Patent Application
20150052141
This application claims priority to Chinese Patent Application No. 201310360154.7 filed on Aug. 19, 2013 in the China Intellectual Property Office, the contents of which are incorporated by reference herein.
Embodiments of the present disclosure relate to data management and file transmissions.
Information, such as for example new articles, may be provided over the Internet. When a user reads a news article over the Internet, the user may want to read other related news articles. Therefore, there is a need to provide the other related news articles to the user.
Many aspects of the disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily drawn to scale, the emphasis instead being placed upon clearly illustrating the principles of the disclosure. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views.
It will be appreciated that for simplicity and clarity of illustration, where appropriate, reference numerals have been repeated among the different figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the embodiments described herein. However, it will be understood by those of ordinary skill in the art that the embodiments described herein can be practiced without these specific details. In other instances, methods, procedures, and components have not been described in detail so as not to obscure the related relevant feature being described. Also, the description is not to be considered as limiting the scope of the embodiments described herein. The drawings are not necessarily to scale and the proportions of certain parts have been exaggerated to better illustrate details and features of the present disclosure.
The present disclosure, including the accompanying drawings, is illustrated by way of examples and not by way of limitation. It should be noted that references to “an” or “one” embodiment in this disclosure are not necessarily to the same embodiment, and such references mean “at least one.”
Furthermore, the term “module”, as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, written in a programming language, such as, Java, C, or assembly. One or more software instructions in the modules can be embedded in firmware, such as in an EPROM. The modules described herein can be implemented as either software and/or hardware modules and can be stored in any type of non-transitory computer-readable medium or other storage device. Some non-limiting examples of non-transitory computer-readable media include CDs, DVDs, BLU-RAY, flash memory, and hard disk drives.
When a user reads a file on one of the client devices 2, the transmission system 10 can determine other files related to the read file according to predetermined rules, and transmit the related files to the client device 2 for the user.
In at least one embodiment, the storage device 11 can include various types of non-transitory computer-readable storage mediums, such as a hard disk, a compact disc, a digital video disc, or a tape drive. The display device 13 can display images and videos, and the input device 14 can be a mouse, a keyboard, or a touch panel.
The acquiring module 100 acquires reading information of users within a predetermined period. In at least one embodiment, the reading information of the users includes keyword characteristic values of predetermined keywords corresponding to each of the users, and reading characteristic values which represents reading habits corresponding to each of the users.
The acquiring module 100 acquires a title of each read file of the users within the predetermined period, and determines the keywords from the title of each read file as being the predetermined keywords. The acquiring module 100 calculates a frequency of each of the predetermined keywords in read files of the specified user. The calculated frequency of each of the predetermined keywords is determined to be a keyword characteristic value of each of the predetermined keywords corresponding to the specified user. For example, the predetermined keywords can include three keywords, namely A, B and C. If a frequency of A in read files of a specified user is 20, a keyword characteristic value of A which corresponds to the specified user is 20.
The reading characteristic values include, but are not limited to, an average daily reading duration, the time(s) of the day when reading is done, an average reading speed, an average number of reading files and a total reading duration of each of the users.
According to the reading information of users, the classification module 101 classifies the users into groups using a clustering method. In some embodiments, a user is classified to a single group, and in other embodiments, a user can be classified into more than one group. In at least one embodiment, the clustering method uses an expectation-maximization algorithm. The classification module 101 establishes a matrix according to the reading information of the users. The matrix is regarded as an input of the expectation-maximization algorithm. For example, as shown in
In other embodiments, the classification module 101 determines whether a classification result of the users is appropriate by calculating a sum of squares for error (SSE) of each of the groups based on the above mentioned keyword characteristic values and reading characteristic values. When a total sum of an SSE of a group is greater than or equal to a predetermined value, the classification module 101 determines that a classification result of users in the group is inappropriate, and reclassifies the users into another group or other groups. When a total sum of a SSE of a group is less than the predetermined value, the classification module 101 determines that a classification result of users in the group is appropriate.
The determination module 102 determines a current user and determines a group that includes the current user.
The transmission module 103 transmits target files for the current user. In at least one embodiment, the transmission module 103 determines the target files according to what is read by the other users in the determined group.
In other embodiments, the acquired reading information is updated after the predetermined period, and the groups can be updated according to the updated reading information of users.
Referring to
In block 301, an acquiring module acquires reading information of users within a predetermined period. In at least one embodiment, the reading information of the users includes keyword characteristic values of predetermined keywords corresponding to each of the users, and reading characteristic values which represents reading habits corresponding to each of the users.
The acquiring module acquires a title of each read file of the users within the predetermined period, and determines keywords from the title of each read file to be the predetermined keywords. The acquiring module calculates a frequency of each of the predetermined keywords in read files of the specified user. The calculated frequency of each of the predetermined keywords is determined to be a keyword characteristic value of each of the predetermined keywords corresponding to the specified user.
The reading characteristic values include an average daily reading duration, the time(s) of the day when reading is done, an average reading speed, an average number of reading files and a total reading duration of each of the users.
In block 302, according to the reading information, a classification module classifies the users into groups using a clustering method. In some embodiments, a user is classified to a single group, and in other embodiments, a user can be classified into more than one group. In at least one embodiment, the clustering method uses an expectation-maximization algorithm. The classification module 101 establishes a matrix according to the reading information of the users. The matrix is regarded as an input of the expectation-maximization algorithm.
In block 303, a determination module determines a current user and determines a group that includes the current user.
In block 304, a transmission module transmits target files for the current user. In at least one embodiment, the transmission module 103 determines the target files according to what read by the other users in the determined group.
In other embodiments, the acquired reading information is updated after the predetermined period, and the groups are updated according to the updated reading information of users.
It should be emphasized that the above-described embodiments of the present disclosure, including any particular embodiments, are merely possible examples of implementations, set forth for a clear understanding of the principles of the disclosure. Many variations and modifications can be made to the above-described embodiment(s) of the disclosure without departing substantially from the spirit and principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2013103601547 | Aug 2013 | CN | national |
