# Blockchain-based Recommender Systems: Applications, Challenges and Future Opportunities

arXiv: https://arxiv.org/abs/2111.11509 · HTML: https://mlsanity.com/paper/2111.11509/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 184 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 18 of 18)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [Deep Reinforcement Learning for Page-wise Recommendations](https://mlsanity.com/paper/1805.02343/) (2018)
- [Poisoning Attacks to Graph-Based Recommender Systems](https://mlsanity.com/paper/1809.04127/) (2018)
- [Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time](https://mlsanity.com/paper/1711.07601/) (2017)
- [Metadata Embeddings for User and Item Cold-start Recommendations](https://mlsanity.com/paper/1507.08439/) (2015)
- [Decentralized & Collaborative AI on Blockchain](https://mlsanity.com/paper/1907.07247/) (2019)
- [A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects](https://mlsanity.com/paper/2102.07654/) (2021)
- [When Mobile Blockchain Meets Edge Computing](https://mlsanity.com/paper/1711.05938/) (2017)
- [Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems](https://mlsanity.com/paper/2006.07934/) (2020)
- [The emergence of Explainability of Intelligent Systems: Delivering Explainable and Personalised Recommendations for Energy Efficiency](https://mlsanity.com/paper/2010.04990/) (2020)
- [Blockchain as a Service: A Decentralized and Secure Computing Paradigm](https://mlsanity.com/paper/1807.02515/) (2018)
- [Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients](https://mlsanity.com/paper/2012.02044/) (2020)
- [A Deep, Forgetful Novelty-Seeking Movie Recommender Model](https://mlsanity.com/paper/1909.01811/) (2019)
- [Poster Abstract: Towards Scalable and Trustworthy Decentralized Collaborative Intrusion Detection System for IoT](https://mlsanity.com/paper/2002.07512/) (2020)
- [Feature-level Rating System using Customer Reviews and Review Votes](https://mlsanity.com/paper/2007.09513/) (2020)

## Built on by (most cited first, 11 of 11)

- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions](https://mlsanity.com/paper/2308.04522/) (2023)
- [Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review](https://mlsanity.com/paper/2307.01221/) (2023)
- [Deep Transfer Learning for Kidney Cancer Diagnosis](https://mlsanity.com/paper/2408.04318/) (2024)
- [Towards Robust Recommender Systems via Triple Cooperative Defense](https://mlsanity.com/paper/2210.13762/) (2022)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems](https://mlsanity.com/paper/2406.04702/) (2024)
- [Securing Recommender System via Cooperative Training](https://mlsanity.com/paper/2401.12700/) (2024)
- [Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation](https://mlsanity.com/paper/2407.18937/) (2024)
- [Benchmarking Deep Neural Networks for Modern Recommendation Systems](https://mlsanity.com/paper/2512.07000/) (2025)

## Abstract (arXiv)

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models' accuracy and ignore issues related to security and the users' privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users' private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research.