mlsanityData to 7 Oct 2026

Cyberswarm: a novel swarm intelligence algorithm inspired by cyber community dynamics

arXiv 2512.14752 ↗First posted 14 Dec 2025cs.SI cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Recommendation systems face challenges in dynamically adapting to evolving user preferences and interactions within complex social networks. Traditional approaches often fail to account for the intricate interactions within cyber-social systems and lack the flexibility to generalize across diverse domains, highlighting the need for more adaptive and versatile solutions. In this work, we introduce a general-purpose swarm intelligence algorithm for recommendation systems, designed to adapt seamlessly to varying applications. It was inspired by social psychology principles. The framework models user preferences and community influences within a dynamic hypergraph structure. It leverages centrality-based feature extraction and Node2Vec embeddings. Preference evolution is guided by message-passing mechanisms and hierarchical graph modeling, enabling real-time adaptation to changing behaviors. Experimental evaluations demonstrated the algorithm's superior performance in various recommendation tasks, including social networks and content discovery. Key metrics such as Hit Rate (HR), Mean Reciprocal Rank (MRR), and Normalized Discounted Cumulative Gain (NDCG) consistently outperformed baseline methods across multiple datasets. The model's adaptability to dynamic environments allowed for contextually relevant and precise recommendations. The proposed algorithm represents an advancement in recommendation systems by bridging individual preferences and community influences. Its general-purpose design enables applications in diverse domains, including social graphs, personalized learning, and medical graphs. This work highlights the potential of integrating swarm intelligence with network dynamics to address complex optimization challenges in recommendation systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 79 references that are in the map. First posted on arXiv 14 Dec 2025.

Builds on

25 of 79 references.

References · 25

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2016Wide & Deep Learning for Recommender Systemscited by 745
  3. 2019Graph Neural Networks for Social Recommendationcited by 711
  4. 2019Neural Graph Collaborative Filteringcited by 660
  5. 2018Session-based Recommendation with Graph Neural Networkscited by 312
  6. 2018Variational Autoencoders for Collaborative Filteringcited by 308
  7. 2020Self-supervised Graph Learning for Recommendationcited by 305
  8. 2021Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendationcited by 173
  9. 2017Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learningcited by 161
  10. 2018A Simple Convolutional Generative Network for Next Item Recommendationcited by 120
  11. 2019Session-based Social Recommendation via Dynamic Graph Attention Networkscited by 76
  12. 2019RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedbackcited by 63
13 more
  1. 2022XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendationcited by 57
  2. 2020DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendationcited by 46
  3. 2020Graph Convolutional Networks for Graphs Containing Missing Featurescited by 46
  4. 2021ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregationcited by 39
  5. 2022Visual Attention Methods in Deep Learning: An In-Depth Surveycited by 37
  6. 2020A Re-visit of the Popularity Baseline in Recommender Systemscited by 18
  7. 2020Incomplete Graph Representation and Learning via Partial Graph Neural Networkscited by 16
  8. 2021SAFRAN: An interpretable, rule-based link prediction method outperforming embedding modelscited by 15
  9. 2019A Deep Learning System for Predicting Size and Fit in Fashion E-Commercecited by 10
  10. 2025A Comprehensive Survey on Spectral Clustering with Graph Structure Learningcited by 10
  11. 2019Generating Reliable Friends via Adversarial Training to Improve Social Recommendationcited by 8
  12. 2020Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposingcited by 5
  13. 2022Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendationcited by 4

Built on by

Not cited yet.