AI based social media recommendations have great potential to improve the user experience. However, often these recommendations do not match the user interest and create an unpleasant experience for the users. Moreover, the recommendation system being a black box creates comprehensibility and transparency issues. This paper investigates social media recommendations from an end user perspective. For the investigation, we used the popular social media platform Facebook and recruited regular users to conduct a qualitative analysis. We asked participants about the social media content suggestions, their comprehensibility, and explainability. Our analysis shows users mostly require explanation whenever they encounter unfamiliar content and to ensure their online data security. Furthermore, the users require concise, non-technical explanations along with the facility of controlled information flow. In addition, we observed that explanations impact the users perception of transparency, trust, and understandability. Finally, we have outlined some design implications and presented a synthesized framework based on our data analysis.

Gone are the days when AI was just extravagant, and marketers used to collect basic demographic information from customers to create a content marketing strategy. In the past few years, there has been a massive change in the way that businesses approach and engage with their customers. Digital media, especially the role of AI, has spread so far and wide that it has practically taken over everyone's daily lives and has a lasting influence on everything we do. Thus, the traditional marketing methods that were effective a few years ago do not produce the same kind of results today. The potential of AI is so enormous that it has impacted every industry, including finance, research, customer support, content marketing, telecommunications, education, and more.

As we laid out in our blog post introducing DBXi, Dropbox is building features to help users stay focused on what matters. Searching through your content can be tedious, so we built content suggestions to make it easier to find the files you need, when you need them. We've built this feature using modern machine learning (ML) techniques, but the process to get here started with a simple question: how do people find their files? What kinds of behavior patterns are most common? Starting with this basic understanding of the kinds of files users access, we built a system using a set of simple heuristics--manually-defined rules that try to capture the behaviors we described above.

Like many users, you've tried to come up with relevant or witty tweets but failed. You've either been unable to grasp the concept and use cases of the service, or you've gotten bored after succumbing to an overwhelming feeling of knowing nothing you could possibly say could add anything to the conversation. Not everyone can be tweet machines. Before you write off Twitter as useless, perhaps give Post Intelligence a try. Launching today, Post Intelligence seeks to bolster your tweets by providing relevant content to help boost your Twitter confidence.