Open AccessAnalytic Method for Estimating the User Behavior Patterns in Multimedia Social Networks
Author(s) -
Kesavarapu Kowshik,
M Sandeep,
Sai Gottipati Mounika,
Avishek Adhikari
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.32.15732
Subject(s) - computer science , serialization , control (management) , multimedia , the internet , domain (mathematical analysis) , human–computer interaction , world wide web , artificial intelligence , mathematical analysis , mathematics , operating system
Now a days the multimedia social networks plays major role in our daily life. All the earlier MSNs are validated and developed very well. The past decade has witnessed the emergence and progress of multimedia social networks (MSNs), which have explosively and tremendously increased to penetrate every corner of our lives, leisure and work. As well as, the users are enabled by Mobile internet & terminals for accessing the MSNs where ever they are and when they want with the help of any identity. It may be a group or a role. So, it become very complicated & comprehensive to provide the behavior’s interaction between MSNs as well as in users. The implemented system having the advancements and developed framework of the analytics in a particular domain; which is called as SocialSitu, And We implemented an algorithm which is named as novel for the analysis of the serialized users intention according to the typical GSP which is the short form of Generalized Sequential Pattern. An enormous number of user’s behavior records were broken for exploring the usual sequence mode. It is mandatory for guessing the intention of the user. We considered the two types of intentions. Those are playing multimedia & sharing multimedia. These 2 are widely used in regular MSNs with the help of intention serialization algorithm in control of various min support threshold (Min_Support). With the help of microscopic behavior analysis of the users, we find out the each user behavior patterns which are in optimized manner in control of the Min_Support. Based on the different identities of the user, the behavior patterns of the users may be varied in session data which is very large.