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Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications
Review Article  ·  Published: 15 October 2024
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ICCK Transactions on Sensing, Communication, and Control
Volume 1, Issue 1, 2024: 30-51
Review Article Free to Read

Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications

by
Yiquan An Yiquan An 1 ORCID
Yingxin Tan Yingxin Tan 2 * ORCID
Xi Sun Xi Sun 1 ORCID
Giovannipaolo Ferrari Giovannipaolo Ferrari 3 ORCID
1 Collaborative Innovation Center for Common Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
2 Department of Philosophy, Sociology, Education, and Applied Psychology, University of Padua, Padova 35139, Italy
3 Department of Human, Philosophic and Education Sciences, University of Salerno, Fisciano 84084, Italy
* Corresponding Author: Yingxin Tan, [email protected]
Volume 1, Issue 1
Volume 1, Issue 1 2024
Received: 03 September 2024 Revised: 28 September 2024 Accepted: 03 October 2024 Published: 15 October 2024 CrossMark
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DOI: 10.62762/TSCC.2024.898503
ARK: ark:/57805/tscc.2024.898503
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Article Information

Published in ICCK Transactions on Sensing, Communication, and Control
Volume/Issue Volume 1, Issue 1, 2024
Pages 30-51
Cited by 9 (Crossref) 7 (Scopus)

Abstract

The proliferation of Recommender Systems (RecSys), driven by their expanding deployment within Sensing-Communication-Control (SCC) architectures and explosive growth of multi-sensor data streams, has cultivated a dynamic research landscape at the intersection of artificial intelligence and cyber-physical systems. Embedded in SCC pipelines, RecSys transforms heterogeneous sensor-acquired data-spanning behavioral signals, physiological measurements, spatial context, and environmental conditions-into personalized control directives for smart homes, industrial IoT platforms, intelligent transportation systems, and precision healthcare environments. This paper comprehensively reviews RecSys foundational concepts, methodologies, and challenges from algorithmic and SCC system-integration perspectives. It categorizes RecSys solutions into five paradigms-collaborative filtering, scenario-aware, knowledge & data co-driven, large language models, and hybrid approaches-analyzing how each interfaces with sensing modalities, communication constraints, and control objectives. Five technical challenges critical to SCC deployment are then examined: robustness under sensing noise, recommendation accuracy under real-time constraints, cold-start problems in sensor-sparse environments, explainability for human-in-the-loop control, and privacy-preserving sensing and communication. The review further addresses how behavioral sensing data encodes biases that propagate into control outputs, and how human factors principles inform the design of transparent, trustworthy recommendation-driven control systems. Future directions include edge-native inference, sensor-adaptive learning, and communication-aware algorithm co-design.

Graphical Abstract

Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications

Keywords

recommender system sensing-communication-control cyber-physical systems IoT-driven recommendation human-in-the-loop control privacy-preserving communication edge-native inference

Data Availability Statement

Not applicable.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

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An, Y., Tan, Y., Sun, X., & Ferrari, G. (2024). Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications. ICCK Transactions on Sensing, Communication, and Control, 1(1), 30-51. https://doi.org/10.62762/TSCC.2024.898503
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TY  - JOUR
AU  - An, Yiquan
AU  - Tan, Yingxin
AU  - Sun, Xi
AU  - Ferrari, Giovannipaolo
PY  - 2024
DA  - 2024/10/15
TI  - Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 1
IS  - 1
SP  - 30
EP  - 51
DO  - 10.62762/TSCC.2024.898503
UR  - https://www.icck.org/article/abs/TSCC.2024.898503
KW  - recommender system
KW  - sensing-communication-control
KW  - cyber-physical  systems
KW  - IoT-driven recommendation
KW  - human-in-the-loop control
KW  - privacy-preserving communication
KW  - edge-native inference
AB  - The proliferation of Recommender Systems (RecSys), driven by their expanding deployment within Sensing-Communication-Control (SCC) architectures and explosive growth of multi-sensor data streams, has cultivated a dynamic research landscape at the intersection of artificial intelligence and cyber-physical systems. Embedded in SCC pipelines, RecSys transforms heterogeneous sensor-acquired data-spanning behavioral signals, physiological measurements, spatial context, and environmental conditions-into personalized control directives for smart homes, industrial IoT platforms, intelligent transportation systems, and precision healthcare environments. This paper comprehensively reviews RecSys foundational concepts, methodologies, and challenges from algorithmic and SCC system-integration perspectives. It categorizes RecSys solutions into five paradigms-collaborative filtering, scenario-aware, knowledge & data co-driven, large language models, and hybrid approaches-analyzing how each interfaces with sensing modalities, communication constraints, and control objectives. Five technical challenges critical to SCC deployment are then examined: robustness under sensing noise, recommendation accuracy under real-time constraints, cold-start problems in sensor-sparse environments, explainability for human-in-the-loop control, and privacy-preserving sensing and communication. The review further addresses how behavioral sensing data encodes biases that propagate into control outputs, and how human factors principles inform the design of transparent, trustworthy recommendation-driven control systems. Future directions include edge-native inference, sensor-adaptive learning, and communication-aware algorithm co-design.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{An2024Recommende,
  author = {Yiquan An and Yingxin Tan and Xi Sun and Giovannipaolo Ferrari},
  title = {Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2024},
  volume = {1},
  number = {1},
  pages = {30-51},
  doi = {10.62762/TSCC.2024.898503},
  url = {https://www.icck.org/article/abs/TSCC.2024.898503},
  abstract = {The proliferation of Recommender Systems (RecSys), driven by their expanding deployment within Sensing-Communication-Control (SCC) architectures and explosive growth of multi-sensor data streams, has cultivated a dynamic research landscape at the intersection of artificial intelligence and cyber-physical systems. Embedded in SCC pipelines, RecSys transforms heterogeneous sensor-acquired data-spanning behavioral signals, physiological measurements, spatial context, and environmental conditions-into personalized control directives for smart homes, industrial IoT platforms, intelligent transportation systems, and precision healthcare environments. This paper comprehensively reviews RecSys foundational concepts, methodologies, and challenges from algorithmic and SCC system-integration perspectives. It categorizes RecSys solutions into five paradigms-collaborative filtering, scenario-aware, knowledge \& data co-driven, large language models, and hybrid approaches-analyzing how each interfaces with sensing modalities, communication constraints, and control objectives. Five technical challenges critical to SCC deployment are then examined: robustness under sensing noise, recommendation accuracy under real-time constraints, cold-start problems in sensor-sparse environments, explainability for human-in-the-loop control, and privacy-preserving sensing and communication. The review further addresses how behavioral sensing data encodes biases that propagate into control outputs, and how human factors principles inform the design of transparent, trustworthy recommendation-driven control systems. Future directions include edge-native inference, sensor-adaptive learning, and communication-aware algorithm co-design.},
  keywords = {recommender system, sensing-communication-control, cyber-physical  systems, IoT-driven recommendation, human-in-the-loop control, privacy-preserving communication, edge-native inference},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}

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