Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Xiangmin Zhou, Lei Chen, Chengkun He, Junfeng Wu, Weiyi Zhou, Jie Shao, Yanchun Zhang
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Universitas Brawijaya
Award: G3702571588
OpenAlex ID: W4410636941