A Responsible and Extendable Context-Aware Recommender System

Context-aware social media recommendation has been important in many applications such as e-commerce and entertainment. However, existing systems consider pre-specified contexts and cannot well handle user preferences, which negatively affects the recommendation quality and efficiency, and causes them not extendable to various applications. In this demo, we design RECARS, the first responsible and extendable context-aware recommender system. RECARS is designed with novel techniques, including efficient data organization over MongoDB and Apache Flink, and effective responsible recommendation generation that supports the system interactions with users. It allows users to perform iteratively refining and explaining the results by active learning and large language model (LLM). We demonstrate the usage of RECARS via YouTube.