As online social networks become more widely used, there is a growing focus on the role of recommender algorithms within these platforms. It is important to assess the accuracy of these algorithms in providing suitable recommendations. Our research demonstrates that the More
As online social networks become more widely used, there is a growing focus on the role of recommender algorithms within these platforms. It is important to assess the accuracy of these algorithms in providing suitable recommendations. Our research demonstrates that the presence of individuals and acquaintances within social networks influences user behavior in ways that are largely psychological. Many user actions on a post are influenced by their respect or closeness to the post's owner. This article explores how the predictability of user behavior towards posts from friends and acquaintances highlights the impact of emotional connections stemming from stable social relationships on post acceptance. It also raises concerns about the potential for incorrect recommendations in algorithms based on collaborative filtering due to data bias caused by these factors.
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