It is now easier and cheaper for people to get involved in technology, and most people are obtaining or receiving tons of information nowadays, such as recommendations. Different recommendations are filled with our devices like smartphones, tablets or even wearable devices. However, most searches and recommendation systems fail to take into consideration the emotional context related to a user’s input and action, and as a consequence, people can hardly receive personalized, emotion-related, user-based recommendations. Though people are given various choices about lives based on searches and saved data, those recommendations can hardly improve the stress and difficulties of accomplishing daily life goals caused by the developing of technology. Therefore, People need more user-centered recommendations that incorporate emotion and action related evaluations to reach more precise results.
This thesis project presents a possible design solution to improve this situation. An affective recommendation system designed to determine a person’s emotional state or condition based on the affective interpretations of their social media content. Combining bio information, exercise or activity records, it provides personalized recommendations like food, entertainment, activities or exercise suggestions related to the users.
This project demonstrates how a new user-centered, emotion and activity based recommendation system can leverage elements of emerging technologies such as conversational User Interfaces (CUI), context recognition, and expression recognition to create a more user-friendly and more meaningful experience
