{ 1 shovon10, 2 shaon007, 3 samcit41, 4 hasank, 5 hasan, 6 mohiuddin}@iut-dhaka.ed Abstract-Social network sites (SNS's) have connected millions of users creating the social revolution. Users' social behavior influences them to connect with others with same mentality. Social networks are constituted because of its user or organizations common interest in some social emerging issues. The popular social networking sites are Facebook, Twitter, MySpace, Orkut, LinkedIn, Google plus etc. which are actually online social networking (OSN) sites. However, the large amount of online users and their diverse and dynamic interests possess great challenges to support recommendation of friends on SNS's for each of the users. In this paper, we proposed a novel friend recommendation framework (FRF) based on the behavior of users on particular SNS's. The proposed method is consisted of the following stages: measuring the frequency of the activities done by the users and updating the dataset according to the activities, applying FP-Growth algorithm to classify the user behavior with some criteria, then apply multilayer thresholding for friend recommendation. The proposed framework shows good accuracy for social graphs used as model dataset.
Motivation is a crucial part of animal and human mental development, fostering competence, autonomy, and open-ended development. Motivational constructs have proved to be an integral part of explaining human and animal behavior. Computer scientists have proposed various computational models of motivation for artificial agents, with the aim of building artificial agents capable of autonomous goal generation. Multi-agent systems and swarm intelligence are natural extensions to the individual agent setting. However, there are only a few works that focus on motivation theories in multi-agent or swarm settings. In this study, we review current computational models of motivation settings, mechanisms, functions and evaluation methods and discuss how we can produce systems with new kinds of functions not possible using individual agents. We describe in detail this open area of research and the major research challenges it holds.
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