Situational Analytic Method for User Behavior Pattern in Multimedia
A primarily extended and enriched the situation analytics framework for the specific social domain, named and further proposed a novel algorithm for users’ intention serialization analysis based on classic Generalized Sequential Pattern (GSP). We leveraged the huge volume of user behaviors records to explore the frequent sequence mode that is necessary to predict user intention. Our experiment selected two general kinds of intentions: playing and sharing of multimedia, which are the most common in MSNs, based on the intention serialization algorithm under different minimum support. By using the users’ microscopic behaviors analysis on intentions, we found that the optimal behavior patterns of each user under the and a user’s behavior patterns are different due to his/her identity variations in a large volume of session’s data. Situational Analytic Method for User Behavior Pattern in Multimedia A mobile Internet and mobile terminals enable users to access to MSNs at anytime, anywhere, on behalf of any identity, including role and group. The interaction behaviors between users and MSNs are becoming more comprehensive and complicated.
Situational Analytic Method for User Behavior Pattern in Multimedia
Existing System
Advancements are usually made by either customizing existing solutions for the intended use case, or by combining multiple analysis algorithms. Customizing algorithms for specific use cases is mostly achieved by either using ad hoc heuristics to improve intermediate results, or by using machine learning to train a classifier to analyze a multimedia resource. This makes these high-quality multimedia analysis methods highly specialized, and only usable for the use case they were designed for.
DISADVANTAGE
Theory by combining the service environment with situation awareness to handle the dynamic update or development of service at run time.
 The service can meet the changing needs of users and provide users with personalized service.
In order to adapt to the dynamic service environment and make a timely respond to the feedback of service environment,
Social media services increasingly require situation awareness.
Proposed System
One is to enrich and extend the Situ theory outreaching for social domain, that is the social media ecosystem, through newly and comprehensively considering user’s changeable identity including role and group), and the other is to propose a novel algorithm for users’ behavior pattern analysis and mining.
ADVANTAGE
- In order to allow smart phone users to access the service easily and timely, Lee designed a recommendation mechanism to predict user’s intention and activate appropriate service.
- An event-condition behavior model and a rule induction algorithm was used to find out behavior patterns of smart phone users, and then, made use of their behavior pattern to predict and recommend the appropriate service for the users.
HARDWARE REQUIREMENTS
System                 : Intel3core
HardDisk             : 8GB
Monitor                : 14’ColorMonitor
Mouse                  :  Optical Mouse
SOFTWARE REQUIREMENTS
Operating system      : Windows7/8/10
Coding Language      :  ASP.Net with C# (Service Pack 1)
Data Base                    :  SQL Server 2014
Tools                            : Visual studio 2013
Modules
USER
- Login
- View profile
- Search Friend
- View friends and Send Friend Request
- Create Post
- View Post
- Friends Post and Share Images
ADMIN
- Add Behavioral Category
- View all friend request and response
- View added Post category
- View all Post
- View Positive Post
- View Negative Post
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