Embodiments of the present invention provide an approach for goal-based user matching among social networking environments. In a typical embodiment, a querying user is asked to respond to a series of questions related to goals, criteria, and criteria priorities (and, optionally, a desired “degree” of separation among other users). The querying user will submit his/her responses, which are then parsed and prioritized accordingly. Based on the responses, social networking sites are then searched to identify a set (e.g., at least one) of candidate users that are potential collaboration partners with the querying user. This set of candidate users may be arranged in a list and sorted according to a best match to the querying user's responses. The list can then be provided to the querying user. In addition, invites can be sent to the set of candidate users to invite such users to connect/collaborate with the querying user.

Goal-oriented user matching among social networking environments

The invention provides an individualized learning recommendation method and device which solve the problems that an existing individualized learning scheme takes little attention to a learning environment of a user, and accordingly the learning efficiency and learning effect are affected. The method comprises the steps that environmental information of the environment in which a target user is located and user learning information of the target user are obtained; according to the environmental information and the user learning information, a to-be-recommended sample model is determined, wherein the to-be-recommended sample model comprises a recommended environment sample and a recommended user sample, the similarity between the recommended environment sample and the target environment is larger than a first preset threshold value, and the similarity between the recommended user sample and the target user is larger than a second preset threshold value; according to the recommended sample model, recommended learning information corresponding to the recommended sample model is determined and recommended to the target user. By means of the individualized learning recommendation method and device, a more real learning scene can be provided, and accordingly the learning effect and learning efficiency of a learner are improved.

Individualized learning recommendation method and device

The invention relates to a method and equipment recommending friends to a social network user based on location, in particular to the method recommending the friends based on the location. The method recommending the friends based on the location comprises the following steps: obtaining current location information of the user; determining the last location from the current location of the user and candidate friends in a certain distance range according to the current location information, wherein the candidate friends are not direct friends of the user but members of the social network; calculating a first weighted value D according to the distance from the last location of the candidate friends to the current location of the user and the obtaining time of the last location of the candidate friends; obtaining a second weighted value G according to the relationship between the candidate friends and the user; and sequencing the cadidating friends according to the first weighted value D and the second weighted G.

Method and equipment recommending friends to social network user based on location

The invention relates to a method and system for recommending users in an SNS community. The method comprises the steps that a client-side acquires update information of users and uploads the update information to a server in real time; the server calculates in an on-line mode to obtain the information of to-be-recommended users according to the update information; the client-side obtains the information of the to-be-recommended users from the server and displays the information of the to-be-recommended users to users. According to the method and system for recommending users in the SNS community, after the update information of users is obtained, the information of the to-be-recommended users is instantly obtained through calculation and is displayed to the users, so that only users relevant to the update information need calculating, and not all users need calculating for one time in an off-line mode. Consequently, time is saved, and the calculation burden of the server is reduced.

Method and system for recommending users in SNS community

The invention discloses a group reading method based on an electronic book, electronic equipment and a computer storage medium. The method comprises the steps that a reading group is created accordingto the reading attribute information of a reading user, wherein the reading attribute information, included in the reading group, of group users is similar; according to the reading attribute information, included in the reading group, of each group user, a group reading task corresponding to the reading group is configured; when a task feedback message generated by each group user included in the reading group according to the group reading task is received, the task feedback information is displayed in the reading group. Therefore, by means the mode, reading users with similar reading attribute information are added into the same reading group as the group users so that the group users can achieve mutual understanding of reading conditions, discuss with one another and share resources in the process of completing the group reading task, and the interactivity of the group users to complete the group reading task is improved.

Group reading method based on an electronic book, electronic equipment and computer storage medium

The invention discloses a method and a system for automatically recommending a learning partner in a distance education social networks service (ESNS). The method comprises the following steps of: 1, automatically receiving learning partner recommendation application of a user side; 2, responding to the learning partner recommendation application, automatically acquiring user information, and dividing the user information into user individual information, academic information and learning partner information; 3, acquiring the tightness of a candidate learning partner and the user according to the user individual information, the academic information and the learning partner information; and 4, acquiring the candidate learning partner according to the tightness of the candidate learning partner and the user and automatically recommending and displaying the candidate learning partner to the user. By the method and the system, the learning partner in the ESNS can be automatically recommended.

Method and system for automatically recommending learning partner in distance education social network service (ESNS)

Web-based collaborative learning (WBCL) system and method

Data service traffic model is playing more and more important role in many fields of 3G (Third Generation) mobile networks such as traffic forecasting, resource allocation, network optimization and so on QQ as one of the most popular Internet service in China also developed very rapidly in mobile networks By analyzing the communication mechanism and format of QQ packet, a novel QQ message model is put forward and the characteristics of the parameters and distributions are given based on the real QQ data collected on the UMTS networks Extensive researches of the network traffic show the packet data traffic has the characteristics of self-similarity and long range dependence The self-similarity of QQ traffic is investigated and the Hurst parameters are estimated with different methods

Analysis of traffic model and self-similarity for QQ in 3G mobile networks

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Papers

Method and system for automatically recommending learning partner in distance education social network service (ESNS)

Web-based collaborative learning (WBCL) system and method

Analysis of traffic model and self-similarity for QQ in 3G mobile networks