by the length H. In step S320, the part between the reading start position and the reading end position is equally divided into N segments, each segment is H/N, if the distance from the reading start position to the scroll bar slider is H, N = [ H/(H/N) ] +1, where [ ] is a rounding function, and [ H/(H/N) ] represents taking an integer part of H/(H/N), so that it can be known which content in the third technical article the slider position corresponds to according to the position H of the scroll bar slider.

In step S330, if the reading time of the first user reading the technical article is 10min, N =10, and the time that the first user stays at the content position corresponding to N =6 is 3min, the attention coefficient corresponding to the content corresponding to N =6 is 3/10= 0.3. Counting the number of users who read the technical article completely to be 100, wherein 35 users have a corresponding attention coefficient >0.25 when reading the content corresponding to n =6, and if the set number is 30, the preset threshold value is 0.25, and the content corresponding to n =6 is taken as the key content of the technical article. It should be noted that, in this embodiment, the reading time consumption level of 100 users may be different, for example, the reading time consumption of the user b is 5min, and the time spent by the user b in the content position corresponding to n =6 is 1.6min, although the absolute time is faster than that of the user a, the attention coefficient of the user b is 0.32, which is larger than that of the user a, and the content may also be considered to be of interest or have reading difficulty. The method and the device do not count the absolute time of stay but judge the key content according to the ratio of the stay time to the total time consumption, and are suitable for users with different reading abilities and technical levels. Since the scroll bar track is equally divided into N segments, each segment corresponds to a part of the content in the technical article, which is equivalent to uniformly segmenting the content of each part of the technical article, so that the content corresponding to each segment is fixed, and the method of the embodiment can consistently identify the staying time of each part of the content no matter how the absolute length of the scroll bar track changes due to the model change of the terminal 110, and is suitable for different models of the terminal 110. As shown in fig. 10, in step S350, the highlighted content located at the bottommost portion of the screen is a content corresponding to n = 6.

In the embodiment, the situation that the user may read articles in scenes such as waiting for a bus and the like is considered, and the attention is easy to disperse, so that the part, with the longest attention time, of most users during reading is automatically highlighted, the effect of reminding the users to concentrate on the attention can be achieved, and the reading experience of the users is improved.

As shown in fig. 6, on the basis of the foregoing embodiment, fig. 6 shows a flowchart of an android-based web community resource recommendation method S400 implemented on a computer device in an embodiment of the present invention. The method comprises the following steps:

and step S410, when the scroll bar slider is detected to move to the scroll bar track position corresponding to the key content, displaying a search frame on the reading page. As shown in fig. 9, a search box is displayed above the reading page.

In step S420, in response to the keyword input by the user in the search box, the content matching the keyword is searched from the third technical article displayed on the current reading page.

In this embodiment, considering that the key content has a certain reading difficulty, in step S410, when the user sees the key content, a search is provided so that the user can search for keywords appearing in the key content in the entire third technical article to understand the content thereof in combination with the context, thereby improving the reading experience of the user.

As shown in fig. 7, on the basis of step S400, fig. 7 shows a flowchart of implementing an android-based web community resource recommendation method S500 on a computer device in an embodiment of the present invention. The method comprises the following steps:

step S510, counting keywords input in the search box when different users read the third technical article.

In step S520, the keywords whose occurrence times are greater than the threshold value of the search times are used as key terms.

In step S530, hyperlinks are added to the key terms in the third technical article displayed on the reading page.

And S540, responding to the touch operation of the user on the key terms displayed in the reading page, and jumping to the page corresponding to the hyperlink.

Specifically, in step S510, the keywords of the search box input by the user when reading the third technical article are uploaded to the server 120 for statistics. In step S520, the threshold of the number of searches is 40, and it is counted that the word of the B/S architecture is searched by the user more than 40 times, the 'B/S architecture' is marked as a key term. In step S530, the reading page displays a word of 'B/S architecture' in the form of a hyperlink. As shown in fig. 10, a hyperlink is added to a key term shown in underline, and when a user clicks the key term, the user can jump to a page corresponding to the hyperlink, such as a hundred-degree search or a third technical article where the key term appears.

As shown in fig. 8, on the basis of the foregoing embodiment, fig. 8 shows a flowchart of an android-based web community resource recommendation method S600 implemented on a computer device in an embodiment of the present invention. The method comprises the following steps:

in step S610, when it is detected that the scroll bar slider moves to the scroll bar track position corresponding to the key content, a graffiti opening control is displayed on the reading page.

In step S620, in response to a touch operation of the user on the graffiti opening control, switching to a graffiti function interface; and the scrawling function interface takes the current screenshot of the reading page as the background.

In step S630, a first object is displayed in a first area of the graffiti function interface.

In step S640, in response to a touch operation of the first object by the user, switching back to the reading page.

As shown in fig. 10, a graffiti opening control is displayed in an area above the reading page, if the user clicks yes, the graffiti opening control is switched to a graffiti function interface, and if the user clicks no or does not click the graffiti opening control for more than 3 seconds, the graffiti opening control is hidden.

In this embodiment, it is considered that the user is inconvenient to carry a notebook in places such as a bus station, and the key contents of technical articles such as programming need to be thought in coordination with the stroke calculation, so in step S610, not only is the doodle function interface switched to, but also the doodle function interface is made to use the current screenshot of the reading page as the background, so that the user can conveniently take the stroke thinking with reference to the key contents, and the reading experience of the user is improved.

In one embodiment, the method further comprises:

and when the scroll bar slider is detected to move to the scroll bar track position corresponding to the key content, displaying a bullet screen in a second area of the reading page, wherein the content of the bullet screen is comment content which is matched from a comment area of a third technical article through a text matching algorithm and has text similarity with the key content larger than preset similarity.

As shown in fig. 10, when the key content of the user enters the display area, a bullet screen is displayed above the key content. Specifically, the content of the bullet screen is comment content which is captured from the comment area through a text matching algorithm and is related to the key content. The specific text similarity matching algorithm belongs to the prior art, and is not described herein in detail. In the embodiment, the user can check the comment content without jumping to the page of the comment area, so that the user is not easy to be distracted and can concentrate on thinking; and the comment contents are contents related to the key contents, so that the user does not need to actively search in the comment area, and the time of the user is saved. Meanwhile, comments of other users about the key content can be popped up, so that the user can be helped to understand the key content, and the user experience is improved.

FIG. 12 is a diagram that illustrates an internal structure of the computer device in one embodiment. The computer device may specifically be the terminal 110 (or the server 120) in fig. 1. As shown in fig. 12, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and also stores a computer program, and when the computer program is executed by a processor, the computer program can enable the processor to realize the android-based network community resource recommendation method. The internal memory may also store a computer program, and when the computer program is executed by the processor, the computer program may cause the processor to execute the android-based web community resource recommendation method. Those skilled in the art will appreciate that the architecture shown in fig. 12 is merely a block diagram of some of the structures associated with the inventive arrangements and is not intended to limit the computing devices to which the inventive arrangements may be applied, as a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, an android-based web community resource recommendation device is provided, including: the android-based network community resource recommendation method comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to execute the steps of the android-based network community resource recommendation method. Here, the steps of the android-based web community resource recommendation method may be steps in the android-based web community resource recommendation method in each of the above embodiments.

In one embodiment, a computer-readable storage medium is provided, which stores computer-executable instructions for causing a computer to perform the steps of the above method for recommending network community resources based on android. The steps of the android-based web community resource recommendation method may be the steps in the android-based web community resource recommendation method in each of the embodiments.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRA), Rambus (Rambus) direct RAM (RDRA), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.

Claims

1. An android-based network community resource recommendation method is characterized by comprising the following steps:

acquiring scene information of a current user;

determining a corresponding readable time based on the scene information;

selecting a technical article of which the time upper limit corresponding to the reading time consumption level does not exceed the readable time from the technical article recommendation set, and recommending the technical article to the current user;

the technical article recommendation set of the current user is obtained through the following steps:

classifying the first technical articles completely read by the current user according to technical categories;

counting the reading time consumption level of each first technical article read by other users one by one for each first technical article in each technical category;

taking the users with the same reading time consumption grade as the current user and the largest number in the same technical category as target users of the technical category;

taking the second technical articles completely read by the target users of all technical categories as a technical article recommendation set of the current user; the second technical article and the first technical article belong to different technical articles, and the reading time consumption level of each second technical article in the technical article recommendation set is the reading time consumption level corresponding to the second technical article which is completely read by the corresponding target user;

wherein, the selecting a technical article from the technical article recommendation set, the upper time limit of which corresponds to the reading time consumption level does not exceed the readable time, and recommending the technical article to the current user specifically includes:

acquiring a first technical category corresponding to a page browsed by a current user;

and selecting the technical articles which belong to the first technical category and have the time upper limit corresponding to the reading time consumption level not exceeding the readable time from the technical article recommendation set, and recommending the technical articles belonging to the first technical category to the current user.

2. The android-based network community resource recommendation method of claim 1, further comprising the step of determining a reading time consumption level corresponding to a complete reading of a technical article by a user through the following steps:

acquiring a first time node when a user enters a reading page corresponding to the technical article; the reading page is displayed with a corresponding scroll bar;

acquiring a second time node when a scroll bar sliding block on a scroll bar moves to a preset reading completion position of a scroll bar track; when the scroll bar slider moves to a preset reading completion position of the scroll bar track, the reading page displays the last content of the technical article;

and comparing the time interval between the second time node and the first time node with the upper and lower time limits corresponding to different reading time consumption levels to obtain the reading time consumption level corresponding to the technical article completely read by the user.

3. The android-based web community resource recommendation method of claim 1, further comprising:

dividing the part between the reading starting position and the reading end position of a scroll bar track of a scroll bar on a reading page into N sections at equal distance; wherein, each segment is respectively associated with the content of the third technical article displayed on the reading page;

obtaining the ratio of the staying time of the scroll bar slider in each section to the staying time of the scroll bar slider between the reading starting position and the reading ending position of the scroll bar track, and taking the ratio as the attention coefficient of the content of the third technical article related to the section;

when the fact that the attention coefficients of the contents of the nth related third technical article obtained when the users with the number exceeding the set number read the third technical article exceed the preset threshold value is detected, the contents of the nth related third technical article are marked as key contents; wherein N is a positive integer greater than 1, and N is a positive integer not greater than N;

and highlighting the key content on the reading page.

4. The android-based web community resource recommendation method of claim 3, wherein the method further comprises:

when the scroll bar slider is detected to move to the scroll bar track position corresponding to the key content, displaying a search box on the reading page;

and responding to the keywords input by the user in the search box, and searching the third technical article displayed on the current reading page for the content matched with the keywords.

5. The android-based web community resource recommendation method of claim 4, wherein the method further comprises:

counting key words input in the search box when different users read the third technical article;

taking the key words with the occurrence times larger than the search time threshold as key terms;

adding hyperlinks to key terms in a third technical article displayed on the reading page;

and jumping to a page corresponding to the hyperlink in response to a touch operation of the key term displayed in the reading page by the user.

6. The android-based web community resource recommendation method of claim 3, further comprising:

when the scroll bar slider is detected to move to the scroll bar track position corresponding to the key content, displaying a graffiti opening control on the reading page;

responding to the touch operation of the user on the scrawling starting control, and switching to a scrawling function interface; the scrawling function interface takes a current screenshot of a reading page as a background;

displaying a first object in a first area of the graffiti function interface;

and switching back to a reading page in response to the touch operation of the user on the first object.

7. The android-based web community resource recommendation method of claim 3, wherein the method further comprises:

and when the scroll bar slider is detected to move to the scroll bar track position corresponding to the key content, displaying a bullet screen in a first area of a reading page, wherein the content of the bullet screen is comment content which is matched from a comment area of a third technical article through a text matching algorithm and has text similarity with the key content larger than preset similarity.

8. An android-based network community resource recommendation device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the android based web community resource recommendation method as claimed in any one of claims 1 to 7.