Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and Blockchain

Recommender systems (RS) play an integral role in many online platforms. Exponential growth and potential commercial interests are raising significant concerns around privacy, security, fairness, and overall responsibility. The existing literature around responsible recommendation services is diverse and multi-disciplinary. Most literature reviews cover a specific aspect or a single technology for responsible behavior, such as federated learning or blockchain. This study integrates relevant concepts across disciplines to provide a broader representation of the landscape. We review the latest advancements toward building privacy-preserved and responsible recommendation services for the e-commerce industry. The survey summarizes recent, high-impact works on diverse aspects and technologies that ensure responsible behavior in RS through an interconnected taxonomy. We contextualize potential privacy threats, practical significance, industrial expectations, and research remedies. From the technical viewpoint, we analyze conventional privacy defenses and provide an overview of emerging technologies including differential privacy, federated learning, and blockchain. The methods and concepts across technologies are linked based on their objectives, challenges, and future directions. In addition, we also develop an open-source repository that summarizes a wide range of evaluation benchmarks, codebases, and toolkits to aid the further research. The survey offers a holistic perspective on this rapidly evolving landscape by synthesizing insights from both recommender systems and responsible AI literature.