امیدرضا بلوکی اسپیلی

Social media has made major changes in various e-commerce areas. One of these marketing cases is in e-commerce systems. The relationship between customers and business is very much appreciated by marketers. The use of social media by customers has given marketers the opportunity to get more information from customer feedback. Recently, in social media, marketers look for customers who have the most impact on other customers. They can influence the ideas of other customers with their opinions about a new product. In addition, influential users can have the greatest impact on specific domains. This domain may be in the domain of a product or service. Therefore, in this article influential users on social media have been studied in terms of impact in different areas. The proposed approach is for influential users using the social knowledge management approach. The knowledge cycle consists of knowledge organization, storage, retrieval, and knowledge discovery and knowledge management, where all explicit and implicit knowledge has been tried to accurately disclose affected users. In this paper, firstly, the problem was adapted to the knowledge management cycle, and in the steps of this cycle, artificial intelligence techniques such as Baysian networks were used to classify and identify influential users .In order to investigate the proposed method, various scenarios based on a variety of data sets are used for evaluation and the results of these studies show the high accuracy of the proposed method in identifying influential users.

Today's online communities, as a multifaceted platform, have many applications in e-commerce, marketing and e-learning. Online blogging services are one of the most popular environment for user interactions. Users share their ideas, opinions, and information in this environment. The spread of information between users plays an essential role in the success of such online communities. However, these communities face challenges in post management and information spread. Modeling the life cycle of a post provides an opportunity to examine how information is disseminated among users. In these communities, each post after creation is reposted and transmitted by users. Depending on their content and online community structure, posts are spread in different ways in the network. Some posts are rapidly becoming epidemic and some are not welcomed by users. In this article, we are looking for a method that estimates the probability of an epidemic of a post. For this purpose, a learning method based on learning automata has been used. The evaluations show that this method is efficient in three evaluation datasets. Furthermore, we will introduce self-organized posts that facilitate the management of posts in online communities.