mlsanityData to 7 Oct 2026

Data Scarcity in Recommendation Systems: A Survey

arXiv 2312.10073 ↗First posted 8 Dec 2023cs.IR cs.AIcited by 17

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Abstract (arXiv)

The prevalence of online content has led to the widespread adoption of recommendation systems (RSs), which serve diverse purposes such as news, advertisements, and e-commerce recommendations. Despite their significance, data scarcity issues have significantly impaired the effectiveness of existing RS models and hindered their progress. To address this challenge, the concept of knowledge transfer, particularly from external sources like pre-trained language models, emerges as a potential solution to alleviate data scarcity and enhance RS development. However, the practice of knowledge transfer in RSs is intricate. Transferring knowledge between domains introduces data disparities, and the application of knowledge transfer in complex RS scenarios can yield negative consequences if not carefully designed. Therefore, this article contributes to this discourse by addressing the implications of data scarcity on RSs and introducing various strategies, such as data augmentation, self-supervised learning, transfer learning, broad learning, and knowledge graph utilization, to mitigate this challenge. Furthermore, it delves into the challenges and future direction within the RS domain, offering insights that are poised to facilitate the development and implementation of robust RSs, particularly when confronted with data scarcity. We aim to provide valuable guidance and inspiration for researchers and practitioners, ultimately driving advancements in the field of RS.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 49 of its 146 references that are in the map. First posted on arXiv 8 Dec 2023.

Builds on

49 of 146 references.

References · 49

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37 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 9; 2026: 5.

17 papers.

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  1. 2025Mixture of Experts (MoE): A Big Data Perspectivecited by 17
  2. 2024Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?cited by 13
  3. 2025Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systemscited by 4
  4. 2025Diffusion Models in Recommendation Systems: A Surveycited by 3
  5. 2025KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusioncited by 2
  6. 2025Graph Contrastive Learning on Multi-label Classification for Recommendationscited by 2
  7. 2025Tree of Preferences for Diversified Recommendationcited by 2
  8. 2024Artificial Intelligence in Landscape Architecture: A Surveycited by 1
  9. 2025Flow-Modulated Scoring for Semantic-Aware Knowledge Graph Completioncited by 1
  10. 2026Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustnesscited by 1
  11. 2024Factory Operators' Perspectives on Cognitive Assistants for Knowledge Sharing: Challenges, Risks, and Impact on Workcited by 0
  12. 2025Fractal Graph Contrastive Learningcited by 0
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  1. 2025Beyond Prefixes: Graph-as-Memory Cross-Attention for Knowledge Graph Completion with Large Language Modelscited by 0
  2. 2026AlphaFree: Recommendation Free from Users, IDs, and GNNscited by 0
  3. 2026Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendationscited by 0
  4. 2026Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measurecited by 0
  5. 2026VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agentscited by 0